A Gradient Descent-Based Backend Feedback Adaptive Motion Planning Algorithm for Autonomous Mobile Robots

This article introduces an innovative motion planning algorithm for autonomous mobile robots, specifically focusing on quadrotor unmanned aerial vehicles (UAVs), utilizing a gradient descent-enhanced frontend and backend architecture. A trajectory planning algorithm is proposed for the front-end part. It relies on backend optimization feedback and memorized jump points. The algorithm builds on the jump point search (JPS) algorithm and introduces an obstacle table and jump point table. A new heuristic function is proposed, which emphasizes the weight of obstacle proportion in order to avoid getting stuck in local optimal paths. In the backend trajectory optimization part, a backend space-time trajectory optimization method based on gradient descent is proposed, and an optimization objective function is designed to ensure the smoothness and safety of the UAV trajectory. The simulation results show that the algorithm proposed in this article has significant advantages for improving real-time performance and environmental adaptability compared with the method based on ESDF and the EGO-planner. The actual flight experiments show that the proposed algorithm can avoid UAVs getting stuck in local optima during path planning. Notably, the proposed methodology also holds promise for application in path planning for other autonomous robots.