Efficient Online Planning and Robust Optimal Control for Nonholonomic Mobile Robot in Unstructured Environments

In complex environments where occupied and unknown areas exceed the free space, it is essential for robots to utilize efficient methods for environmental perception, trajectory planning, and trajectory tracking. This paper introduces the jump point search (JPS) algorithm as a global planning approach and integrates the complete trajectory and safe trajectory using convex decomposition for local planning purposes. We specifically formulate the planning process as a jerk optimization problem to reduce robot vibrations and improve stability. To address trajectory tracking challenges, we propose an innovative robust control Lyapunov function method. This method efficiently manages disturbances in mobile robot motion, enhancing stability. It considers input constraints such as angular and linear velocity limits, along with optimization metrics like minimal input effort. We utilize a proximal augmented Lagrangian method to solve the optimization problem related to trajectory planning and the robust control Lyapunov function. Through experiments involving different friction forces and torques, we validate the effectiveness of our proposed robust control Lyapunov function controller in managing unknown disturbances. This demonstrates its superior adaptability and robustness compared to conventional model control.