A Rapid Adapting and Continual Learning Spiking Neural Network Path Planning Algorithm for Mobile Robots

TL;DR

This paper introduces a neurobotic navigation system using a Spiking Neural Network Wavefront Planner and E-prop learning, enabling adaptive, continual path planning in complex environments with minimal training time.

Contribution

It presents a novel neuro-inspired mapping and planning algorithm that adapts in real-time and integrates traversal costs, compatible with neuromorphic hardware.

Findings

System effectively maps environment costs using three measures.

Learns traversal costs in 12 hours of online training.

Plans shorter, lower-cost paths than A* and RRT* in simulations.

Abstract

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.