Classical snake robot control leverages mimicking snake-like gaits tuned for specific environments. However, to operate adaptively in unstructured environments, gait generation must be dynamically scheduled. In this work, we present a four-layer hierarchical control scheme to enable the snake robot to navigate freely in large-scale environments. The proposed model decomposes navigation into global planning, local planning, gait generation, and gait tracking. Using reinforcement learning (RL) and a central pattern generator (CPG), our method learns to navigate in complex mazes within hours and can be directly deployed to arbitrary new environments in a zero-shot fashion. We use the high-fidelity model of Northeastern's slithering robot COBRA to test the effectiveness of the proposed hierarchical control approach.

翻译：传统蛇形机器人控制依赖于针对特定环境模仿蛇类步态。然而，为了在非结构化环境中自适应运行，步态生成必须动态调度。本文提出一种四层分级控制方案，使蛇形机器人在大规模环境中实现自由导航。该模型将导航分解为全局规划、局部规划、步态生成与步态跟踪四个层级。通过强化学习与中枢模式发生器结合，该方法可在数小时内学会在复杂迷宫中导航，并能够零样本直接部署至任意新环境。我们采用东北大学滑行机器人COBRA的高保真模型验证了所提分级控制方法的有效性。