We present HERE, an active 3D scene reconstruction framework based on neural radiance fields, enabling high-fidelity implicit mapping. Our approach centers around an active learning strategy for camera trajectory generation, driven by accurate identification of unseen regions, which supports efficient data acquisition and precise scene reconstruction. The key to our approach is epistemic uncertainty quantification based on evidential deep learning, which directly captures data insufficiency and exhibits a strong correlation with reconstruction errors. This allows our framework to more reliably identify unexplored or poorly reconstructed regions compared to existing methods, leading to more informed and targeted exploration. Additionally, we design a hierarchical exploration strategy that leverages learned epistemic uncertainty, where local planning extracts target viewpoints from high-uncertainty voxels based on visibility for trajectory generation, and global planning uses uncertainty to guide large-scale coverage for efficient and comprehensive reconstruction. The effectiveness of the proposed method in active 3D reconstruction is demonstrated by achieving higher reconstruction completeness compared to previous approaches on photorealistic simulated scenes across varying scales, while a hardware demonstration further validates its real-world applicability.

Visual navigation models often struggle in real-world dynamic environments due to limited robustness to the sim-to-real gap and the difficulty of training policies tailored to target deployment environments (e.g., households, restaurants, and factories). Although real-to-sim navigation simulation using 3D Gaussian Splatting (GS) can mitigate this gap, prior works have assumed only static scenes or unrealistic dynamic obstacles, despite the importance of safe navigation in dynamic environments. To address these issues, we propose ReaDy-Go, a novel real-to-sim simulation pipeline that synthesizes photorealistic dynamic scenarios for target environments. ReaDy-Go generates photorealistic navigation datasets for dynamic environments by combining a reconstructed static GS scene with dynamic human GS obstacles, and trains policies robust to both the sim-to-real gap and moving obstacles. The pipeline consists of three components: (1) a dynamic GS simulator that integrates scene GS with a human animation module, enabling the insertion of animatable human GS avatars and the synthesis of plausible human motions from 2D trajectories, (2) navigation dataset generation for dynamic environments that leverages the simulator, a robot expert planner designed for dynamic GS representations, and a human planner, and (3) policy learning using the generated datasets. ReaDy-Go outperforms baselines across target environments in both simulation and real-world experiments, demonstrating improved navigation performance even after sim-to-real transfer and in the presence of moving obstacles. Moreover, zero-shot sim-to-real deployment in an unseen environment indicates its generalization potential. Project page: https://syeon-yoo.github.io/ready-go-site/.

Driving without considering the preferred separation distance from surrounding vehicles may cause discomfort for users. To address this limitation, we propose a planning framework that explicitly incorporates user preferences regarding the desired level of safe clearance from surrounding vehicles. We design a questionnaire purposefully tailored to capture user preferences relevant to our framework, while minimizing unnecessary questions. Specifically, the questionnaire considers various interaction-relevant factors, including the surrounding vehicle’s size, speed, position, and maneuvers of surrounding vehicles, as well as the maneuvers of the ego vehicle. The response indicates the user-preferred clearance for the scenario defined by the question and is incorporated as constraints in the optimal control problem. However, it is impractical to account for all possible scenarios that may arise in a driving environment within a single optimal control problem, as the resulting computational complexity renders real-time implementation infeasible. To overcome this limitation, we approximate the original problem by decomposing it into multiple subproblems, each dealing with one fixed scenario. We then solve these subproblems in parallel and select one using the cost function from the original problem. To validate our work, we conduct simulations using different user responses to the questionnaire. We assess how effectively our planner reflects user preferences compared to preference-agnostic baseline planners by measuring preference alignment.

Fast replanning of the local trajectory is essential for autonomous robots to ensure safe navigation in crowded environments, as such environments require the robot to frequently update its trajectory due to unexpected and dynamic obstacles. In such settings, relying on the single trajectory optimization may not provide sufficient alternatives, making it harder to quickly switch to a safer trajectory and increasing the risk of collisions. While parallel trajectory optimization can address this limitation by considering multiple candidates, it depends heavily on welldefined initial guidance, which is difficult to obtain in complex environments. In this work, we propose a method for identifying the multimodality of the optimal trajectory distribution for safe navigation in crowded 3D environments without initial guidance. Our approach ensures safe trajectory generation by projecting sampled trajectories onto safe constraint sets and clustering them based on their potential to converge to the same locally optimal trajectory. This process naturally produces diverse trajectory options without requiring predefined initial guidance. Finally, for each trajectory cluster, we utilize the Model Predictive Path Integral framework to determine the optimal control input sequence, which corresponds to the local maxima of a multimodal optimal trajectory distribution. We first validate our approach in simulations, achieving higher success rates than existing methods. Subsequent hardware experiments demonstrate that our fast local trajectory replanning strategy enables a drone to safely navigate crowded environments.

