The coordination of Automated Guided Vehicles (AGVs) in high-density industrial environments represents a critical challenge within Logistics 4.0, as traditional traffic management methods often lead to inefficiencies caused by negotiation-based priority assignment. To overcome the resulting limitations, this paper presents an innovative AGV traffic management system based on a Lifelong Multi-Agent Path Finding (L-MAPF) algorithm operating on roadmaps generated with Non-Uniform Rational B-Splines (NURBS) curves. The approach guarantees locally optimal coordination and ensures safe operation of large and heterogeneous AGVs. Building on this concept, the proposed framework integrates a modified version of the Bounded Horizon Conflict Based Search (CBS) technique within a Rolling Horizon Conflict Resolution strategy, utilizing an extended time horizon for each agent to enable effective conflict resolution in corridors identified by a topological map. In contrast to state-of-the-art methods for AGV fleet traffic management, the proposed solution is designed for real-world, non-standardized (i.e., non-grid-like) industrial settings characterized by narrow bidirectional corridors and high-traffic density, where AGVs of various sizes and capabilities operate simultaneously. Key contributions include an anytime conflict resolution strategy with adaptive time horizon regulation, an execution layer for safe and standard-compliant interaction with real AGVs, and an advanced mechanism for deadlock detection and resolution. Experimental results obtained in realistic industrial environments demonstrate higher throughput, with improvements of up to 11% over a conventional rule-based traffic management system, a state-of-the-art industrial method, and a priority-based L-MAPF variant, while maintaining continuous operation and improved efficiency.
Swarm navigation of micro-/nanorobots has been attracting extensive attention, as it is a vital technique for microrobotic applications, for example, targeted drug delivery/therapy and micromanipulation. Researchers have shown that, controlled by a global magnetic field, millions of micro-/nanorobots can assemble and then efficiently navigate to targeted locations. However, current navigation control schemes for microswarms do not consider constraints on swarm actuation, for example, motion direction and rotating angular velocity, which would result in non-optimal navigation performance and even failure. In this work, we propose an actuation-constrained control framework for microswarms that explicitly treats important swarm motion properties as hard constraints in both trajectory planning and motion control. In our framework, we derive a constrained coarse-to-fine differential dynamic programming (DDP)-based trajectory planner that can generate the optimal trajectory in obstacle environments under the constraint of microswarm rotational angular velocity to maintain stable swarm assembly. Regarding trajectory tracking, we formulate a nonholonomic model predictive control (MPC) scheme, which makes the swarm optimally track the planned trajectory while complying with constraints on motion direction and rotational angular velocity. By our framework, microswarms can navigate with stable swarm assembly and the fastest motion speed, realizing optimal navigation performances. A series of comparative simulations and experiments validate the advantages of our framework in terms of swarm stability and navigation speed. Experimental results also show that our framework can work with different environmental morphologies, for example, discrete obstacles and channels, showing high adaptability to working scenarios. Navigation with moving obstacles and fluid flow further proves the capability of our framework for dynamic environments.
Unmanned underwater vehicles (UUVs) have been deployed for fish net-pen visual inspection (FNVI) in offshore aquaculture. Limited energy capacity of onboard power supplies constrains the UUV's working range and operating time. To minimize the energy consumption by the UUV during the FNVI of the Blue Endeavour Project (an offshore salmon farm of the New Zealand King Salmon Company), an energy-optimal linear quadratic tracking (EO-LQT) control scheme is proposed in this paper. For EO-LQTs implementation, a new Linear-Parameter-Varying (LPV) system that approximates the nonlinear UUV dynamics model with an accuracy of approximately 99% regardless of the operating points in real-time, with the modified versions of Bh & amacr;skara I's sine approximation and Shirali's cosine approximation, is developed. The use of the Lagrangian under the Principle of Least Action with the UUV's kinetic energy and the non-quadratic thruster power function in the EO-LQT performance index (PI) is demonstrated. The steps to solve the Hamilton-Jacobi-Bellman (HJB) equation with the non-quadratic Hamiltonian are detailed to derive the new analytical EO-LQT optimal control form. Five EO-LQT controllers with different PIs are tested against the conventional LQT (CO-LQT) controller in both high-fidelity simulations under simulated disturbance speed up to 0.9 m/s and pool experiments, reducing energy consumption up to 37.1%. As key comparison metrics for the pose tracking and energy consumption, the mean-absolute-error (MAE) and T200 thruster power function are used to validate the effectiveness of the proposed EO-LQT controllers, compared to the CO-LQT controller.
