The research objective of this paper is to explore the path planning problem of Unmanned Ground Vehicles (UGV) in unstructured environments. Firstly, we establish a kinematic model based on the UGV, and after reasonable assumptions, only consider the longitudinal motion of the UGV. Next, provide the overall control architecture of the vehicle mainly including the information of an unstructured map and a path planner. Then, based on the rasterization of the 2D map, the A-star algorithm is applied to obtain the path, and have been improved by our team. Finally, in the robot emulation environment, the effectiveness and stable of our designed approach have been demonstrated through simulation experiments.

Our paper studies consensus problem for multi-agent systems (MASs) by switching topologies based on passivity theory. Especially, novel admissible edge-dependent (AED) switching approach is introduced for dealing with the switching topologies containing consensusable and unconsensusable topologies. By virtue of model transformation, sufficient consensus criteria are derived for achieving guaranteed passivity performance, and desired controllers for MASs are further designed. A numerical example is finally presented for demonstrating usefulness of our established approach.

Multi-robot formations can extend sensing coverage and improve operational resilience, but their practical value depends on coordinated navigation through cluttered environments. Under mapless local sensing, existing methods face conflicting demands for obstacle avoidance, collective progress and formation recovery. This study presents Coupled ESRM–MFAM, a deterministic bio-inspired controller for six differential-drive robots. An Elastic Skin Reflex Model (ESRM) provides local obstacle-surface response, while a Muscle Fibre Actuation Model (MFAM) generates collective driving forces. Their direct combination, together with teammate-safety handling, integrates environmental response and formation coordination within a local control loop. Across 800 geometry-certified environments, the controller achieved a task completion rate of 75.00% within 120 s, compared with 56.50% for VFH+, the strongest tested baseline adaptation. This advantage remained significant after Holm correction and was reproduced on a separately generated 400-environment evaluation set. Component ablations supported the contributions of environmental response, collective actuation and teammate safety to timely completion. ROS2/Gazebo measurements further showed that six-robot controller computation remained within the budget required for 10 Hz control. These results support coupled environmental response and collective actuation as a practical approach to timely formation navigation under the tested local-sensing conditions.

The overall swing dynamics of rigid slender payloads lifted in a vertical orientation deviate significantly from the ideal point-mass pendulum model, leading to severe performance degradation of feedforward control strategies designed based on this simplified model. This paper focuses on the bridge crane system and establishes a double-pendulum dynamic model that accounts for the payload’s mass distribution effect. To compensate for the theoretical error of the linearized model, a data-driven payload swing frequency correction strategy is proposed. Based on this corrected model, a dual-mode Zero Vibration Derivative (Corrected-Dual-ZVD) input shaping feedforward controller is designed. Simulations under eight typical operating conditions were conducted using the Matlab/Simulink control system simulation software. The results show that compared to the controller designed based on the traditional single-pendulum model, the proposed Corrected-Dual-ZVD controller, based on the corrected double-pendulum model, can significantly reduce the maximum residual swing angle of the payload. The average swing angle suppression rate reaches 68.9% across seven valid operating conditions, and it can reach 98.9% under the extreme condition of high speed and short rope length. When model parameters are subjected to ±10% disturbances, the proposed method demonstrates good robustness.