Deep Reinforcement Learning-Based Path Planning with Dynamic Collision Probability for Mobile Robots

Abstract

This study proposed a novel approach for mobile robots path planning and avoiding collisions by using Collision Probability (CP) along with the Soft Actor-Critic Lagrangian (SACL-L) framework. Our approach enables the mobile robot to dynamically deal with static and dynamic environments while ensuring safety and efficiency. The proposed SAC-L (CP) aims to minimize the total costs, which is the combination of both negative rewards and collision occurs. This dual focus strategy ensures trajectory planning inherently safer and providing a robust solution for complex dynamic obstacles environments. The framework’s efficiency is validated through extensive simulations on the Gazebo platform involving three increasingly difficult scenarios, demonstrating superior performance, adaptability and safety of our approach compared to traditional Deep Reinforcement Learning (DRL) methods. Our results showcase significant improvements in social and ego safety scores, contributing to the advancement of autonomous navigation in complex environments. This framework marks a step towards safer, more reliable mobile robot navigation and opens new avenues for future research in mobile robot path planning. A supplementary video further demonstrates the effectiveness of our framework.<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>.