IPGDR-SAC: a Lidar-Driven Mapless Navigation Algorithm Using Reinforcement Learning

Enhancing the service capabilities of robots relies heavily on their autonomous navigation performance and effective obstacle avoidance during motion. This study addresses key limitations of current deep reinforcement learning-based path planning methods, such as low training efficiency, suboptimal trajectory generation, and poor performance in complex trap scenarios. To address these challenges, we propose GDR-SAC (Genetic and Dynamic Reward-based Soft Actor-Critic), a novel approach that integrates a dedicated Lidar data feature extraction module for enhanced environmental perception, a customized SAC network architecture, a dynamic reward function tailored for continuous action spaces, and an innovative sampling strategy enhanced by genetic algorithms. Furthermore, we introduce IPGDR-SAC, an extension of GDR-SAC that incorporates a Point of Interest (PoI) exploration module to effectively navigate complex trap scenarios. Comprehensive training and evaluation across diverse environments, including both Gazebo simulation platforms and real-world settings demonstrate the effectiveness of the proposed approach. Experimental results show that the GDR-SAC algorithm significantly improves training efficiency and convergence speed, while generating smoother, shorter, and more efficient collision-free trajectories. Moreover, IPGDR-SAC exhibits superior autonomous navigation performance in intricate environments by leveraging Lidar data to successfully escape from entrapment situations and handle challenging navigation tasks.