DRL-DCLP: A Deep Reinforcement Learning-Based Dimension-Configurable Local Planner for Robot Navigation

Wei Zhang, Shanze Wang, Mingao Tan, Zhibo Yang, Xianghui Wang, Xiaoyu Shen

Abstract: In this letter, we present a deep reinforcement learning-based dimension-configurable local planner (DRL-DCLP) for solving robot navigation problems. DRL-DCLP is the first neural-network local planner capable of handling rectangular differential-drive robots with varying dimension configurations without requiring post-fine-tuning. While DRL has shown excellent performance in enabling robots to navigate complex environments, it faces a significant limitation compared to conventional local planners: dimension-specificity. This constraint implies that a trained controller for a specific configuration cannot be generalized to robots with different physical dimensions, velocity ranges, or acceleration limits. To overcome this limitation, we introduce a dimension-configurable input representation and a novel learning curriculum for training the navigation agent. Extensive experiments demonstrate that DRL-DCLP facilitates successful navigation for robots with diverse dimensional configurations, achieving superior performance across various navigation tasks.