Optimization-based local planner for a nonholonomic autonomous mobile robot in semi-structured environments

This paper proposes an optimization-based adaptive local planner to address the aggressive motion planning problem of an autonomous mobile robot in realistic semi-structured scenarios. The goal is to enable the robot to perform autonomous navigation tasks at the limit of its speed capability to maximize the efficiency, while ensuring safety and feasibility. The proposed approach constitutes an integrated scheme that leverages real-time hierarchical collision avoidance constraints reformulation and handles the heterogeneous constraints using a unified nonlinear optimization. An aggressiveness adaption method is employed to cope with disturbances instead of relying on excessive obstacle inflation, resulting in reduced conservatism while enhancing safety. The efficacy of the proposed method is demonstrated through simulation and experimental results. Compared with previous work, the proposed approach provides significant improvements in navigation success rate without sacrificing the task efficiency.