2021/04/13 by Yuanyang Zhu, Zhi Wang, Chunlin Chen +1
Computer Science · Mathematics · #Artificial intelligence #Computer science #Distributed Control Multi-Agent Systems #Focus (optics) #Grid #Mathematics #Mobile robot #Motion planning #Path (computing) #Process (computing) #Reduction (mathematics) #Reinforcement Learning in Robotics #Reinforcement learning #Robot #Robotic Path Planning Algorithms #Trajectory #cs.AI #cs.RO
paper · pdf · doi:10.1109/tmech.2021.3072675
published as IEEE/ASME Transactions on Mechatronics, 2021 · Accepted by IEEE/ASME Transactions on Mechatronics, 2021, DOI: 10.1109/TMECH.2021.3072675
openalex publication_date 2021/04/13 · arxiv created 2021/04/15 · arxiv updated 2021/04/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
For real-world deployments, it is critical to allow robots to navigate in complex environments autonomously. Traditional methods usually maintain an internal map of the environment, and then design several simple rules, in conjunction with a localization and planning approach, to navigate through the internal map. These approaches often involve a variety of assumptions and prior knowledge. In contrast, recent reinforcement learning (RL) methods can provide a model-free, self-learning mechanism as the robot interacts with an initially unknown environment, but are expensive to deploy in real-world scenarios due to inefficient exploration. In this article, we focus on efficient navigation with the RL technique and combine the advantages of these two kinds of methods into a rule-based RL (RuRL) algorithm for reducing the sample complexity and cost of time. First, we use the rule of wall-following to generate a closed-loop trajectory. Second, we employ a reduction rule to shrink the trajectory, which in turn effectively reduces the redundant exploration space. Besides, we give the detailed theoretical guarantee that the optimal navigation path is still in the reduced space. Third, in the reduced space, we utilize the Pledge rule to guide the exploration strategy for accelerating the RL process at the early stage. Experiments conducted on real robot navigation problems in hex-grid environments demonstrate that RuRL can achieve improved navigation performance.