Towards Obstacle-Avoiding Control of Planar Snake Robots Exploring Neuro-Evolution of Augmenting Topologies

TL;DR

This paper presents a resource-efficient neuro-evolutionary approach using NEAT to generate obstacle-avoiding control strategies for planar snake robots, demonstrating superior efficiency and comparable performance to recent methods in complex environments.

Contribution

It introduces a novel NEAT-based framework for dynamic gait control of snake robots in cluttered environments, reducing computational overhead while maintaining high performance.

Findings

Outperforms existing state-of-the-art methods in obstacle avoidance

Achieves similar results to recent CBRL approaches with lower computational cost

Validated through physics engine simulations in PyBullet

Abstract

This work aims to develop a resource-efficient solution for obstacle-avoiding tracking control of a planar snake robot in a densely cluttered environment with obstacles. Particularly, Neuro-Evolution of Augmenting Topologies (NEAT) has been employed to generate dynamic gait parameters for the serpenoid gait function, which is implemented on the joint angles of the snake robot, thus controlling the robot on a desired dynamic path. NEAT is a single neural-network based evolutionary algorithm that is known to work extremely well when the input layer is of significantly higher dimension and the output layer is of a smaller size. For the planar snake robot, the input layer consists of the joint angles, link positions, head link position as well as obstacle positions in the vicinity. However, the output layer consists of only the frequency and offset angle of the serpenoid gait that control the speed and heading of the robot, respectively. Obstacle data from a LiDAR and the robot data from various sensors, along with the location of the end goal and time, are employed to parametrize a reward function that is maximized over iterations by selective propagation of superior neural networks. The implementation and experimental results showcase that the proposed approach is computationally efficient, especially for large environments with many obstacles. The proposed framework has been verified through a physics engine simulation study on PyBullet. The approach shows superior results to existing state-of-the-art methodologies and comparable results to the very recent CBRL approach with significantly lower computational overhead. The video of the simulation can be found here: https://sites.google.com/view/neatsnakerobot