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IPGDR-SAC: a lidar-driven mapless navigation algorithm using reinforcement learning
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AI
DOI：10.1007/s10586-026-06315-2.png)
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Abstract
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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.
Keywords:
Path planning
Deep reinforcement learning
SAC
Reward function
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