We present a demonstration showcasing the Autonomy Testbed for Multi-purpose Orbiting Systems (ATMOS), a planar spacecraft-analog robot designed for hardware-in-the-loop evaluation of guidance and control strategies in microgravity-like conditions. Using ATMOS as the physical test platform, we investigate the design, analysis, and performance evaluation of control architectures for remotely operated spacecraft under round-trip communication delays. In this work, we develop and experimentally validate a control strategy that combines state prediction and trajectory tracking control to perform a docking maneuver, accounting for time-varying random communication latency between ground operators and the ATMOS system. The demonstration includes a long-distance remote control experiment between Seoul and Stockholm, introducing realistic intercontinental delays and variability. The results highlight the capability of ATMOS to support rapid, reliable, and cost-effective testing of spacecraft teleoperation concepts, establishing a first step toward robust validation of on-orbit operations in microgravity-like environments.

Achieving full autonomy in robotics requires safe navigation and reliable collision avoidance in unknown environments. This demands robust perception and mapping, as well as control strategies that can effectively utilize environmental information while enforcing safety constraints. However, to address noisy and sparse measurements from inexpensive onboard sensors commonly used on low-power mobile platforms, probabilistic and continuous map representations have emerged as more effective alternatives to traditional deterministic occupancy grids. Despite their advantages in perception and mapping, their integration into safety-critical control frameworks remains underexplored. In this work, we address this gap by proposing a reactive and safe control strategy that operates over probabilistic maps constructed using Gaussian Process-based continuous representations. Our method combines a reactive control framework with control barrier functions, and incorporates risk measures that consider both occupancy probabilities and their associated uncertainties. Furthermore, for practical implementation, we introduce an object-level probabilistic mapping approach suited for indoor environments and integrate it with the proposed control strategy. The resulting framework enables risk-aware, reactive navigation in previously unknown environments. We validate its effectiveness through extensive simulations and hardware experiments, demonstrating robust vision-based navigation under uncertainty.

Constraint admissible positively invariant (CAPI) sets play a pivotal role in ensuring safety in control and planning applications, such as the recursive feasibility guarantee of explicit reference governor and model predictive control. However, existing methods for finding CAPI sets for nonlinear systems are often limited to single equilibria or specific system dynamics. This limitation underscores the necessity for a method to construct a CAPI set for general reference tracking control and a broader range of systems. In this work, we leverage recent advancements in learning-based methods to derive Lyapunov functions, particularly focusing on those with piecewise-affine activation functions. Previous attempts to find an invariant set with the piecewise-affine neural Lyapunov function have focused on the estimation of the region of attraction with mixed integer programs. We propose a methodology to determine the maximal CAPI set for any reference with the neural Lyapunov function by transforming the problem into multiple linear programs. Additionally, to enhance applicability in real-time control scenarios, we introduce a learning-based approach to train the estimator, which infers the CAPI set from a given reference. The proposed approach is validated with multiple simulations to show that it can generate a valid CAPI set with the given neural Lyapunov functions for any reference. We also employ the proposed CAPI set estimation method in the explicit reference governor and demonstrate its effectiveness for constrained control.

Control engineering has made significant progress in addressing various challenges for real-world applications. For the next stage of robotics automation, it is necessary to guarantee formal safety as well as conventional control tasks including set point stabilization and trajectory tracking. This survey focuses on a review of safe motion planning and control, especially for mobile robots. We explore various advancements in safety-critical control problems that can ultimately be formulated as an ideal infinite-horizon optimal control, and classify them into two clusters: 1) receding horizon methods and 2) safety filtering approaches. Receding horizon methods, such as nonlinear model predictive control (NMPC) and reachability-based receding horizon motion planning, use finite-horizon sliding windows for tractability. Safety filtering methods, employing techniques such as control barrier function (CBF) and reference governor (RG), adjust nominal signals to enforce safety. This survey highlights the challenges of ensuring safety in dynamic and complex environments where mobile robots are deployed, where their computational limitations and uncertainties in dynamic models are significant factors. By providing a comprehensive review of current methodologies and specifying future research directions, we aim to offer a solid foundation for developing efficient safety-critical control methodologies for mobile robots.

