Research on Robot Obstacle Avoidance and Generalization Methods Based on Fusion Policy Transfer Learning

作者：Suyu Wang, Zhenlei Xu, Peihong Qiao, Quan Yue, Ya Ke, Feng Gao · 发表于：Biomimetics · 年份：2025 · DOI：10.3390/biomimetics10080493 · 被引用次数：1 · 研究领域：Reinforcement Learning in Robotics、Robotic Path Planning Algorithms、Adaptive Dynamic Programming Control

In nature, organisms often rely on the integration of local sensory information and prior experience to flexibly adapt to complex and dynamic environments, enabling efficient path selection. This bio-inspired mechanism of perception and behavioral adjustment provides important insights for path planning in mobile robots operating under uncertainty. In recent years, the introduction of deep reinforcement learning (DRL) has empowered mobile robots to autonomously learn navigation strategies through interaction with the environment, allowing them to identify obstacle distributions and perform path planning even in unknown scenarios. To further enhance the adaptability and path planning performance of robots in complex environments, this paper develops a deep reinforcement learning framework based on the Soft Actor-Critic (SAC) algorithm. First, to address the limited adaptability of existing transfer learning methods, we propose an action-level fusion mechanism that dynamically integrates prior and current policies during inference, enabling more flexible knowledge transfer. Second, a bio-inspired radar perception optimization method is introduced, which mimics the biological mechanism of focusing on key regions while ignoring redundant information, thereby enhancing the expressiveness of sensory inputs. Finally, a reward function based on ineffective behavior recognition is designed to reduce unnecessary exploration during training. The proposed method is validated in both the ...