Investigating slope deformation in densely vegetated or remote areas is a major challenge for slope stability assessment. This study introduces and validates an integrated UAV-borne low-frequency Ground Penetrating Radar (UAV-GPR) and LiDAR methodology to characterize an unstable slope in Melizzano, Southern Italy. Radar data were acquired along an east-west transect at similar to 1 m above ground level, while high-resolution LiDAR were used to generate a detailed Digital Terrain Model for topographic correction and geomorphological analysis. The processed radargram images subsurface features down to similar to 15 m, revealing a laterally continuous high-amplitude reflector at similar to 10 m, interpreted as a key main sliding surface. Chaotic reflections above this interface indicate heterogeneous deposits associated with gravitational deformation, while more homogeneous reflections below correspond to stable geological units. The geometry of the reflector suggests a compound landslide mechanism. Borehole data validate the geophysical interpretation, showing depth discrepancies lower than 2 m. The integration of UAV-GPR and LiDAR enables a reliable correlation between surface morphology and subsurface structures. This non-invasive, spatially continuous approach provides an effective framework for subsurface characterization and for improving the interpretation of landslide geometry and internal structure in challenging environments. This study demonstrates the capability of low-frequency UAV-borne GPR to detect deep-seated sliding surfaces (>10 m) in vegetated environments when integrated with high-resolution LiDAR topography.
Highlights What are the main findings? A novel cross-layer optimization scheme for BBR, named SDN-BBR, is proposed. It integrates a lightweight In-Band Network Telemetry (INT) mechanism for real-time route-switching detection. It establishes a QoS inequality model to derive critical bandwidth and reconstructs the BBR state machine for cross-layer coordination. This scheme effectively addresses the performance degradation of BBR in highly dynamic routing environments of Software-Defined Unmanned Aerial Vehicle Ad Hoc Networks (SD-UAVANETs). What are the implications of the main findings? Simulation results demonstrate that SDN-BBR significantly enhances transmission performance. Specifically, it achieves a 69.8% reduction in convergence time and a 73.9% increase in throughput during bandwidth upgrade scenarios, and an 86.8% reduction in packet loss rate and an 8.3% reduction in delay during bandwidth downgrade scenarios. In multi-service flow concurrent scenarios, it achieves overall improvements of 12.3% in throughput, 21% in packet loss rate, and 7.9% in delay. The scheme provides an effective solution for reliable and high-performance transmission in dynamic UAV networks.Highlights What are the main findings? A novel cross-layer optimization scheme for BBR, named SDN-BBR, is proposed. It integrates a lightweight In-Band Network Telemetry (INT) mechanism for real-time route-switching detection. It establishes a QoS inequality model to derive critical bandwidth and reconstructs the BBR state machine for cross-layer coordination. This scheme effectively addresses the performance degradation of BBR in highly dynamic routing environments of Software-Defined Unmanned Aerial Vehicle Ad Hoc Networks (SD-UAVANETs). What are the implications of the main findings? Simulation results demonstrate that SDN-BBR significantly enhances transmission performance. Specifically, it achieves a 69.8% reduction in convergence time and a 73.9% increase in throughput during bandwidth upgrade scenarios, and an 86.8% reduction in packet loss rate and an 8.3% reduction in delay during bandwidth downgrade scenarios. In multi-service flow concurrent scenarios, it achieves overall improvements of 12.3% in throughput, 21% in packet loss rate, and 7.9% in delay. The scheme provides an effective solution for reliable and high-performance transmission in dynamic UAV networks.Abstract Unmanned Aerial Vehicle Ad Hoc Networks (UAVANETs) are characterized by highly dynamic topology changes and unstable link conditions, which necessitate deep collaboration between transport-layer congestion control and network-layer routing decisions to ensure service quality. However, the existing layered architecture of Software-Defined Networking (SDN) results in a significant separation between routing information and congestion control mechanisms, rendering traditional protocols ineffective in handling severe performance fluctuations caused by highly dynamic route switching. The significant disconnect between network-layer route planning and transport-layer congestion control strategies in Software-Defined Unmanned Aerial Vehicle Ad Hoc Networks (SD-UAVANETs) leads to degraded transmission performance of BBR (Bottleneck Bandwidth and Round-trip propagation time) under high-dynamic route switching scenarios. As such, this paper proposes an in-band network telemetry (INT)-based cross-layer optimization scheme for BBR, named SDN-BBR. Firstly, a lightweight real-time route switching detection mechanism based on INT is designed. Secondly, a QoS inequality model before and after path switching is established, deriving the critical bandwidth of the new path and integrating it into the BBR algorithm to accelerate convergence and avoid congestion. Finally, the BBR state machine is redesigned to achieve cross-layer information fusion and coordinated control, thereby optimizing transmission performance. Experimental results show that the proposed scheme reduces convergence time by 69.8% and increases throughput by 73.9% in low-bandwidth to high-bandwidth switching scenarios; decreases packet loss rate by 86.8% and reduces delay by 8.3% in high-bandwidth to low-bandwidth switching scenarios; and improves throughput by 12.3%, lowers packet loss rate by 21%, and reduces delay by 7.9% in multi-traffic flow concurrent scenarios. The scheme significantly enhances the transmission performance of BBR in highly dynamic routing environments of SD-UAVANET.
Highlights What are the main findings? The ED-SAC algorithm achieves superior trajectory tracking, reducing the average tracking error to 0.27 m-a 40% improvement over the standard SAC. The algorithm demonstrates robust performance in simulated dynamic grassland highway environments, maintaining a 96.2% mission success rate under continuous random disturbances. What are the implications of the main findings? The proposed ensemble Q-network and delayed policy update mechanisms effectively mitigate value overestimation and training instability in continuous control tasks. This approach provides a reliable UAV proactive patrol solution for grassland highways, mitigating traffic accident risks caused by livestock crossings and enhancing overall road safety.Highlights What are the main findings? The ED-SAC algorithm achieves superior trajectory tracking, reducing the average tracking error to 0.27 m-a 40% improvement over the standard SAC. The algorithm demonstrates robust performance in simulated dynamic grassland highway environments, maintaining a 96.2% mission success rate under continuous random disturbances. What are the implications of the main findings? The proposed ensemble Q-network and delayed policy update mechanisms effectively mitigate value overestimation and training instability in continuous control tasks. This approach provides a reliable UAV proactive patrol solution for grassland highways, mitigating traffic accident risks caused by livestock crossings and enhancing overall road safety.Abstract To address the issue of traffic accidents caused by livestock crossing roads on grassland highways, this paper proposes an adaptive cruise control method for unmanned aerial vehicles (UAVs) based on an ensemble Q-network and a Soft Actor-Critic (SAC) with delayed policy updates, namely the ED-SAC algorithm. Building upon the standard SAC framework, this method introduces multiple independent Critic networks to form an ensemble Q-network, and employs a random subset minimization strategy during the calculation of target Q-values to mitigate policy bias resulting from overestimated values; simultaneously, a delayed policy update mechanism decouples the optimization processes of the Actor and Critic networks, thereby enhancing training stability and control robustness. Using the PyBullet simulation platform, this paper constructs a UAV inspection scenario on grassland roads and designs three typical test tasks: infinite loop, grid scan and spiral trajectories, to conduct comparative validation of the PPO, TD3, SAC and ED-SAC algorithms. Experimental results demonstrate that, under disturbance-free conditions, ED-SAC achieves the highest mission success rate and the lowest tracking error across all three trajectory scenarios, with an average tracking error as low as 0.27 m and a mission success rate as high as 98.7%. Under continuous random external disturbances, ED-SAC still maintains high trajectory tracking accuracy and attitude control stability, with a mission success rate reaching up to 96.2%. The results demonstrate that the proposed ED-SAC algorithm can effectively enhance the trajectory tracking accuracy, training stability and anti-disturbance capability of UAVs in complex grassland road inspection scenarios, providing a reliable intelligent control method for active grassland road inspection and traffic safety early warning.
