RL-BiRRT: A Reinforcement Learning-Driven Framework for Intelligent Path Planning

Dibyendu Ghosh, Devodita Chakravarty, Ankit Gupta, Debashish Chakravarty

Abstract: Efficient path planning and smooth trajectory generation are crucial for mobile robots in complex environments. While Rapidly-exploring Random Tree (RRT) methods provide robust solutions, they often suffer from slow convergence, suboptimal paths, and extensive post-processing. We introduce RL-BiRRT, a Reinforcement Learning-enhanced Bidirectional RRT that leverages a Dueling Deep Q-Network (Dueling DQN) with an LSTM-based architecture for intelligent node generation and inherent path smoothing. Our adaptive exploration mechanism optimizes tree growth and reduces computational overhead. Experiments demonstrate that RL-BiRRT reduces iterations, nodes, and path length by 8.91×, 5.33×, and 1.84× compared to Bi-RRT, and by 9.62×, 5.63×, and 1.47× compared to RRT*, respectively. The results confirm RL-BiRRT’s efficiency in complex environments.

External IDs:dblp:conf/icar/GhoshCGC25