A methodology for impedance control based on mechanical vibration theory was presented to select stiffness and damping to meet the desired dynamic response using modal analysis. This methodology provided profound insights into how changes in stiffness and damping matrices influence a robot’s dynamic response under impedance control, by determining natural frequencies and damping ratios in the modal space. In this paper, our analytical and experimental results challenge the claim of a recent paper stating that the use of a null-space projection does not affect the outcomes, which appears to be a claim that is valid based only on their specific choice of stiffness and damping matrices and robot configuration. Through experiments using a 7-DOF Franka Emika Panda robot, we demonstrate the influence of both stiffness/damping matrices and the weighting matrix of null-space control on the responses of robot manipulators with degrees of redundancy performing primary and secondary tasks. We also introduce and show insights of the benefits of switching between the statically and the dynamically consistent projection matrices, contributing to the broader understanding of the dynamic response of redundant robotic manipulators.

To address the limitation of maximum jump height constrained by the maximum torque of hip joint motors in bipedal wheeled robots during trajectory planning using the dual-mass linear spring model (DMLSM), this study aims to reduce the maximum required hip joint torque for achieving equivalent jump heights. Concurrently, reducing the maximum rotational speed of hip joint demanded by equivalent jumps is essential to minimize motor wear and enhance system stability. Building upon the dual-mass model, we propose a trajectory planning method based on hip joint motor state optimization (TPM-HJMSO). This method integrates a nonlinear spring model, cubic polynomial interpolation, and a min–max optimization approach with nonlinear constraints. Simulation results demonstrate two critical advances: TPM-HJMSO achieves 163.16% higher jump heights than the DMLSM under identical maximum hip torque constraints, while reducing maximum hip motor speed by 37.17% when attaining equivalent jump heights. These outcomes validate the method’s superior performance. Precise trajectory tracking is demonstrated through feedforward-plus-PD control in both Webots simulations and physical prototype experiments, verifying TPM-HJMSO’s effectiveness.

The spiral structure of interventional robots is widely utilized for vascular dredging procedures. Variations in the spiral structural parameters of these robots exert a significant impact on the hemodynamic parameters within the vessel during the interventional process; therefore, optimizing the design of these parameters constitutes a crucial prerequisite for ensuring safe diagnosis and treatment. Based on the bidirectional fluid-structure interaction (FSI) between pulsatile blood and elastic blood vessels, theoretical calculations and experimental measurements of the fluid velocity inside the vessel during robotic intervention were conducted. The results demonstrate that the trends and magnitudes of the calculated values are essentially consistent with those of the measured values. Furthermore, the range and variance methods in orthogonal design were applied to numerically investigate the effects of three key spiral parameters – spiral pitch, spiral groove depth, and thread profile – on hemodynamic and vascular parameters, including blood velocity, blood pressure, vascular wall shear stress, and vessel deformation. The findings indicate that spiral groove depth is the primary and most significant factor influencing blood velocity, blood pressure, and vessel deformation. In contrast, spiral pitch emerged as the main and critical factor affecting vascular wall shear stress. Meanwhile, thread profile is found to be a secondary factor with relatively minor impacts on all the aforementioned parameters. To achieve the objective of safe interventional diagnosis and treatment, it is recommended that the robot’s spiral structure be designed with a smaller spiral groove depth, a moderate spiral pitch, and a thread profile featuring strong cutting capability.

This study systematically addresses the design and aerodynamic optimization of tail configurations for bio-inspired flapping-wing robot. Based on a flapping-wing robot prototype, a theoretical model linking tail-size parameters to the pitch static stability margin was established. Six types of tail configurations — arc-shaped tail, triangular-shaped tail, swallow-shaped tail, webbed tail, T-shaped tail, and V-shaped tail — comprising 17 parametrically varied specimens were designed and fabricated. Through a high-precision decoupled wind-tunnel test platform (free stream velocity: 10 m/s, flapping frequency: 2.5 Hz), the three-axis aerodynamic moments (pitch, yaw, roll) under various actuation states were quantitatively measured. The influence of key geometric parameters such as characteristic width, opening angle, and control-surface area on handling and stability was thoroughly investigated. Experimental results show that the T-shaped tail (#53) performs best in pitch control moment and lateral-directional stability margin, with a peak pitch moment of 0.185 N·m — approximately 42 % higher than the baseline configuration — demonstrating the most outstanding overall performance. The V-shaped tail (#62) exhibits excellent lateral-directional control capability under differential actuation, achieving a roll moment of up to 0.106 N·m, albeit with pronounced control coupling effects. This research provides reliable experimental evidence and a theoretical reference for the configuration selection and parameter optimization of tails in flapping-wing robot, offering significant engineering guidance for enhancing flight quality and control effectiveness. In addition, this paper establishes a theoretical framework for preliminary tail size design based on pitch static stability and proposes a parametric design method for multi-configuration tails for flapping-wing robot. The research results not only provide a theoretical basis for tail parameter selection but also offer experimental references for tail configuration optimization and subsequent control-oriented system design.