Highlights What are the main findings? We propose SA-DSM-MADDPG for 3v1 multi-UAV cooperative encirclement, integrating a self-attention critic, double-screened experience replay (PER + relevance screening), and curriculum learning. Experimental results show that SA-DSM-MADDPG achieves a higher cooperative capture success rate and more stable convergence than the MADDPG baseline in obstacle-rich environments. What are the implications of the main finding? The improved success rate indicates that combining interaction-aware coordination modeling (attention), phase-relevant sample selection (DSM), and staged reward shaping (curriculum) effectively mitigates sparse-feedback learning issues in pursuit-evasion tasks. The proposed design provides a practical guideline for developing more reliable multi-drone interception/containment decision policies in cluttered environments under the CTDE paradigm.Highlights What are the main findings? We propose SA-DSM-MADDPG for 3v1 multi-UAV cooperative encirclement, integrating a self-attention critic, double-screened experience replay (PER + relevance screening), and curriculum learning. Experimental results show that SA-DSM-MADDPG achieves a higher cooperative capture success rate and more stable convergence than the MADDPG baseline in obstacle-rich environments. What are the implications of the main finding? The improved success rate indicates that combining interaction-aware coordination modeling (attention), phase-relevant sample selection (DSM), and staged reward shaping (curriculum) effectively mitigates sparse-feedback learning issues in pursuit-evasion tasks. The proposed design provides a practical guideline for developing more reliable multi-drone interception/containment decision policies in cluttered environments under the CTDE paradigm.Abstract Multi-UAV cooperative encirclement in pursuit-evasion scenarios requires effective coordination under dynamic inter-agent interactions, sparse task feedback, and obstacle-constrained motion. While MADDPG offers a practical CTDE framework for multi-agent continuous control, its direct application to cooperative encirclement still faces challenges in modeling time-varying teammate dependencies, selecting informative replay samples, and maintaining stable learning under delayed rewards. To address these challenges, we propose SA-DSM-MADDPG, an enhanced multi-agent deep deterministic policy gradient method that integrates the following: (i) a self-attention critic to model dynamic inter-agent relevance, (ii) a double-screened experience replay strategy combining prioritized sampling and relevance screening to improve replay quality, and (iii) curriculum learning with staged reward shaping to provide denser and more stable training signals. We evaluate the proposed method in 3v1 cooperative encirclement environments with static obstacles and varying initial conditions. Experimental results show that SA-DSM-MADDPG improves the success rate by approximately 22 percentage points over MADDPG and 35 percentage points over MAPPO, while also exhibiting faster convergence and better training stability.
Highlights What are the main findings? This study proposes AAR-TW, an online UAV-UGV coordination framework that combines adaptive anticipatory rendezvous with charging-time-window scheduling in environments with obstacles. It achieves clear performance gains over baseline methods, reducing completion time by 14-19%, emergency landings by 90-97%, and system energy consumption by 14-25% in representative test settings. What are the implications of the main findings? This paper provides an effective solution for energy-constrained online UAV-UGV cooperation, improving mission continuity and reducing safety-critical interruptions in environments with obstacles. The closed-loop prototype validation confirms the engineering feasibility of the proposed framework and shows its potential for practical deployment in real UAV-UGV systems.Highlights What are the main findings? This study proposes AAR-TW, an online UAV-UGV coordination framework that combines adaptive anticipatory rendezvous with charging-time-window scheduling in environments with obstacles. It achieves clear performance gains over baseline methods, reducing completion time by 14-19%, emergency landings by 90-97%, and system energy consumption by 14-25% in representative test settings. What are the implications of the main findings? This paper provides an effective solution for energy-constrained online UAV-UGV cooperation, improving mission continuity and reducing safety-critical interruptions in environments with obstacles. The closed-loop prototype validation confirms the engineering feasibility of the proposed framework and shows its potential for practical deployment in real UAV-UGV systems.Abstract In persistent patrol and online task discovery in environments with obstacles, unmanned aerial vehicle (UAV) swarms are constrained by limited battery capacity and frequent recharging disrupts patrol continuity. In comparison, unmanned ground vehicle (UGV) fleets have higher endurance and payload capacity and can serve as mobile charging platforms while executing ground-service tasks. In such collaborative scenarios, UAVs patrol along a coverage path and discover tasks online, whereas UGVs execute discovered ground tasks and provide mobile charging support. To cope with rendezvous uncertainty due to obstacle-induced detours and inefficient usage of UGV time during charging, this study proposes an energy-constrained UAV-UGV coordination framework based on adaptive anticipatory rendezvous and time-window scheduling. In particular, the adaptive anticipatory rendezvous module handles anticipatory rendezvous planning, while the time-window scheduling module models the post-rendezvous charging stage as a schedulable time window for opportunistic ground-task insertion. Simulations demonstrate that the proposed framework consistently reduces system energy consumption, completion time, and the number of emergency landings compared with three representative baselines. Moreover, a UAV-UGV prototype with AprilTag-based visual landing and post-landing mechanical correction is developed to validate the engineering feasibility of the key closed-loop process.
