Autonomous navigation in complex and partially observable environments remains a central challenge in robotics. Several bio-inspired models of mapping and navigation based on place cells in the mammalian hippocampus have been proposed. This paper introduces a new robust model that employs parallel layers of place fields at multiple spatial scales, a replay-based reward mechanism, and dynamic scale fusion. Simulations show that the model improves path efficiency and accelerates learning compared to single-scale baselines, highlighting the value of multiscale spatial representations for adaptive robot navigation.

翻译：在复杂且部分可观测环境中的自主导航仍然是机器人学中的核心挑战。已有若干基于哺乳动物海马体位置细胞的仿生映射与导航模型被提出。本文提出了一种新的鲁棒模型，该模型采用多空间尺度的并行位置场层、基于回放的奖励机制以及动态尺度融合。仿真结果表明，与单尺度基线模型相比，该模型提高了路径效率并加速了学习过程，凸显了多尺度空间表征对于自适应机器人导航的价值。