Degeneracy caused by the repetition of geometric features remains a significant bottleneck in robotic state estimation systems like odometry and SLAM. To address this challenge, we propose a novel approach based on screw theory to model feature constraints and analyze degeneracy. Unlike conventional methods, our approach derives feature constraint representations via geometric operations rather than by derivative computations, and decouples the cost-function formulation, yielding a more robust and interpretable framework. Initially, a feature constraint model is formulated using the screw representation. Subsequently, a degeneracy analysis model and a judgment formula are presented, both grounded in screw theory and the feature constraint model. The proposed model inherently separates translational and helical degeneracy while accurately estimating environmental parameters, such as the position and pitch of the rotation axis, to reduce assessment errors, enable outlier rejection, and improve the robustness of degeneracy detection. A novel feature extraction and management method is proposed to improve computation efficiency and coverage of the feature in sparsely scanned scenarios. Simulations and real-world experiments show that our approach improves the robustness and accuracy of degeneracy detection while reducing the computation time by more than 70%. A dimensionless stability metric for the judgment formula is proposed, showing that our approach significantly outperforms existing state-of-the-art (SOTA) approaches by more than two orders of magnitude. To date, this study constitutes the first formal endeavor to conceptualize and systematically analyze the problem of helical degeneracy, providing a novel and rigorous perspective on the inherent challenges of robotic state estimation.
Reliable robot autonomy hinges on decision-making systems that account for uncertainty without imposing overly conservative restrictions on the robot’s action space. We introduce Chance-Constrained Via-Point-Based Stochastic Trajectory Optimisation ( CC - VPSTO ), a real-time capable framework for generating task-efficient robot trajectories that satisfy constraints with high probability by formulating stochastic control as a chance-constrained optimisation problem. Since such problems are generally intractable, we propose a deterministic surrogate formulation based on Monte Carlo sampling, solved efficiently with gradient-free optimisation. To address bias in naïve sampling approaches, we quantify approximation error and introduce padding strategies to improve reliability. We focus on three challenges: (i) sample-efficient constraint approximation, (ii) conditions for surrogate solution validity, and (iii) online optimisation. Integrated into a receding-horizon MPC framework, CC-VPSTO enables reactive, task-efficient control under uncertainty, balancing constraint satisfaction and performance in a principled manner. The strengths of our approach lie in its generality, that is, no assumptions on the underlying uncertainty distribution, system dynamics, cost function, or the form of inequality constraints; and its applicability to online robot motion planning. We demonstrate the validity and efficiency of our approach in both simulation and on a Franka Emika robot. Videos and additional material are made available here: https://sites.google.com/oxfordrobotics.institute/cc-vpsto .
