Global–local Path Planning and Four-wheel-steering-based Tracking Control Architecture for Obstacle Avoidance of Industrial Autonomous Mobile Robots

This paper presents a hierarchical control architecture for autonomous mobile robots (AMRs) that enhances obstacle avoidance and trajectory smoothness in industrial-like navigation tasks. A global A*-based planner generates a collision-free path on an occupancy grid map and triggers replanning when newly detected obstacles invalidate the current route; dynamic obstacles are handled by projecting their short horizon predicted occupancy as virtual walls on the grid. A timed elastic band (TEB) local planner refines the global path in the spatiotemporal domain to satisfy nonholonomic constraints, velocity and acceleration limits, and obstacle- avoidance requirements, producing a smooth reference trajectory with moderated curvature variations. For tracking, a four-wheel steering (4WS) 2-degrees of freedom (DOF) lateral dynamics model is controlled by a discrete-time linear quadratic regulator (LQR) feedback controller combined with an Ackermann-based feedforward term to proactively compensate the reference curvature and stabilize lateral and heading errors under steering constraints. Simulations in MATLAB and a high-fidelity multi-DOF vehicle dynamics simulation program compare five schemes under both static and dynamic obstacle scenarios: A* with Pure Pursuit (short/medium/long look-ahead), A* with LQR/feedforward, and the proposed A*-TEB-LQR/feedforward architecture. The proposed method successfully avoids obstacles and achieves the smallest average curvature and shortest arrival time among collision-free algorithms, while Pure Pursuit suffers from look-ahead sensitivity and A*-LQR/feedforward shows increased local curvature near obstacles.