Highlights What are the main findings? We model UAV path deviation using a fuzzy rule base for wind interference and construct a UAV stereoscopic path employing precision control algorithms that transition from global to local. We present research on UAV and WSN spatiotemporal coordination for early warning of high-risk fire zones and dynamic adjustment of monitoring priorities. What are the implications of the main findings? Reduction of path errors of UAVs in urban building clusters, enhancing trajectory accuracy, and shortening UAV inspection time; Enhancing the response efficiency of the UAV-WSN fire monitoring system while reducing system energy consumption costs.Highlights What are the main findings? We model UAV path deviation using a fuzzy rule base for wind interference and construct a UAV stereoscopic path employing precision control algorithms that transition from global to local. We present research on UAV and WSN spatiotemporal coordination for early warning of high-risk fire zones and dynamic adjustment of monitoring priorities. What are the implications of the main findings? Reduction of path errors of UAVs in urban building clusters, enhancing trajectory accuracy, and shortening UAV inspection time; Enhancing the response efficiency of the UAV-WSN fire monitoring system while reducing system energy consumption costs.Abstract To address challenges such as the sudden onset of urban fires, data synchronization delays in early warning systems, response lags, and insufficient routine monitoring, this paper proposes a Spatio-Temporal Collaborative Optimization for Joint Control and Scheduling (STCO-JCS) algorithm tailored for unmanned aerial vehicles (UAVs) and wireless sensor networks (WSNs). First, spatial autocorrelation analysis based on fire data classifies areas into ultra-high, high, medium, and low risk zones to assist in determining UAV access priorities. Second, we construct optimal inspection trajectories for the UAV by taking into account the inspection sequence and the city's topography. By modeling the path deviations caused by wind interference and designing precision control algorithms, we improve the accuracy of the UAV's flight path, ultimately achieving the goal of reducing UAV inspection time. Finally, by coordinating the spatiotemporal operations of drones and wireless sensor networks, we can achieve early detection and rapid response in high-risk fire zones, thereby reducing drone energy consumption while enhancing the efficiency of the UAV-WSN fire monitoring system. Simulation results demonstrate that under a 20-square-kilometer simulation area, STCO-JCS controls inspection paths within 14-17 km. In the multi-UAV scenario, the proposed method achieves approximately 3.17-9.66% improvement in energy efficiency, while in the single-UAV scenario, improvements of 10.83%, 50.54%, and 9.26% are observed in metrics. This provides effective decision support for the dynamic deployment of firefighting and rescue resources.
Highlights What are the main findings? A novel Keypoint-Sparse Cache (KSC) strategy is proposed to significantly reduce the search complexity of 3D path planning through sparse keypoint extraction and path caching reuse. A hierarchical KSC-PPO framework is further developed, which integrates PPO-based local dynamic obstacle avoidance with global KSC path planning. This approach achieves a favorable balance between high-quality global path planning and the flexibility of rapid local obstacle avoidance in three-dimensional complex terrain environments. What are the implications of the main findings? In static-environment benchmarks, KSC achieves substantially lower computation time than RRT* and Informed RRT* while maintaining competitive path quality, and it also outperforms four bio-inspired optimization algorithms across terrains of increasing complexity. The KSC-PPO architecture mitigates the inefficiency of current dynamic obstacle-avoidance methods, which typically require full-path re-optimization once obstacles are detected. By activating PPO-based local avoidance only when safety thresholds are reached and replanning the remaining path after threat clearance, the framework limits avoidance computation to a local region while maintaining high-quality global path generation. This offers an efficient and robust solution for UAV path planning in mountainous and dynamically changing environments.Highlights What are the main findings? A novel Keypoint-Sparse Cache (KSC) strategy is proposed to significantly reduce the search complexity of 3D path planning through sparse keypoint extraction and path caching reuse. A hierarchical KSC-PPO framework is further developed, which integrates PPO-based local dynamic obstacle avoidance with global KSC path planning. This approach achieves a favorable balance between high-quality global path planning and the flexibility of rapid local obstacle avoidance in three-dimensional complex terrain environments. What are the implications of the main findings? In static-environment benchmarks, KSC achieves substantially lower computation time than RRT* and Informed RRT* while maintaining competitive path quality, and it also outperforms four bio-inspired optimization algorithms across terrains of increasing complexity. The KSC-PPO architecture mitigates the inefficiency of current dynamic obstacle-avoidance methods, which typically require full-path re-optimization once obstacles are detected. By activating PPO-based local avoidance only when safety thresholds are reached and replanning the remaining path after threat clearance, the framework limits avoidance computation to a local region while maintaining high-quality global path generation. This offers an efficient and robust solution for UAV path planning in mountainous and dynamically changing environments.Abstract UAV path planning in complex 3D terrain faces the dual challenges of computational efficiency and reliable obstacle avoidance. To address these issues, this paper proposes a Keypoint-Sparse Cache (KSC) strategy and a hierarchical KSC-PPO (Proximal Policy Optimization) framework for mountainous environments with both static terrain and dynamic obstacles. The KSC strategy reduces search complexity through orthogonal slice-based sparse keypoint extraction and path caching reuse, thereby improving the efficiency of global path planning. On this basis, PPO-based local obstacle avoidance is activated only when safety thresholds are exceeded, while the remaining path is replanned globally after threat clearance, which confines avoidance computation to a local scope while preserving global path quality. Experiments in static mountainous environments show that KSC requires substantially less computation time than RRT* and Informed RRT* while maintaining competitive path efficiency, and it also outperforms four bio-inspired optimization algorithms across terrains of increasing complexity. Hybrid navigation validation experiments further show that KSC-PPO achieves high mission success, low collision rates, and low avoidance overhead in dynamic mountainous environments. Experiments demonstrate that KSC-PPO decomposes exponential global search space into controllable linear subproblems, significantly enhancing efficiency while ensuring path quality, providing an effective solution for UAV navigation in complex terrain.
Highlights What are the main findings? A bio-inspired phase transition control framework with two distinct motion phases is proposed for UAV swarms, combining a self-propulsion term, an interaction potential term, and a roosting force term. The existence of the translational and vortex motion phases are proven, and the stability properties of the two motion phases are discussed based on Lyapunov theory. What are the implications of the main findings? The proposed framework provides a phase transition mechanism triggered by the roosting force term, enabling UAV swatms to switch between different motion phases. Numerical simulations validate the stability of the two motion phases, and display the transition process triggered by the roosting force term, demonstrating the framework's potential for enhancing swarm adaptability in complex missions.Highlights What are the main findings? A bio-inspired phase transition control framework with two distinct motion phases is proposed for UAV swarms, combining a self-propulsion term, an interaction potential term, and a roosting force term. The existence of the translational and vortex motion phases are proven, and the stability properties of the two motion phases are discussed based on Lyapunov theory. What are the implications of the main findings? The proposed framework provides a phase transition mechanism triggered by the roosting force term, enabling UAV swatms to switch between different motion phases. Numerical simulations validate the stability of the two motion phases, and display the transition process triggered by the roosting force term, demonstrating the framework's potential for enhancing swarm adaptability in complex missions.Abstract This study proposes a bio-inspired control framework for unmanned aerial vehicle (UAV) swarms, designed to emulate the collective motion phase transitions observed in the homing behavior of pigeon flocks. A second-order self-propelled particle model is established, integrating a self-propulsion term, an interaction potential term, and a key roosting force term inspired by the roosting behavior of pigeons. The framework enables the swarm to dynamically switch between a translational motion phase and a vortex motion phase based on the distance to a designated roost location. Based on the proposed swarm model, theoretical analysis proves the stability property of the specific two motion phases under specific conditions. Numerical simulations validate the stability of the two motion phases, demonstrating that UAV swarms can reliably maintain each phase and execute phase transitions triggered by the roosting force. The proposed framework is able to describe the phase transition behavior in the process of pigeons returning home.
