A Brain-Inspired Harmonized Learning with Concurrent Arbitration for Enhancing Motion Planning in Fuzzy Environments

Motion planning, considered a fuzzy sequential decision-making problem, encounters significant challenges due to inherent environmental uncertainty. Traditional planning methods that rely on single strategies often struggle in complex scenarios. While fuzzy systems excel at handling uncertainty, high-dimensional continuous spaces require a large number of fuzzy rules, which significantly increases computational complexity. In contrast, humans leverage limited and fuzzy information to address various decision-making scenarios flexibly and efficiently. The concurrent reasoning mechanism in the prefrontal cortex plays a crucial role during this process. Consequently, the brain-inspired model and the concept of multi-fuzzy rules offer a novel perspective for addressing motion planning challenges. Motivated by this, this paper proposes a brain-inspired motion planning method called Harmonized Learning with Concurrent Arbitration (HLCA). Specifically, inspired by the concurrent inference model, a concurrent arbitration module is employed in the planning process to effectively manage the boundary between exploration and exploitation. Furthermore, inspired by the multi-strategy processing mechanism, HLCA introduces multi-strategy harmonized learning by referring to the concept of multiple fuzzy rules, allowing the dynamic selection of strategies through a reliability function to enable self-improving learning. Experimental results demonstrate that HLCA outperforms state-of-the-art benchmarks, highlighting its potential to enhance the planning performance of robots by learning from the human brain.