Vibration-Aware Trajectory Optimization for Mobile Robots in Wild Environments via Physics-Informed Neural Network

Abstract

The suspension system, through effective damping of vibrations and shocks, can enhance the stability of wheeled robots traversing challenging terrain. Because the suspension system decouples the rigid correspondence between terrain changes and robot vibrations, considering suspension modeling in trajectory planning offers the advantage of more accurate prediction of the robot’s response to terrain. This improved predictive capability facilitates the planning of safer trajectories and may reduce tracking errors in the subsequent control process. In this work, inspired by the structure of Physics-Informed Neural Network (PINN), we propose a physics-informed planning method that considers the vibrational effects of complex nonlinear suspension systems. In addition, we design a two-stage process to accelerate training. By incorporating PINN, our method can better guarantee the physical feasibility of the planned trajectories. The proposed approach has been evaluated on a real robot platform. Compared to state-of-the-art baseline methods, our proposed approach achieves a 15.38% reduction in hazardous planning for mobile robots in wild environments.