Mapping traversal costs in an environment and planning paths based on this map are important for autonomous navigation. We present a neurobotic navigation system that utilizes a Spiking Neural Network Wavefront Planner and E-prop learning to concurrently map and plan paths in a large and complex environment. We incorporate a novel method for mapping which, when combined with the Spiking Wavefront Planner, allows for adaptive planning by selectively considering any combination of costs. The system is tested on a mobile robot platform in an outdoor environment with obstacles and varying terrain. Results indicate that the system is capable of discerning features in the environment using three measures of cost, (1) energy expenditure by the wheels, (2) time spent in the presence of obstacles, and (3) terrain slope. In just twelve hours of online training, E-prop learns and incorporates traversal costs into the path planning maps by updating the delays in the Spiking Wavefront Planner. On simulated paths, the Spiking Wavefront Planner plans significantly shorter and lower cost paths than A* and RRT*. The spiking wavefront planner is compatible with neuromorphic hardware and could be used for applications requiring low size, weight, and power.

翻译：我们提出一种神经机器人导航系统，该系统利用脉冲神经网络波前规划器与E-prop学习算法，在大型复杂环境中同步实现地图构建与路径规划。通过引入新型地图构建方法，并结合脉冲波前规划器，该系统能够通过选择性考虑任意组合的成本指标实现自适应路径规划。该系统在包含障碍物与复杂地形的户外环境中，基于移动机器人平台进行测试。结果表明，该系统能够通过三种成本度量指标识别环境特征：(1)轮式移动的能量消耗，(2)障碍物区域停留时间，(3)地形坡度。经过仅12小时的在线训练，E-prop算法通过更新脉冲波前规划器的延迟参数，将行进成本有效学习并整合至路径规划地图中。在模拟路径测试中，脉冲波前规划器规划的路径长度与成本均显著优于A*与RRT*算法。该脉冲波前规划器兼容神经形态硬件，可适用于对尺寸、重量及功耗有严苛要求的应用场景。