Trust forms the bedrock of successful human interactions, and its integration into human-robot collaboration remains a critical challenge. Contemporary research predominantly explores human trust in robotic systems, focusing on refining the appearance and behavior of artificial agents to foster their acceptability in social settings. However, this systematic review centers on the less-explored dimension of trust mechanisms within autonomous robotic systems. Our aim is to survey what we define as Computational Trust models, which encompass a robot's capability to both assess the trustworthiness of other agents ("Artificial Trust") and to predict their levels of trust towards itself ("Natural Trust"). To achieve this objective, an initial set of 1916 papers, ranging from 2013 to 2023, was collected from IEEE Xplore, Scopus, and ISI Web of Science. Eligibility criteria were then applied to this set to select works that designed a Computational Trust model for a robotics application, which was validated through an experiment. These criteria were agreed upon by all authors to ensure unanimous decisions on whether to retain or remove results. At the end of this process, 101 key papers were identified. Following the selection process, we conducted thorough analyses to cluster these works based on the type of Computational Trust model used, the application domain, the robotic platforms employed in the validation, the experimental design, and the evaluation metrics. Finally, we identify common trends in this emerging branch of Human-Robot Interaction and provide guidelines for scholars wishing to contribute to this field.
Self-reconfigurable wheeled mobile robots (SRWMRs) are capable of achieving multiple motion modes and subsequent switching between them through the coordinated sequential movements of multiple rocker-bogie joints, leading to overcoming dynamic obstacles posed by different terrains. However, real-time coordination of internal joints for executing reconfiguration actions while maintaining strong adhesion between the wheels and terrain remains challenging, particularly during action transitions. To enhance multimode motion capability by using a unified model, this work develops an inverse kinematics control (IKC) method, including 3D kinematic modeling of an SRWMR with actively and passively articulated suspensions, and additional motion-constraint inequalities for multi-joints in the wheel-suspension system. Specifically, the 3D model is built to achieve horizontal movements and vertical lifting of the robot chassis and its wheels. To further stabilize body posture and reduce wheel slippage during multimode motion, motion constraints are proposed to regulate the relative velocities among multiple joints and the displacement of the robot's center of mass. According to the results of physical experiments with the HIT-MRII robot, Wheel Rolling, Wheel Crabbing, Wheel Lifting, Robot Chassis Lifting, Robot Creeping modes, and parts of their hybrid modes are achieved steadily and safely by the developed IKC method. The maximum motion performance of each mode is achieved by the proposed motion constraints. The enhanced mobility of the robot is demonstrated by comparing various traversal modes on soil terrain and presenting the corresponding control strategies.
Underwater ROVs (Remotely Operated Vehicles) are indispensable for subsea exploration and task execution, yet typical teleoperation engines based on egocentric (first-person) video feeds restrict human operators’ field-of-view and limit precise maneuvering in complex, unstructured underwater environments. To address this, we first propose EgoExo, a geometry-driven solution integrated into a visual SLAM pipeline that synthesizes on-demand exocentric (third-person) views from egocentric camera feeds. We further propose EgoExo++, which extends beyond 2D exocentric view synthesis (EgoExo) to augment a piecewise-planar 2.5D ground surface estimation on-the-fly. Its anchor-free aerial viewpoint supports ground-relative reasoning, such as clearance and terrain-based navigation marker following. The computations involved are closed-form and rely solely on egocentric views and monocular SLAM estimates, which makes it portable across existing teleoperation engines and robust to varying waterbody characteristics. We validate the geometric accuracy of our approach through extensive experiments of 2-DOF indoor navigation and 6-DOF underwater cave exploration in challenging low-light conditions. To assess operational benefits, we conduct two user studies with simulation and real-world data, each involving 15 participants, comparing baseline egocentric teleoperation and EgoExo++. Results indicate improved system usability (SUS), reduced perceived workload (NASA-TLX), and significant gains in objective teleoperation performance, including 16% faster missions, 5-fold reduction in path deviation ratio, and fewer collision events (2 vs 5 across trials). Furthermore, we highlight the role of EgoExo++ augmented visuals in supporting shared autonomy, operator training, and embodied teleoperation. This new interactive approach to ROV teleoperation presents promising opportunities for future research in subsea telerobotics. The source packages for EgoExo++ are available at: https://github.com/uf-robopi/EgoExo .