Highlights What are the main findings? A novel Weighted Average Algorithm-based Clustering and Routing (WAA-CR) framework that enables secure, resilient, and energy-efficient drone-based communication for disaster response and recovery. Results demonstrate that WAA-CR significantly improves energy efficiency, cluster stability, and end-to-end data delivery compared with existing baseline FANET routing protocols even under the presence of compromised nodes. What are the implication of the main findings? The proposed modular design enables adaptive and self-healing drone-based networks that can sustain connectivity and coordination during infrastructure failures in smart city disaster scenarios. Integrating trust management, lightweight authentication mechanism, mobility awareness, and adaptive maintenance supports energy-efficient, scalable, and reliable communication among drones and between drones and ground shelters, improving network stability and performance for emergency response and post-disaster recovery scenarios.Highlights What are the main findings? A novel Weighted Average Algorithm-based Clustering and Routing (WAA-CR) framework that enables secure, resilient, and energy-efficient drone-based communication for disaster response and recovery. Results demonstrate that WAA-CR significantly improves energy efficiency, cluster stability, and end-to-end data delivery compared with existing baseline FANET routing protocols even under the presence of compromised nodes. What are the implication of the main findings? The proposed modular design enables adaptive and self-healing drone-based networks that can sustain connectivity and coordination during infrastructure failures in smart city disaster scenarios. Integrating trust management, lightweight authentication mechanism, mobility awareness, and adaptive maintenance supports energy-efficient, scalable, and reliable communication among drones and between drones and ground shelters, improving network stability and performance for emergency response and post-disaster recovery scenarios.Abstract In today's densely populated and technology-driven smart cities, natural and human-made disasters increasingly threaten the resilience of communication infrastructures, creating critical challenges for maintaining reliable connectivity. The failure of conventional networks during crises significantly hampers emergency response, coordination, and information dissemination. To address these challenges, this paper presents Weighted Average Algorithm-based Clustering and Routing (WAA-CR), a novel, secure, and adaptive UAV-based framework for disaster response and recovery. WAA-CR integrates three key components: shelters or Ground Control Stations (GCSs) as communication anchors and support hubs, survivable clustering and routing using a WAA-based metaheuristic optimizer, and secure and trustworthy drone communication enabled by a lightweight trust evaluation mechanism, and authentication model. The framework formulates a multi-objective optimization model that simultaneously minimizes the number of active UAVs and routing cost, while maximizing trust, communication reliability, and coverage. Cluster head (CH) election and routing decisions are guided by a composite fitness function that considers residual energy, link stability, mobility, and dynamic trust scores. Additionally, an adaptive maintenance mechanism enables dynamic reconfiguration to handle CH failures, trust degradation, or mobility-driven topology changes. Extensive simulations conducted in MATLAB R2020ademonstrate that WAA-CR significantly outperforms existing baseline FANET protocols in terms of energy efficiency, cluster stability, trust accuracy, and end-to-end delivery performance. These results validate the proposed framework's effectiveness in building resilient, scalable, and secure UAV-based communication networks for post-disaster environments.
Highlights What are the main findings? This study proposes a joint optimization framework for multi-UAV-relay-assisted aviation FSO/RF networks. The model jointly optimizes trajectory, ground station association, and power allocation, while systematically incorporating multi-dimensional practical constraints-including platform dynamics, information causality, co-channel interference, meteorological effects, and multi-UAV collision avoidance-thereby significantly enhancing its engineering applicability. This study introduces a dynamic power control strategy that achieves an optimal trade-off between desired signal enhancement and co-channel interference suppression. By flexibly adjusting power to partially substitute for spatial adjustments, this strategy proves effective in complex multi-station and multi-UAV scenarios. What are the implications of the main findings? This study addresses a critical gap in existing aviation FSO/RF research, which often relies on idealized models while overlooking practical engineering constraints. This work provides both a systematic theoretical framework and a practically implementable technical pathway for multi-UAV relay communications in complex aviation scenarios. The proposed "multi-node, multi-constraint, multi-variable" joint optimization approach can be extended to the trajectory and resource co-design of other space-based communication networks (e.g., high-altitude platforms and satellite relays), laying a theoretical and algorithmic foundation for high-capacity, high-reliability transmission in future integrated space-air-ground information networks.Highlights What are the main findings? This study proposes a joint optimization framework for multi-UAV-relay-assisted aviation FSO/RF networks. The model jointly optimizes trajectory, ground station association, and power allocation, while systematically incorporating multi-dimensional practical constraints-including platform dynamics, information causality, co-channel interference, meteorological effects, and multi-UAV collision avoidance-thereby significantly enhancing its engineering applicability. This study introduces a dynamic power control strategy that achieves an optimal trade-off between desired signal enhancement and co-channel interference suppression. By flexibly adjusting power to partially substitute for spatial adjustments, this strategy proves effective in complex multi-station and multi-UAV scenarios. What are the implications of the main findings? This study addresses a critical gap in existing aviation FSO/RF research, which often relies on idealized models while overlooking practical engineering constraints. This work provides both a systematic theoretical framework and a practically implementable technical pathway for multi-UAV relay communications in complex aviation scenarios. The proposed "multi-node, multi-constraint, multi-variable" joint optimization approach can be extended to the trajectory and resource co-design of other space-based communication networks (e.g., high-altitude platforms and satellite relays), laying a theoretical and algorithmic foundation for high-capacity, high-reliability transmission in future integrated space-air-ground information networks.Abstract The utilization of unmanned aerial vehicle (UAV) relays has significantly improved the availability and reliability of free-space optical (FSO) communication links within airborne communication backhaul networks. This paper proposes an FSO/RF dual-hop backhaul network employing multiple UAV relays and investigates a joint optimization scheme for three-dimensional (3D) trajectories and resource allocation of multiple UAVs. In this scheme, network throughput is maximized by jointly optimizing three variables: the association between the UAVs and the ground stations (GSs), power allocation, and the UAVs' trajectories. Moreover, to enhance the engineering applicability of this research, we systematically incorporate multi-dimensional practical constraints-including the motion of the AWACS, platform dynamics, information causality, co-channel interference, the influence of weather variations, and multi-UAV collision avoidance. Furthermore, to address this challenging mixed-integer non-convex optimization problem, an iterative algorithm is developed. This algorithm integrates the principles of block coordinate descent with successive convex approximation, thereby alternately optimizing the three variable blocks within each iterative cycle. Numerical simulations confirm that the proposed scheme achieves a substantial throughput improvement in the multi-UAV-assisted FSO/RF hybrid backhaul network in comparison with other benchmark schemes.