Precision agriculture has significantly advanced in recent years and is now widely used to improve the quality and sustainability of agricultural products by applying the right treatment, in the right place, at the right time. This paper presents the development and testing of AGRIMARO.Q (AGRIcultural MAte RObot), a novel omnidirectional, three-wheeled robot designed for agricultural applications in structured environments such as greenhouses. The platform features a reconfigurable track-widening mechanism that enhances its versatility by allowing it to navigate crop rows of varying widths. The paper provides a detailed discussion of the robot’s design, focusing on the track adjustment system and steering wheel drive subsystems, as well as the kinematic model and low-level control systems. Then, experimental tests are presented to evaluate both the robot’s energy consumption and its behavior in executing certain trajectories, such as row traversal and inter-row transitions, in different terrain types, replicating typical greenhouse operations.

Spherical robots can conduct surveillance in hostile, cluttered environments without being damaged. Their protective shell can safely house sensors and sensitive components. The pendulum-based spherical robot provides a platform (yoke) for a camera to be housed inside the protective hull. However, lateral oscillations, also known as wobble, become prominent when operated at low speeds, leading to shaky camera feedback. These oscillations are caused by the coupling between the forward and steering motions due to nonholonomic constraints. Designing a controller to limit wobbling in these robots is challenging due to their underactuated nature. In this paper, we propose a model-based (wobble-free) controller to navigate a pendulum-steered spherical robot to perform wobble-free turning maneuvers consisting of circular arcs and straight lines. The model is developed using Lagrange-D’Alembert equations and accounts for the coupled forward and steering motions. Furthermore, we also propose quantifiable metrics for wobble by deriving expressions for radius of curvature, precession rate, wobble amplitude, and wobble frequency during circular motions and designed an input–output feedback linearization-based controller to control the robot’s heading direction and wobble. Additionally, we extend the controller’s capabilities to handle point-to-point navigation, enabling the robot to execute more complex and goal-oriented trajectories. This research ensures the robot maintains wobble-free motion across diverse paths, enhancing its navigational autonomy and the quality of camera feedback. Overall, the proposed controller enables a teleoperator to command a specific forward velocity and pendulum angle as per the desired turning radius or destination while effectively limiting the robot’s lateral oscillations.

This paper proposes a kinematically redundant wall-climbing robot (CR), which employs a kinematically redundant parallel mechanism (PM) as its main body, combined with a serial mechanism, to achieve wall-climbing motion and avoid the dangers associated with manual operation. The improved Grübler-Kutzbach formula is utilized to analyze the degrees of freedom (DOF) of the PM. Subsequently, a kinematic analysis covering position, velocity, singularity, and workspace is conducted to establish the theoretical basis for the robot’s motion planning. To cope with complex working environments, trajectory planning is carried out for the CR, and the complex path planning problem in space is addressed through hierarchical processing, including global path planning, local path planning, and single-step motion planning. After theoretical analysis, kinematic, singularity, workspace analysis, and trajectory planning method of the proposed CR were verified through simulation experiments, confirming the correctness of the kinematic modeling and trajectory solution. Finally, a physical prototype was built to verify the climbing capability of the robot.

The shipborne 3UPS-3SS parallel mechanism is widely used in ship barge operation, but it has problems such as large volume and poor force transmission and acceleration performance. In order to solve the above problems, the kinematic/dynamic performance analysis and multi-objective optimization of scale parameters were carried out on the mechanism. The vector method is used to construct the position inverse solution model and constraint equations, and the kinematic inverse solution and velocity expression are derived, and the velocity Jacobian matrix is obtained. The fast polar coordinate search method was used to plot the reachable workspace by MATLAB software, and the volume expression of the workspace of the mechanism was obtained. The analytical equation of velocity and acceleration of each moving part was established by the differential method; the mechanism dynamics model was established by combining the virtual work principle, and the condition number was calculated and visualized based on the average mass matrix. Based on the distribution of the condition number of the velocity Jacobian matrix and average mass matrix, the force transferability and dynamic dexterity of the mechanism are evaluated. In the end, the workspace volume of the mechanism increased by 7.14%, the average minimum singular value increased by 196.43%, and the dynamic dexterity decreased by 55.69%, which provided a theoretical basis for the control and practical application of the mechanism.

In multi-robot collision avoidance navigation problems, it is common to encounter situations where robots get stuck and cannot avoid collisions or complete navigation tasks, known as deadlock problems. There are various reasons for the occurrence of deadlock problems, such as issues with the navigation method itself. Optimal reciprocal collision avoidance (ORCA) is widely used in multi-robot navigation. However, in some scenes, such as where robots distribute symmetrically, ORCA usually fails to plan reasonable collision avoidance velocities, resulting in the robot getting stuck. In this paper, we aim to address the deadlock problem caused by ORCA. We first analyze and simplify the planning process of ORCA from the principal horizon to break the deadlock situation. Then, deep reinforcement learning is used to optimize the escape velocity and collision avoidance responsibility of ORCA to guarantee collision-free navigation. We compare our method with the original ORCA and other learning-based methods. The results show that the proposed method is beneficial in reducing the deadlock probability and the collision probability in multi-robot navigation.