This paper presents a fast and low-vibration tracking control strategy for an uncertain fully flexible link-joint (FFLJ) robot manipulator. Due to the highly underactuated nature of the system, along with the presence of uncertainties and external disturbances, a two-time-scale singular perturbation (SP) approach is adopted to decompose the system into slow and fast subsystems. To control the slow subsystem, a fractional-order fast terminal sliding mode control (FOFTSMC) is designed, ensuring rapid convergence with minimal transient and steady-state errors, which is essential for vibration suppression. Additionally, a free-drift partially adaptive super twisting reaching law is incorporated to prevent overestimation of control inputs, mitigate uncertainties and disturbances, and reduce chattering while optimizing energy efficiency. For the fast subsystem, a linear state-space representation is formulated based on the slow subsystem’s control input, explicitly considering uncertainties. An optimal proportional derivative linear quadratic regulator (PD-LQR) is then employed to regulate the fast subsystem dynamics, leading to a robust composite control scheme. A rigorous stability analysis guarantees the global asymptotic stability of both subsystems and the overall closed-loop control system. Simulation results confirm the effectiveness of the proposed strategy in handling nonlinearities, underactuation, and uncertainties. Compared to the FOFTSMC and the integer order robust fuzzy SMC (IORFSMC), the proposed free-drift adaptive FOFTSMC (AFOFTSMC) demonstrates superior performance, achieving approximately 45% and 85% improvement, respectively, in the first link tracking, and 4% and 65% improvement, respectively, in the second link tracking in terms of the integral of time multiplied by absolute error (ITAE) index. These results highlight the proposed approach’s robustness, efficiency, and capability to ensure precise trajectory tracking, vibration suppression, and chattering reduction while maintaining energy efficiency.
For magnetically actuated robots driven by external magnetic sources (EMSs), rapid and stable localization of the internal permanent magnet (IPM) is essential for closed-loop control. Existing sensor-array-based localization methods require a fixed model with an offline-calibrated dipole moment to separate the magnetic field generated by the IPM from the measured total field. However, model accuracy varies with the distance to the sensor array, leading to inaccurate separation and unstable localization. Additionally, nonlinear optimization is typically used for localization with sensor arrays, which is computationally inefficient. We propose a sampling-based method that jointly estimates the dipole moments and the IPM position, thereby eliminating the need for magnetic field separation. Given the EMS positions and the measured total field, our particle importance analysis efficiently identifies the sampled particle that most closely approximates the dipole position and concurrently estimates the optimal dipole moments. Adaptive particle sampling enables high-accuracy localization using sparsely sampled particles, thereby improving computational efficiency. We validated the approach experimentally with EMSs actuated by manipulators and electromagnetic systems. With a 72-sensor array and the IPM located 20 cm above the sensor plane, the method achieved an average computation time of 1.2 ms and localization errors of 4.70 mm and 4.17 degrees. Compared with an optimization-based method, our method reduced the maximum localization error by 59% and improved computational efficiency by a factor of 30. The method reliably localized in permanent magnet, electromagnetic, and hybrid magnetic fields. Finally, we applied the method to the simultaneous localization and actuation of a magnetic endoscope, validating its feasibility in a colon phantom.
Reliable absolute positioning remains a challenge in autonomous ground robotics, particularly in complex and dynamic real-world environments. The fusion of drift-free global positioning and precise local positioning is essential to ensure continuous and accurate localization in mobile ground robots. However, a benchmark dataset encompassing challenging scenarios for both absolute and relative positioning is still lacking, which limits further research and comprehensive evaluation of fusion-based Simultaneous Localization and Mapping (SLAM) methods for autonomous ground robots. To fill this gap, we introduce a ground robot dataset for multi-sensor navigation in diverse environments. All sensors are well calibrated, and Global Navigation Satellite System (GNSS), Inertial Measurement Unit (IMU), camera, and Light Detection and Ranging (LiDAR) measurements are hardware-synchronized. Furthermore, several auxiliary sensors are also included in our system, which are often overlooked in existing datasets but may be vital in certain applications. We perform data acquisition in a variety of challenging environments, both outdoors and indoors. In outdoor scenarios, ground truth is provided by a high-level integrated navigation system, while in indoor environments, it is obtained using a motion capture system. We evaluate the positioning performance of several baseline algorithms on our dataset, and the results show that current methods need further improvement in specific challenging scenarios. To advance relevant research, we make the dataset and associated tools publicly available. The project can be accessed at https://github.com/lizhipro/MSN-DE.git .