Highlights What are the main findings? An auxiliary system is designed to alleviate the input saturation effect: an auxiliary variable is introduced into the controller to ensure that high-precision tracking capability can still be maintained under actuator saturation conditions. A fixed-time prescribed performance controller is developed by simultaneously considering actuator saturation constraints and actuator faults, realizing fixed-time prescribed performance fault-tolerant control under actuator faults and saturation conditions. What are the implications of the main findings? By combining a fixed-time disturbance observer with an anti-saturation mechanism, and integrating this with a fixed-time sliding mode control method, the problem of physical actuator output limits has been effectively resolved. The deep integration of error transformation techniques with fault-tolerant logic provides a control framework that balances performance and steady-state safety for high-reliability flight missions in constrained environments.Highlights What are the main findings? An auxiliary system is designed to alleviate the input saturation effect: an auxiliary variable is introduced into the controller to ensure that high-precision tracking capability can still be maintained under actuator saturation conditions. A fixed-time prescribed performance controller is developed by simultaneously considering actuator saturation constraints and actuator faults, realizing fixed-time prescribed performance fault-tolerant control under actuator faults and saturation conditions. What are the implications of the main findings? By combining a fixed-time disturbance observer with an anti-saturation mechanism, and integrating this with a fixed-time sliding mode control method, the problem of physical actuator output limits has been effectively resolved. The deep integration of error transformation techniques with fault-tolerant logic provides a control framework that balances performance and steady-state safety for high-reliability flight missions in constrained environments.Abstract With the gradual development of science and technology, increasingly complex application environments impose higher requirements on the control performance of quadrotor unmanned aerial vehicles (UAVs). This requires UAVs to achieve high-performance tracking control under various challenging conditions, such as model uncertainties, external disturbances, actuator saturation, and actuator faults. Considering these issues, this paper proposes a novel fixed-time controller. First, to address the external disturbances and model uncertainties that UAVs may encounter during flight, a non-singular fixed-time terminal sliding mode control method is proposed, and a variable exponential fixed-time adaptive sliding mode disturbance observer is introduced to improve the estimation accuracy of the lumped disturbances. Secondly, considering the impact of actuator input saturation, an auxiliary system is constructed to mitigate the actuator saturation problem. Finally, a fixed-time fault-tolerant control scheme with actuator saturation and prescribed performance constraints is investigated for quadrotor UAVs. The convergence performance of the controller is rigorously established based on Lyapunov stability theory. Comparative simulation results are provided to demonstrate the effectiveness of the proposed control strategy.
Highlights What are the main findings? Decomposing the relative wind velocity vector into body frame axial and lateral components using quaternion-derived heading angles reduces the root mean square error of cruise-phase energy prediction by approximately 15.9% compared with the induced-power baseline model. The physics-informed wind-coupled model maintains a relatively stable absolute prediction error profile at wind speeds exceeding 6 m/s. In this regime, the baseline model exhibits a systematic residual that increases with wind speed, suggesting that the added aerodynamic feature terms capture wind-induced power fluctuations that static parametric formulations leave unaddressed. What are the implications of the main findings? The proposed framework retains the computational efficiency of closed-form parametric equations, suggesting that it may be suitable for direct deployment on onboard flight controllers to support real-time battery monitoring and wind-resilient energy estimation in unmanned logistics operations. By comparing the reachable-range envelope implied by the baseline and proposed models under strong crosswinds, the results suggest that physics-informed energy prediction could contribute to more stable range estimation inputs for downstream flight-endurance and energy-reserve sizing routines.Highlights What are the main findings? Decomposing the relative wind velocity vector into body frame axial and lateral components using quaternion-derived heading angles reduces the root mean square error of cruise-phase energy prediction by approximately 15.9% compared with the induced-power baseline model. The physics-informed wind-coupled model maintains a relatively stable absolute prediction error profile at wind speeds exceeding 6 m/s. In this regime, the baseline model exhibits a systematic residual that increases with wind speed, suggesting that the added aerodynamic feature terms capture wind-induced power fluctuations that static parametric formulations leave unaddressed. What are the implications of the main findings? The proposed framework retains the computational efficiency of closed-form parametric equations, suggesting that it may be suitable for direct deployment on onboard flight controllers to support real-time battery monitoring and wind-resilient energy estimation in unmanned logistics operations. By comparing the reachable-range envelope implied by the baseline and proposed models under strong crosswinds, the results suggest that physics-informed energy prediction could contribute to more stable range estimation inputs for downstream flight-endurance and energy-reserve sizing routines.Abstract This study aims to improve the accuracy of cruise-phase power consumption prediction for multirotor unmanned aerial vehicles operating under varying wind conditions. Existing parametric energy models typically retain the wind velocity vector in the ground or inertial reference frame, and this representation does not distinguish between axial drag contributions along the fuselage and lateral attitude-correction contributions perpendicular to it. The proposed framework addresses this limitation through a physics-informed coordinate transformation that projects the measured wind vector into the body frame of the aircraft using quaternion-derived heading angles, yielding separate axial and lateral wind components. These components enter the power model as two additional predictors that augment the induced-power baseline, with the axial term following a cubic airspeed-power relationship consistent with parasitic drag formulations and the lateral term following a quadratic relationship consistent with attitude-correction mechanics. The framework is validated on a publicly available flight dataset, which comprises 188 flights of a DJI Matrice 100 quadcopter across payloads of 0 to 0.75 kg, ground speeds of 4 to 12 m/s, and altitudes of 25 to 100 m. Compared with the induced-power baseline, the proposed model reduces the root mean square error by 15.9% and the mean squared error by 29.7% during the cruise phase. The improvement is larger when wind speeds exceed 6 m/s, a regime in which the baseline residuals increase while the proposed model retains a comparatively stable error profile. Residual analysis indicates that baseline errors follow an approximately quadratic trend relative to the axial and lateral wind components, consistent with established parasitic-power and attitude-correction formulations. The closed-form structure of the proposed model is compatible with onboard execution on flight controllers, which suggests a feasible pathway toward its use as the power-prediction module within downstream range-estimation and energy-reserve sizing routines.
