Motion planning and fuzzy reasoning for autonomous navigation of a mobile robot

Abstract

This thesis aims to advance autonomous navigation for wheeled mobile robots by addressing fundamental challenges in motion planning and control within both static and dynamic environments. A hybrid pathplanning framework is developed, combining an enhanced adaptive A* algorithm for global path generation with a modified Dynamic Window Approach (DWA) for real-time local obstacle avoidance. The adaptive A* algorithm improves search efficiency through a dynamic heuristic function, path simplification using redundant jump point removal, and trajectory smoothing via (Eta-Spline and Cubic B-Spline) interpolation. For local planning, a fuzzy logic system is employed to dynamically optimize the DWA evaluation function, with a modified formulation proposed for dynamic environments. This enhancement reduces planning time, improves trajectory smoothness, and effectively mitigates the local minima problem. In terms of control, the thesis proposes a robust trajectory tracking strategy based on the integration of Dynamic Feedback Linearization (DFL) and Fuzzy Logic Control (FLC). By exploiting the system’s differential flatness property, the control law is derived in the flat output space to ensure accurate path tracking and effective obstacle avoidance behaviors. The synergy between model-based DFL and data-driven fuzzy control enhances tracking accuracy, robustness, and disturbance rejection. The proposed methods are validated through extensive simulations and real-world experiments, demonstrating significant improvements in navigation efficiency, control precision, and obstacle avoidance performance compared to conventional approaches.