TD3 Based Collision Free Motion Planning for Robot Navigation

TL;DR

This paper introduces the TD3-DWA algorithm, a hybrid of Deep Reinforcement Learning and traditional motion planning, to improve collision-free navigation in complex environments using sensor data.

Contribution

It presents a novel fusion of TD3 and DWA for enhanced robotic path planning, optimizing obstacle avoidance in dynamic settings.

Findings

Significantly improved navigation safety and reliability in simulations.

Enhanced efficiency in path planning around static and dynamic obstacles.

Validated performance gains over traditional methods.

Abstract

This paper addresses the challenge of collision-free motion planning in automated navigation within complex environments. Utilizing advancements in Deep Reinforcement Learning (DRL) and sensor technologies like LiDAR, we propose the TD3-DWA algorithm, an innovative fusion of the traditional Dynamic Window Approach (DWA) with the Twin Delayed Deep Deterministic Policy Gradient (TD3). This hybrid algorithm enhances the efficiency of robotic path planning by optimizing the sampling interval parameters of DWA to effectively navigate around both static and dynamic obstacles. The performance of the TD3-DWA algorithm is validated through various simulation experiments, demonstrating its potential to significantly improve the reliability and safety of autonomous navigation systems.