For safe vision-based control applications, perception-related constraints have to be satisfied in addition to other state constraints. In this paper, we deal with the problem where a multirotor equipped with a camera needs to maintain the visibility of a point of interest while tracking a reference given by a high-level planner. We devise a method based on reference governor that, differently from existing solutions, is able to enforce control-level visibility constraints with theoretically assured feasibility. To this end, we design a new type of reference governor for linear systems with polynomial constraints which is capable of handling time-varying references. The proposed solution is implemented online for the real-time multirotor control with visibility constraints and validated with simulations and an actual hardware experiment.

This letter presents a new online multi-agent trajectory planning algorithm that guarantees to generate safe, dynamically feasible trajectories in a cluttered environment. The proposed algorithm utilizes a linear safe corridor (LSC) to formulate the distributed trajectory optimization problem with only feasible constraints, so it does not resort to slack variables or soft constraints to avoid optimization failure. We adopt a priority-based goal planning method to prevent the deadlock without an additional procedure to decide which robot to yield. The proposed algorithm can compute the trajectories for 60 agents on average 15.5 ms per agent with an Intel i7 laptop and shows a similar flight distance and distance compared to the baselines based on soft constraints. We verified that the proposed method can reach the goal without deadlock in both the random forest and the indoor space, and we validated the safety and operability of the proposed algorithm through a real flight test with ten quadrotors in a maze-like environment.

Motion strategies for multiple robots that actively acquire information in a dynamic environment have been widely studied. However, the existing active information gathering algorithms are restricted by the assumption of linear target dynamics or completely known models. In this study, we formulate the active information gathering problem with the belief distribution of the desired target information with unknown underlying dynamics. The reward function is derived based on the mutual information of the measurement and belief distribution, and it can be efficiently computed under the Gaussian assumption on the belief distribution. Moreover, a decentralized path planner is designed to maximize the reward function, which scales well in terms of both the numbers of agents and targets. We apply the proposed planner to an active target tracking scenario and validate the performance and scalability through a numerical simulation.

Visual navigation has been widely used for state estimation of micro aerial vehicles (MAVs). For stable visual navigation, MAVs should generate perception-aware paths which guarantee enough visible landmarks. Many previous works on perception-aware path planning focused on sampling-based planners. However, they may suffer from sample inefficiency, which leads to computational burden for finding a global optimal path. To address this issue, we suggest a perception-aware path planner which utilizes topological information of environments. Since the topological class of a path and visible landmarks during traveling the path are closely related, the proposed algorithm checks distinctive topological classes to choose the class with abundant visual information. Topological graph is extracted from the generalized Voronoi diagram of the environment and initial paths with different topological classes are found. To evaluate the perception quality of the classes, we divide the initial path into discrete segments where the points in each segment share similar visual information. The optimal class with high perception quality is selected, and a graph-based planner is utilized to generate path within the class. With simulations and real-world experiments, we confirmed that the proposed method could guarantee accurate visual navigation compared with the perception-agnostic method while showing improved computational efficiency than the sampling-based perception-aware planner.

This article introduces a fully actuated multirotor flight system utilizing the tilting-thruster-type multirotor ($T^3$-multirotor), a new type of multirotor platform that enables six-controllable-degree-of-freedom flight with minimal structural heterogeneity compared to conventional multirotor designs. This new multirotor platform consists of upper and lower parts (or thruster and fuselage parts), with a unique kinematic structure and dedicated servomechanism that controls the relative attitude between the two parts. With the new mechanism, the fuselage of the $T^3$-multirotor can control the translational and rotational motions independently of each other, allowing six-degree-of-freedom motion that was not possible with conventional multirotors. A dedicated robust control algorithm is developed based on a thorough analysis of system dynamics to derive accurate six-degree-of-freedom motion of the platform. The flight control performance of the platform is validated through simulations and actual experiments. Several flight tasks are also performed to demonstrate the potential of the $T^3$-multirotor in overcoming the limitations of conventional multirotors.