Highlights What are the main findings? A multi-vehicle and multi-UAV collaborative rescue routing model is developed for flood-disrupted environments, jointly considering road feasibility, water-depth-dependent vehicle speeds, multi-point UAV sorties, payload-dependent energy consumption, and vehicle-UAV synchronization. In the Guangdong 2024 flood case, the proposed dual-track solution framework shows that the heuristic method scales effectively to large instances and can outperform time-limited MILP solutions in the 135-node instance, while the priority-weighted objective improves response timeliness for critical nodes. Comparative experiments confirm that the proposed method outperforms vehicle-only delivery, single-stop UAV collaboration, two-stage decomposition, and ALNS without embedded DP refinement. What are the implications of the main findings? Vehicle-UAV collaboration is a practical and effective rescue logistics strategy when flood-induced inundation degrades road accessibility. Sensitivity analysis suggests that maintaining a moderate trade-off coefficient (alpha in 0.2-0.8) helps preserve both overall completion efficiency and priority-response performance, providing quantitative decision support for balancing mission makespan and critical-node protection in humanitarian operations.Highlights What are the main findings? A multi-vehicle and multi-UAV collaborative rescue routing model is developed for flood-disrupted environments, jointly considering road feasibility, water-depth-dependent vehicle speeds, multi-point UAV sorties, payload-dependent energy consumption, and vehicle-UAV synchronization. In the Guangdong 2024 flood case, the proposed dual-track solution framework shows that the heuristic method scales effectively to large instances and can outperform time-limited MILP solutions in the 135-node instance, while the priority-weighted objective improves response timeliness for critical nodes. Comparative experiments confirm that the proposed method outperforms vehicle-only delivery, single-stop UAV collaboration, two-stage decomposition, and ALNS without embedded DP refinement. What are the implications of the main findings? Vehicle-UAV collaboration is a practical and effective rescue logistics strategy when flood-induced inundation degrades road accessibility. Sensitivity analysis suggests that maintaining a moderate trade-off coefficient (alpha in 0.2-0.8) helps preserve both overall completion efficiency and priority-response performance, providing quantitative decision support for balancing mission makespan and critical-node protection in humanitarian operations.Abstract Flood disasters often disrupt road networks and severely reduce ground accessibility, hindering the timely delivery of emergency supplies. To address this challenge, this study investigates a collaborative routing problem involving multiple vehicles and multiple UAVs under road disruptions and formulates a mixed-integer linear programming model that jointly minimizes mission makespan and priority-weighted response time for critical nodes. The model explicitly captures road feasibility, vehicle speeds affected by flood depth, multi-point UAV sorties, payload-dependent energy consumption, and vehicle-UAV spatiotemporal synchronization. To balance solution quality and scalability, a dual-track solution framework is developed: exact optimization is used for small instances, while a adaptive large neighborhood search algorithm with embedded dynamic programming is designed for larger instances. A case study based on the 2024 Guangdong flood with 135 demand points shows that the heuristic can obtain high-quality solutions efficiently and outperforms time-limited MILP solutions on large instances. Comparative experiments further demonstrate that multi-point sorties, integrated coordination, and embedded sortie refinement are all crucial to performance improvement. Sensitivity analysis indicates that setting the trade-off coefficient alpha within 0.2-0.8 provides a robust balance between overall mission efficiency and timely response to critical nodes.
Highlights What are the main findings? A hybrid multi-agent reinforcement learning algorithm (H-MAPPO) is proposed for joint deployment and resource coordination of satellites and UAVs in UAV-assisted space-air-ground-sea integrated networks (SAGSINs). The proposed method significantly improves communication coverage and reduces total system cost compared with baseline algorithms including PPO, MAPPO, MADDPG, HASAC, DQN, Greedy, and the random baseline. What is the implication of the main findings? The results highlight the important role of UAV-assisted platforms in extending communication coverage and improving service flexibility in maritime SAGSIN environments.Highlights What are the main findings? A hybrid multi-agent reinforcement learning algorithm (H-MAPPO) is proposed for joint deployment and resource coordination of satellites and UAVs in UAV-assisted space-air-ground-sea integrated networks (SAGSINs). The proposed method significantly improves communication coverage and reduces total system cost compared with baseline algorithms including PPO, MAPPO, MADDPG, HASAC, DQN, Greedy, and the random baseline. What is the implication of the main findings? The results highlight the important role of UAV-assisted platforms in extending communication coverage and improving service flexibility in maritime SAGSIN environments.Abstract To support intelligent maritime applications, space-air-ground-sea integrated networks (SAGSINs) have been introduced in maritime communications to provide wide coverage and reliable network services. In unmanned aerial vehicle (UAV)-assisted SAGSIN architectures, UAVs can flexibly extend coverage and provide on-demand communication and computing support. However, due to the high mobility of low Earth orbit (LEO) satellites and the limited endurance of UAVs, single-platform deployment strategies struggle to provide both flexibility and scalability in maritime communication networks. To mitigate the service instability caused by satellite orbital dynamics and limited UAV endurance, we propose a Hybrid Multi-Agent Proximal Policy Optimization (H-MAPPO)-based joint satellite-UAV deployment scheme for UAV-assisted SAGSIN systems. The proposed method optimizes joint UAV positioning and resource allocation to enhance communication coverage while reducing overall operational cost. By incorporating satellite orbital dynamics and UAV mobility into a multi-agent reinforcement learning (MARL) framework, adaptive resource scheduling can be achieved under time-varying maritime demands. Simulation results show that the proposed H-MAPPO algorithm achieves superior convergence performance, higher user coverage, and lower total system cost compared with learning-based, random, and heuristic methods while maintaining stable and robust performance under varying user densities and network scales.
Highlights What are the main findings? A behavior-based swarm control architecture is proposed for coordinated motion in time-varying Double-Gyre flow fields, where a graph attention mechanism guided by agent heading angles dynamically assigns inter-agent interaction weights. A Bayesian optimization framework is introduced to determine the optimal behavioral weights for swarm coordination. What are the implications of the main findings? This method enhances swarm adaptability, coordination stability, and control performance under complex environmental disturbances. The framework provides a robust and scalable basis for swarm control in dynamic flow environments, with promising potential for autonomous marine multi-agent applications.Highlights What are the main findings? A behavior-based swarm control architecture is proposed for coordinated motion in time-varying Double-Gyre flow fields, where a graph attention mechanism guided by agent heading angles dynamically assigns inter-agent interaction weights. A Bayesian optimization framework is introduced to determine the optimal behavioral weights for swarm coordination. What are the implications of the main findings? This method enhances swarm adaptability, coordination stability, and control performance under complex environmental disturbances. The framework provides a robust and scalable basis for swarm control in dynamic flow environments, with promising potential for autonomous marine multi-agent applications.Abstract Conventional cooperative control methods for multi-AUV systems typically rely on quasi-steady hydrodynamic assumptions and do not explicitly account for time-varying uncertainties in ocean dynamics. In addition, controller parameters are often tuned empirically. As a result, under complex disturbed flow fields and communication constraints, AUV swarms are prone to group fragmentation and reduced polarization, which undermines stable cooperative navigation. To address these limitations, we propose a double-gyre-flow-optimized autonomous underwater vehicle swarm (DGF-OAS) model for coordinated operations in time-varying flow fields. The proposed model incorporates a heading-aware graph attention mechanism to adaptively adjust adjacency weights among agents with different roles. It further integrates the Lennard-Jones potential to preserve safe inter-vehicle spacing and embeds a periodically varying double-gyre flow field to characterize ocean disturbances. Bayesian optimization is then employed to automatically identify suitable weights for the alignment and attraction-repulsion terms, thereby improving swarm cohesion and environmental adaptability. Simulation results demonstrate that, under flow-field disturbances, DGF-OAS achieves group polarization of up to 96%, reduces the average task completion time by 15.84% compared with the baseline model, and attains a task completion rate of 97%, significantly outperforming the compared methods. These findings indicate that the proposed approach exhibits strong adaptability and stability in complex environments and offers an effective solution for AUV swarm control.