The knee joint plays a critical role in locomotion but is susceptible to overuse injuries, motivating the development of assistive exoskeletons. Current designs face a fundamental trade-off between achieving kinematic compatibility with the knee's complex polycentric motion and providing effective variable-stiffness functionality for biomechanical support. This study presents a novel cable-driven multisegment exoskeleton to reconcile these competing requirements through an integrated biomimetic design. The proposed system employs redundant rotational joints and a linear guide rail to passively accommodate natural joint kinematics while enabling wide-range stiffness regulation (0-207 Nm/rad) via active cable length adjustment. This single-actuator approach achieves dynamic stiffness regulation, deterministic torque transmission with an effective moment arm exceeding 70 mm, and seamless state modulation within a low-profile structure (0.63 kg). Benchtop characterization confirmed precise stiffness control across the operational range (rmse <= 0.035 Nm/rad). Human subject experiments revealed significant muscular effort reduction during demanding tasks without compromising natural joint kinematics, including 23.9% decrease in peak vastus lateralis activation during incline walking and 29.2% reduction during squatting compared to unassisted conditions. These results validate the exoskeleton's ability to reconcile anatomical compatibility with physiologically relevant stiffness regulation, representing a significant advance in knee assistive technology with broad applications in clinical rehabilitation and physical performance augmentation. This study bridges a critical gap in knee exoskeleton development, offering a unified solution for comfortable and effective assistance across dynamic tasks.
To use new robot hardware, it is necessary to develop a control program tailored to the specific robot. Considering the reusability of software among robots is crucial for minimizing the effort involved in this process and maximizing software reuse across different robots. This paper proposes a method to reduce the effort of software development for a specific robot by considering hardware-level reusability, using a Learning-from-Observation (LfO) framework with a pre-designed skill library. The LfO framework first represents the demonstrated actions in hardware-independent representations, referred to as task models, from observing human demonstrations, and captures the necessary parameters for the interaction between the environment and the robot (Ikeuchi et al., 2024). Then, for executing the demonstrated actions, a set of skill agents is employed to convert the representations of the task models into robot commands. This paper focuses on the latter part of the LfO framework and explores a hardware-independent design approach for these skill agents. Especially, we dedicate a manipulation skill library. To achieve this, we describe these skill agents in a hardware-independent manner based on the physical characteristics of a grasped object and an environment. This paper, first, defines a necessary and sufficient skill-agent set corresponding to covering all possible actions, and considers the design principles for these skill agents. We provide concrete examples of such skill agents and demonstrate the practicality of these skill agents by showing that the same representations can be executed on two different robots, Nextage and Fetch, and two different end-effectors, Shadow Hand-Lite and parallel gripper.
This paper presents NavWareSet , a novel dataset crafted to advance socially compliant robot navigation research. NavWareSet provides multi-modal recordings of both socially compliant and non-compliant robot trajectories in controlled indoor environments. Drawing upon seven carefully selected scenarios, it captures complex human-robot interactions and a range of navigation challenges that mirror realistic social contexts. NavWareSet establishes a rich dataset for evaluating and training navigation algorithms by incorporating two distinct robot platforms—Toyota Human Support Robot (HSR) and Clearpath’s Jackal—and systematically varying their navigation behaviors. With data modalities spanning lidar, RGB-D camera, odometry, and human position annotations, NavWareSet enables fine-grained analysis of the robot’s decision-making process and its impact on human comfort and safety. Ultimately, this dataset provides a versatile resource for developing robust, ethically guided navigation policies and for measuring their performance across a range of social situations. More information can be seen at: https://anr-navware.github.io/navwareset/ .