This letter deals with the problem of multiple robots working together to explore and gather at the global maximum of the unknown field. Given noisy sensor measurements obtained at the location of robots with no prior knowledge about the environmental map, Gaussian process regression can be an efficient solution to construct a map that represents spatial information with confidence intervals. However, because the conventional Gaussian process algorithm operates in a centralized manner, it is difficult to process information coming from multiple distributed sensors in real-time. In this work, we propose a multi-robot exploration algorithm that deals with the following challenges: i) distributed environmental map construction using networked sensing platforms; ii) online learning using successive measurements suitable for a multi-robot team; iii) multi-agent coordination to discover the highest peak of an unknown environmental field with collision avoidance. We demonstrate the effectiveness of our algorithm via simulation and a topographic survey experiment with multiple UAVs.

This paper suggests a new cooperative transportation system with T-3-multirotors. From the promising characteristics of T-3-multirotor for fully-actuated motion, the proposed system is able to track a desired payload trajectory which is impossible with a conventional underactuated multirotor. Unlike other cooperative methods such as slung-load transportation and aerial manipulator, the system can accommodate simpler mechanism by directly attaching cargo to the multirotor body. We build the dynamic model of the system and design a trajectory tracking controller considering low-level control. Dynamic equations show that the system is overactuated and it can follow arbitrary twice-differentiable trajectory if input and mechanical feasibilities are satisfied. For tracking control, linear control methods with linear quadratic regulator(LQR) and PID are sufficient for stable and efficient tracking. Control allocation is used for the sake of determining input for each actuator. Further improvement is expected by combining the proposed approach with a trajectory generation module.

No AccessTechnical NoteBidirectional Thrust Vectoring Control of a Rectangular Sonic JetMyungYeon Lee, MyungJun Song, DaBin Kim and Yeol LeeMyungYeon LeeKorea Aerospace University, Goyang-si 10540, Republic of Korea, MyungJun SongKorea Aerospace University, Goyang-si 10540, Republic of Korea, DaBin KimKorea Aerospace University, Goyang-si 10540, Republic of Korea and Yeol LeeKorea Aerospace University, Goyang-si 10540, Republic of KoreaPublished Online:24 Jan 2018https://doi.org/10.2514/1.J056598SectionsRead Now ToolsAdd to favoritesDownload citationTrack citations ShareShare onFacebookTwitterLinked InRedditEmail About References [1] Deere K. A., “Summary of Fluidic Thrust Vectoring Research Conducted at NASA Langley Research Center,” 21st AIAA Applied Aerodynamics Conference, AIAA Paper 2003-3800, June 2003. 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H. and Lee Y., “Development of the High-Accuracy Multi-Component Balance for Fluidic Thrust Vectoring Nozzle of UAV,” Journal of the Korean Society for Aeronautical and Space Sciences, Vol. 41, No. 2, 2013, pp. 142–149. doi:https://doi.org/10.5139/JKSAS.2013.41.2.142 CrossrefGoogle Scholar Previous article Next article FiguresReferencesRelatedDetailsCited byMultiaxis Shock Vectoring Control of Overexpanded Supersonic Jet Using a Genetic AlgorithmFernando Zigunov , MyungJun Song, Prabu Sellappan and Farrukh S. Alvi7 October 2022 | Journal of Propulsion and Power, Vol. 0, No. 0 What's Popular Volume 56, Number 6June 2018 CrossmarkInformationCopyright © 2018 by MyungYeon Lee, MyungJun Song, DaBin Kim, and Yeol Lee. Published by the American Institute of Aeronautics and Astronautics, Inc., with permission. All requests for copying and permission to reprint should be submitted to CCC at www.copyright.com; employ the ISSN 0001-1452 (print) or 1533-385X (online) to initiate your request. See also AIAA Rights and Permissions www.aiaa.org/randp. TopicsAerodynamic PerformanceAerodynamicsAeronautical EngineeringAeronauticsAircraft EnginesCombustion ChambersFlow RegimesFluid DynamicsJet EnginesOblique Shock WavePropulsion and PowerRocket EngineRocketryShock Waves KeywordsThrust Vector ControlSonic JetChamber PressureCoanda EffectSupersonic FlowStagnation TemperatureAspect RatioMach DiskThrust Vector AngleOperating CostsAcknowledgmentThis research was supported by the Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Education, Science and Technology (2016R1D1A1B03930471).PDF Received5 August 2017Accepted17 December 2017Published online24 January 2018