用生物导航细胞启发的进化算法，提升机器人动态路径规划能力
NEAT-NC: NEAT guided Navigation Cells for Robot Path Planning
- 用神经进化算法演化基于脑区模型的递归网络
- 在动态环境中路径规划成功率显著优于传统方法
- 适合需要实时适应的机器人与游戏智能体
为实现空间导航，大脑通过位置细胞、网格细胞、头朝向细胞、边界细胞和速度细胞等构建环境内部表征，并结合感官输入探索周围空间。受此生物机制启发，我们提出 NEAT-NC：一种基于神经进化增益拓扑（NEAT）的导航细胞框架。该方法将导航细胞作为输入，演化递归神经网络以模拟大脑海马体功能，在静态与动态场景中评估性能。实验表明，该算法在复杂多变环境中具备强适应性，验证了生物理论在强化学习中的实用性。结果表明，该方法适用于机器人与游戏中的实时动态路径规划。
原文摘要 · Abstract (English)
To navigate a space, the brain makes an internal representation of the environment using different cells such as place cells, grid cells, head direction cells, border cells, and speed cells. All these cells, along with sensory inputs, enable an organism to explore the space around it. Inspired by these biological principles, we developed NEATNC, a Neuro-Evolution of Augmenting Topology guided Navigation Cells. The goal of the paper is to improve NEAT algorithm performance in path planning in dynamic environments using spatial cognitive cells. This approach uses navigation cells as inputs and evolves recurrent neural networks, representing the hippocampus part of the brain. The performance of the proposed algorithm is evaluated in different static and dynamic scenarios. This study highlights NEAT's adaptability to complex and different environments, showcasing the utility of biological theories. This suggests that our approach is well-suited for real-time dynamic path planning for robotics and games.
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