Highlights What are the main findings? A fully actuated deformable integrated aerial platform (IAP) is developed for aerial manipulation with time-varying dynamics. A geometric MRAC framework on SO(3) is proposed and validated in real-world grasping and assembly experiments. What are the implications of the main findings? The results support the use of adaptive geometric control for deformable IAPs in contact-rich manipulation tasks.Highlights What are the main findings? A fully actuated deformable integrated aerial platform (IAP) is developed for aerial manipulation with time-varying dynamics. A geometric MRAC framework on SO(3) is proposed and validated in real-world grasping and assembly experiments. What are the implications of the main findings? The results support the use of adaptive geometric control for deformable IAPs in contact-rich manipulation tasks.Abstract Integrated aerial platforms (IAPs), composed of multiple unmanned aerial vehicles (UAVs), can perform tasks such as aerial grasping and cooperative manipulation. In this paper, we introduce and design an IAP with joint-driven active deformation capability. During deformation and tasks such as aerial grasping, configuration-dependent variations in inertia and the center of mass (CoM) challenge control stability. To address this issue, a geometric model reference adaptive control (MRAC) scheme is developed on SO(3) to ensure robust and decoupled control under these time-varying conditions. The almost global stability of the closed-loop system is rigorously established through Lyapunov-based analysis and verified in simulations. The advantages of the proposed controller are further validated through real-world deformation experiments on a self-developed prototype, which successfully performs aerial grasping and assembly tasks.
Highlights What are the main findings? The proposed PPO-based method significantly improves the convergence speed and stability of UAV trajectory planning in vehicle tracking scenarios. Compared with traditional path planning approaches, the optimized policy effectively reduces tracking error and enhances trajectory smoothness. What are the implications of the main findings? The results demonstrate that reinforcement learning-based trajectory planning can provide reliable and adaptive tracking performance in dynamic traffic environments. The proposed method offers a practical solution for intelligent transportation applications such as traffic monitoring, autonomous escorting, and aerial-ground cooperative systems.Highlights What are the main findings? The proposed PPO-based method significantly improves the convergence speed and stability of UAV trajectory planning in vehicle tracking scenarios. Compared with traditional path planning approaches, the optimized policy effectively reduces tracking error and enhances trajectory smoothness. What are the implications of the main findings? The results demonstrate that reinforcement learning-based trajectory planning can provide reliable and adaptive tracking performance in dynamic traffic environments. The proposed method offers a practical solution for intelligent transportation applications such as traffic monitoring, autonomous escorting, and aerial-ground cooperative systems.Abstract Unmanned Aerial Vehicle (UAV) tracking of ground moving targets holds significant applications in domains such as intelligent transportation, logistics distribution, and environmental monitoring, placing greater demands on efficient and stable path-planning methods for vehicular tracking. This study investigates a UAV path tracking approach based on a deep reinforcement learning algorithm, Proximal Policy Optimization (PPO). Starting from the kinematic characteristics of UAVs and ground vehicles, a 3D path planning model was constructed that considers spatial coordinates, velocity, and attitude constraints. A well-designed objective function-including tracking error minimization, energy optimization, and safety distance constraints-was incorporated. By designing the state space, action space, and reward function, the PPO algorithm is capable of adaptive learning in complex environments. Compared with traditional Artificial Potential Field (APF), Q-learning, and TD3 algorithms, PPO better balances exploration and exploitation and demonstrates stronger learning stability and global optimization capability in dynamic multi-obstacle scenarios. Simulation results show that PPO-based UAV path planning outperforms Q-learning and other comparative algorithms in terms of tracking accuracy, convergence speed, and robustness. In specific scenarios, Q-learning achieves a trajectory error of approximately 1 m, TD3 and APF exhibit errors around 0.3 m with noticeable oscillations, and PPO achieves an error of about 0.2 m. The UAV can follow the vehicle trajectory smoothly, with a more continuous path and rapidly converging, stable error curves, indicating the promising application potential of PPO in intelligent UAV control. The PPO-based UAV-tracking path planning method effectively enhances the UAV's intelligent decision-making and path optimization capabilities, providing new technical approaches and a research foundation for intelligent UAV traffic and cooperative control systems.
Highlights What are the main findings? Standardized Decision Framework: Developed a two-stage protocol that replaces arbitrary model selection with a systematic approach, mapping specific infrastructural and informational constraints to five distinct modeling regimes. Empirical Evaluation and Consistency: The framework's qualitative logic, evaluated against a 24-study independent literature holdout set, achieved high consistency with observed literature choices, demonstrating structural reliability in identifying the most suitable modeling strategy. Quantitative Multi-Criteria Ranking: Integrated the Analytic Hierarchy Process (AHP) to mathematically rank models based on context-specific priorities like predictive accuracy, physical interpretability, development cost, and customization adaptability. What are the implications of the main findings? Operational Resilience through "Fallback Flexibility": The framework allows researchers to seamlessly pivot to the next highest-ranked feasible alternative when unforeseen roadblocks, such as equipment failure or data loss, occur. Optimized Resource Allocation: By enforcing strict feasibility filters before evaluating preferences, the framework prevents the over-allocation of computational and experimental research resources. Enhanced Reproducibility and Standardization: Supported by an open-source Python GUI, this methodology fosters greater consistency and transparency within the energy-aware UAV research community.Highlights What are the main findings? Standardized Decision Framework: Developed a two-stage protocol that replaces arbitrary model selection with a systematic approach, mapping specific infrastructural and informational constraints to five distinct modeling regimes. Empirical Evaluation and Consistency: The framework's qualitative logic, evaluated against a 24-study independent literature holdout set, achieved high consistency with observed literature choices, demonstrating structural reliability in identifying the most suitable modeling strategy. Quantitative Multi-Criteria Ranking: Integrated the Analytic Hierarchy Process (AHP) to mathematically rank models based on context-specific priorities like predictive accuracy, physical interpretability, development cost, and customization adaptability. What are the implications of the main findings? Operational Resilience through "Fallback Flexibility": The framework allows researchers to seamlessly pivot to the next highest-ranked feasible alternative when unforeseen roadblocks, such as equipment failure or data loss, occur. Optimized Resource Allocation: By enforcing strict feasibility filters before evaluating preferences, the framework prevents the over-allocation of computational and experimental research resources. Enhanced Reproducibility and Standardization: Supported by an open-source Python GUI, this methodology fosters greater consistency and transparency within the energy-aware UAV research community.Abstract The growing deployment of unmanned aerial vehicles (UAVs) in energy-constrained applications has highlighted the need for appropriate energy consumption models. However, selecting between physics-based (white-box) and data-driven (black-box) modeling paradigms remains a largely implicit process. Researchers often navigate undocumented trade-offs among required predictive accuracy, empirical data availability, and access to aerodynamic testing infrastructure without a formalized structure. This study proposes a two-stage decision-making framework to formalize UAV energy model selection. In the first stage, a qualitative decision tree is inductively derived from a corpus of 23 recent studies, explicitly mapping infrastructural and informational constraints to five distinct modeling regimes. In the second stage, the Analytic Hierarchy Process (AHP) is applied to quantitatively evaluate the feasible alternatives based on context-specific criteria: accuracy, interpretability, development cost, and customization adaptability. The structural logic of the framework is evaluated against an independent set of 24 holdout studies, demonstrating a high degree of consistency between the framework's recommendations and the methodologies employed in the literature. Furthermore, the quantitative AHP scoring introduces "fallback flexibility," enabling researchers to mathematically identify alternative modeling strategies when primary experimental conditions are compromised. Supported by an open-source Python graphical interface, this framework aims to reduce methodological ambiguity and support more structured, reproducible model selection in UAV energy research.
