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

翻译：本研究旨在为平面蛇形机器人在密集障碍物环境中的避障跟踪控制开发一种资源高效的解决方案。具体而言，采用神经进化增强拓扑算法（NEAT）为蛇形步态函数生成动态步态参数，该函数通过蛇形机器人的关节角度实现，从而控制机器人沿期望的动态路径运动。NEAT是一种基于单一神经网络的进化算法，已知在输入层维度显著高于输出层维度时表现优异。对于平面蛇形机器人，输入层包含关节角度、连杆位置、头部连杆位置以及邻近障碍物位置；而输出层仅包含控制机器人速度和航向的蛇形步态频率与偏置角。通过激光雷达获取的障碍物数据、各类传感器采集的机器人数据，结合目标终点位置与时间信息，共同参数化奖励函数，并通过优势神经网络的迭代选择性传播实现奖励最大化。实现与实验结果表明，所提方法计算效率高，尤其适用于包含大量障碍物的大规模环境。该框架已通过PyBullet物理引擎仿真研究验证，其性能优于现有先进方法，并与近期CBRL方法结果相当，同时计算开销显著降低。仿真视频可访问：https://sites.google.com/view/neatsnakerobot