Reconfigurable modular snake robot locomotion via learning based hybrid motion control system architecture

Snake robots propose significant advantages especially for indeterminate, chaotic environments through their robustness and versatility against unforeseen conditions and scenarios. In addition to distinct locomotion characteristics of snake robots, their redundant structure provides also fault tolerant operation capacity. However, sophisticated and versatile locomotion characteristics and redundant body structure also bring difficulty for dynamic modelling and motion control of snake robots and, for this reason, generation of snake locomotion patterns have been an ongoing challenge. To address this point; a reconfigurable, modular snake robot is designed and modelled with fundamental electromechanical structure, joint and actuation subsystem and contact force modellings based on minimal requirements which are determined by presented mathematical analysis for snake locomotion. A hybrid motion control system architecture which is constituted with state-of-the-art reinforcement learning based algorithms and a cascaded PID controller which comprises command shaper and gain scheduling components is presented. While reinforcement learning based algorithms which indicate promising potential for generation of sophisticated behaviours are employed for generation of 2D snake gait patterns with corresponding reward function terms, locomotion capabilities are expanded to 3D space with the proposed cascaded PID architecture and possible high level planners. Various experimentations that cover comparison of different reinforcement learning algorithms, individual effects of specified reward function terms, locomotion of snakes which are composed by different number of modules, fault tolerant locomotion trainings for defective snake robots and realization of 3D operation scenarios are investigated. In this content, from a holistic perspective, future directions are drawn for potential physical realization of the electro-mechanical structure, mechanical design details for self-assembly mechanisms, further improvements of the training process and curriculum, and learning based multi-robot scenarios which cover swarms of differently configured snakes to realize collaborative tasks.