Highlights What are the main findings? Mission-constrained stereo-inertial navigation and real-time optimization for a Mars rotorcraft (Tianwen-3 concept). A Parity-Window sliding-window back-end achieves bounded per-update complexity and deterministic onboard execution under Tianwen-3-class compute constraints. What are the implications of the main findings? Enables reliable real-time navigation under tight the computing budgets of space avionics. Flight experiments demonstrate accurate and robust state estimation and provide strong evidence for the feasibility of Tianwen-3-class rotorcraft navigation.Highlights What are the main findings? Mission-constrained stereo-inertial navigation and real-time optimization for a Mars rotorcraft (Tianwen-3 concept). A Parity-Window sliding-window back-end achieves bounded per-update complexity and deterministic onboard execution under Tianwen-3-class compute constraints. What are the implications of the main findings? Enables reliable real-time navigation under tight the computing budgets of space avionics. Flight experiments demonstrate accurate and robust state estimation and provide strong evidence for the feasibility of Tianwen-3-class rotorcraft navigation.Abstract Reliable autonomous navigation for Tianwen-3-class Mars rotorcraft must satisfy both sampling-level accuracy and hard real-time execution under severe onboard computational constraints. To address this challenge, we develop MarsBird-VII, a mission-constrained stereo visual-inertial navigation system that combines a computation-aware vision front-end with a Parity-Window sliding-window optimization back-end. The front-end decouples high-rate tracking from feature replenishment to bound perception latency, while the back-end alternates updates over interleaved state subsets and preserves full-window coupling through unified marginalization. Unlike simply reducing the sliding-window size, the proposed strategy reduces the per-update optimization cost without shrinking the geometric observation horizon, thereby improving the accuracy-runtime trade-off for embedded avionics. Earth-analog flight experiments demonstrate strong navigation performance under mission-relevant conditions. In full-sequence evaluation, the proposed system achieves an SE(3)-aligned translation APE of 0.31 m RMSE/0.47 m Max and further reaches 0.06 m RMSE/0.15 m Max on a nominal stable segment. Runtime profiling over 5000+ update cycles shows that the Parity-Window back-end keeps the maximum optimization latency below 58.32 ms, satisfying the 66.7 ms hard real-time deadline while maintaining accuracy close to full-window optimization. These results show that the proposed system provides a practical balance of accuracy, robustness, and deterministic real-time performance for Tianwen-3-class Mars rotorcraft navigation.
Highlights What are the main findings? The Cognitive-Intent Decoupled Architecture (CIDA) resolves awareness loss in contested environments by separating belief reconstruction from tactical guidance. CIDA achieves a 96.15% mission success rate, significantly outperforming standard memory-augmented MARL baselines. What are the implications of the main findings? Separating 'where it is safe' from 'where to go' avoids passive survival and brittle reward shaping in RF-denied swarms. The proposed steady-state mechanism stabilizes Transformer-based reinforcement learning, providing a reliable pipeline for safety-critical swarm deployments.Highlights What are the main findings? The Cognitive-Intent Decoupled Architecture (CIDA) resolves awareness loss in contested environments by separating belief reconstruction from tactical guidance. CIDA achieves a 96.15% mission success rate, significantly outperforming standard memory-augmented MARL baselines. What are the implications of the main findings? Separating 'where it is safe' from 'where to go' avoids passive survival and brittle reward shaping in RF-denied swarms. The proposed steady-state mechanism stabilizes Transformer-based reinforcement learning, providing a reliable pipeline for safety-critical swarm deployments.Abstract Multi-unmanned aerial vehicle (UAV) platforms integrate radio-frequency (RF) sensing, datalinks, and onboard embedded compute; adversarial electronic warfare (EW) degrades these subsystems through jamming and forces decentralized control policies to act on fragmented observations-a setting aligned with intelligent electronic systems and autonomous robotics in contested spectrum. Cooperative swarms then face two compounding failure modes: loss of coherent situational awareness, and reward-driven passive survival that suppresses mission completion. Memory-based multi-agent reinforcement learning (MARL) partially addresses the first but tends to reinforce the second; dense intent shaping addresses the second but becomes unreliable when observations are incomplete. We propose CIDA (Cognitive-Intent Dual-Stream Architecture), a reinforcement learning framework that decouples belief reconstruction from tactical intent at the representation level while coupling them through a unified actor-critic update. The cognitive stream encodes a 64-step observation history with a pre-normalized Transformer to reconstruct threat belief; the intent stream supplies a hierarchical potential field (reconnaissance, threat-weighted engagement, and approach incentives). A steady-state training mechanism (dynamic reward scaling and adaptive gradient clipping) stabilizes Transformer-based on-policy learning under non-stationary multi-agent dynamics. In a complex terrain scenario with SAM, AAA, and jammer assets, CIDA reaches 96.15% task success versus 12.21% (memoryless PPO) and 25.28% (MAPPO+RNN), with ablations showing nonlinear coupling and emergent tactics such as jammer bypass and weak-sector traversal. Results are robust to a four-fold sweep of the intent-shaping weight (above 90% success).