Actuator faults in autonomous mobile robotic systems pose significant challenges, especially in unpredictable environments where system reliability is paramount. Fault tolerant control (FTC) strategies, particularly those leveraging actuator redundancy, have been explored to address these issues. However, traditional methods commonly rely on explicit fault diagnosis, which can be resource-intensive and challenging to implement accurately. This paper introduces a novel approach that combines deep reinforcement learning (DRL) with a linearised optimal model-based controller to achieve actuator fault recovery without explicit fault diagnosis. The integration of DRL within a model-based controller framework enhances system stability and fault recovery capabilities. The chosen application platform for this study is an autonomous underwater vehicle (AUV), where the partial or total failure of a mission critical component such as a thruster could jeopardise the success of the mission and potentially render the vehicle unrecoverable in the event of a fault. In this application case, a linear quadratic regulator (LQR) controller is employed as the model-based controller, while the soft actor-critic (SAC) algorithm is used as the DRL component to handle fault recovery. The DRL model is trained and evaluated in simulation before being directly applied to the physical AUV. The proposed method’s effectiveness is demonstrated through comparisons with a standard LQR controller, a conventional adaptive LQR controller and the proposed hybrid LQR-SAC controller. The results indicate that the LQR-SAC controller outperforms the standard and conventional adaptive LQR controllers in maintaining system performance under fault conditions, achieving a significant reduction in trajectory tracking error on a physical AUV.
Passive deformation due to compliance is a commonly used benefit of soft robots, providing opportunities to achieve robust actuation with few active degrees of freedom. Soft growing robots in particular have shown promise in navigation of unstructured environments due to their passive deformation. If their collisions and subsequent deformations can be better understood, soft robots could be used to understand the structure of the environment from direct tactile measurements. In this work, we propose the use of soft growing robots as mapping and exploration tools. We do this by first characterizing collision behavior during discrete turns, then leveraging this model to develop a geometry-based simulator that models robot trajectories in 2D environments. Finally, we demonstrate the model and simulator validity by mapping unknown environments using Monte Carlo sampling to estimate the optimal next deployment given current knowledge. Over both uniform and non-uniform environments, this selection method rapidly approaches ideal actions, showing the potential for soft growing robots in unstructured environment exploration and mapping.
Partially observable Markov decision processes (pomdps) are a general and principled framework for motion planning under uncertainty. Despite tremendous improvement in the scalability of pomdp solvers, long-horizon pomdps remain difficult to solve. To alleviate the difficulty, this paper proposes a new approximate online pomdp solver, called reference-based online pomdp planning via rapid state space sampling (rop-ras3). rop-ras3 uses novel extremely fast sampling-based motion planning techniques to sample the state space and generate a diverse set of macro-actions online, which are then used to bias belief-space sampling and infer high-quality policies without requiring exhaustive enumeration of the action space-a fundamental constraint for modern online pomdp solvers. rop-ras3 converges to a near-optimal reference-based solution at a rate that depends on the number of sampled actions, rather than the size of the action space. rop-ras3 is evaluated on various long-horizon pomdps with up to 3000 lookahead steps and 35-dimensional state spaces, where the state, action and observation spaces can be continuous, discrete, or a hybrid of discrete and continuous. Although the reference-based optimal solution may not be the same as the optimal pomdp solution, empirical results indicate that in all of these problems, in terms of success rate, rop-ras3 outperforms other state-of-the-art methods by up to multiple folds. We also demonstrate the capability of our approach on a physical robot demonstration. This work extends the theory and empirical results of our ISRR24 paper. Code can be found at .