Motivation and gap: PID-family controllers remain a pragmatic choice for many robotic systems due to their simplicity and interpretability, but tuning stable, high-performing gains is time consuming and typically non-transferable across robot morphologies, payloads, and deployment conditions. Fuzzy gain scheduling can provide interpretable online adjustment, yet its per-joint scaling and consequent parameters are platform dependent and difficult to tune systematically.

Proposed approach: We propose a hierarchical framework for cross-platform tuning of a learnable fuzzy gain-scheduled PID (LF-PID). The controller uses shared fuzzy membership partitions to preserve common error semantics, while learning per-joint scaling and Takagi–Sugeno consequent parameters that schedule PID gains online. Combined with physics-constrained virtual robot synthesis, meta-learning provides cross-platform initialization from robot physical features, and a lightweight reinforcement learning (RL) stage performs deployment-specific refinement under dynamics mismatch. Starting from three base simulated platforms, we generate 232 physically valid training variants via bounded perturbations of mass ($\pm$10%), inertia ($\pm$15%), friction ($\pm$20%), and damping ($\pm$30%).

Results and insight: We evaluate cross-platform generalization on two distinct systems (a 9-DOF serial manipulator and a 12-DOF quadruped) under multiple disturbance scenarios. The RL adaptation stage improves tracking performance on top of the meta-initialized controller, with up to 80.4% error reduction in challenging high-load joints (12.36$^\circ$$\rightarrow$2.42$^\circ$) and 19.2% improvement under parameter uncertainty. We further identify an optimization ceiling effect: online refinement yields substantial gains when the meta-initialized baseline exhibits localized deficiencies, but provides limited improvement when baseline quality is already uniformly strong.

In extreme environments, hexapod robots are highly susceptible to locomotor failure due to leg structure damage or electronic circuit interruptions. Such failures are particularly critical in ipsilateral two-leg loss, where most conventional hexapod designs fail to maintain stability. This paper introduces a hexapod robot platform equipped with a functional arm (FA) and proposes a fault-tolerant strategy based on FA compensation. The FA serves as a task-oriented module under normal conditions and as a compensatory leg during instability caused by leg loss, enabling rapid recovery of stability and walking capacity without complex adjustments to the remaining legs. An optimization algorithm was developed for gait adjustment, using the longitudinal stability margin as the optimization objective with the compensation median angle θ and swing angle α as key parameters. The optimal compensatory positions of the FA were identified for both the ipsilateral middle-hind leg and front-hind leg failure scenarios. Experimental results demonstrate a marked improvement in both walking stability and capability after FA compensation. For both failure modes, the robot’s attitude fluctuations were significantly suppressed, and the stride length was substantially increased, affirming the feasibility and effectiveness of the proposed approach in enhancing the environmental adaptability of multi-legged robots.

To overcome the limitations of conventional robot task allocation algorithms, which often converge to suboptimal solutions, suffer from low computational accuracy, and produce excessively long execution paths as problem scales increase, we propose a novel marine predator algorithm (NMPA). By redesigning the predator–prey encoding mechanism in the traditional marine predator algorithm (MPA) and integrating information-based environmental search strategies with local search techniques, the proposed method substantially improves global exploration capability and solution precision. The NMPA is then evaluated on multiple benchmark and practical scenarios, including the traveling salesman problem (TSP), multi-traveling salesman problem (MTSP), single-robot task allocation with rigid time windows, and multi-robot task allocation. Experimental results on TSP and MTSP indicate that NMPA achieves more stable convergence, higher accuracy, and stronger robustness than ant colony optimization (ACO), simulated annealing, genetic algorithms (GAs), and their variants. Moreover, validation on real-world factory data in both single-robot and multi-robot task allocation scenarios confirms that NMPA delivers superior solution quality and overall performance compared with ACO, GAs, and their variants.

In this article, we propose a navigation approach based on a flocking algorithm for multiple agents that discover the environment with their sensors. The proposed method is able to move the agents in a flexible formation that preserves a pattern that minimizes a measure of energy, but is also able to avoid collision with obstacles while maintaining communication and visibility between agents. Control laws are combined with a new version of the planner called the Nearness Diagram (ND), extended to multiple agents. Two central ideas in the approach are: (1) the global leader of the formation can change, selecting as global leader one that has favorable conditions to reach the goal, (2) each agent locally follows another agent, the agent that is followed minimizes a cost related to the shortest path to the global leader in a graph. The method is intensively validated with several computer simulations, implemented in differential-drive robots. The results showed that the approach maintains communication formation and flexible enough to allow agents to line up when narrow passages appear in the environment.