Robotic deformable object manipulation (DOM) faces critical challenges in industrial and medical applications due to under-actuation, unpredictable deformation, and partial observability. Model-free methods often suffer from unstable Jacobians arising from ill-conditioned observations, while physics-based models typically depend on precise parameters and volumetric meshing, limiting their real-time practicality. We propose a wavelet-boundary element method (BEM) framework that leverages multiscale wavelet descriptors to control 3D deformations directly from efficient feedback modalities, such as contours and curves. By coupling wavelets with BEM, we derive an analytical deformation Jacobian that functions independently of material stiffness (e.g., Young's modulus), relying solely on an online-calibrated Poisson's ratio. This mesh-free formulation significantly enhances real-time performance and robustness against sensor occlusion. Validated in simulation and on the da Vinci Research Kit (dVRK) with phantom and ex vivo animal tissue, our method achieves millimetre-level accuracy. Comparative studies against Fourier-based, model-free, and online finite element method (FEM) approaches demonstrate superior stability and computational efficiency. Notably, our framework achieves convergence speeds significantly faster than online FEM by avoiding volumetric computations, while resolving ill-conditioning through spatial-frequency localization. This work advances deformable object manipulation in unstructured environments, particularly in surgical robotics, where stability under partial observability is essential. Project page: https://junleihu.github.io/projects/dwtbem/.
In this paper, we present a robotic ultrasound system that implements explicit contact force control and force-based full probe orientation optimization to achieve stable, responsive, and high-quality ultrasound imaging. Traditional robotic ultrasound systems often rely on implicit force control methods, such as admittance control, which are limited by their underlying motion-control loops. In contrast, our approach directly regulates contact force and moment at the end-effector, enabling rapid adaptation to heterogeneous tissue properties and dynamic environments. We benchmark the proposed explicit force controller against a state-of-the-art integral adaptive admittance controller, demonstrating a significant reduction in phase lag from 121 degrees to 5.76 degrees at force tracking frequencies exceeding typical respiratory rates. In a second set of benchmarking experiments, compared to the admittance-based method, the explicit force controller achieves a reduction in average force tracking error of 77.7 % when the phantom is moving toward the transducer, and 72.6 % when the phantom is moving away. We integrate the explicit force and moment controller into a six-DoF haptic framework that renders physically-grounded interaction forces to the operator while the robot autonomously regulates contact force and optimizes probe alignment based on acoustic coupling. Validation across static and dynamic scans, as well as under external perturbations, shows that the system consistently maintains target force and moment profiles, aligns the probe with local surface normals, and adapts to changing contact conditions. Experimental results demonstrate that explicit force-based control improves ultrasound image quality, as quantified by confidence maps, compared to a manual haptic scan. These findings support the use of explicit force and moment control as an effective approach for robotic ultrasound imaging.
In nature, fish leverage lateral lines to detect subtle variations in flow velocity and pressure. Inspired by this, artificial lateral line systems (ALLS) have been developed, with notable success in underwater target recognition and localization. However, due to the inherent complexity of fluid dynamics, the perceptual capabilities of current systems remain significantly inferior to those of real fish, typically limited to detecting simple objects under fixed spatial configurations between the perceiver and target. In this work, the perception capabilities of ALLS are extended to a more challenging scenario, wherein a free-swimming robotic boxfish is enabled to estimate, in real time, both the position and orientation of a free-swimming robotic koi carp. To improve signal fidelity, a bio-inspired intermittent swimming pattern is introduced to reduce the impact of the perceiver's own oscillations on the flow field and suppress sensor noise. A hybrid network architecture is proposed to extract informative features from complex vortex-induced pressure signals, wherein an attention mechanism is incorporated to facilitate enhanced spatiotemporal feature extraction across sensor channels. This architecture outperforms conventional models in both accuracy and efficiency. Extensive experiments on both Computational Fluid Dynamics and real-world platforms demonstrate that the perceiver can precisely infer the target's position and orientation from local pressure data alone. These results affirm the robustness of the proposed method and shed light on the intermittent behaviors in real fish, offering new avenues for bio-inspired robotic perception.
