Motion Planning and Iterative Learning Control of a Modular Soft Robotic Snake - Luo, Ming; Wan, Zhenyu; Sun, Yinan; Skorina, Erik H.; Tao, Weijia; Chen, Fuchen; Gopalka, Lakshay; Yang, Hao; Onal, Cagdas D. (Frontiers in Robotics and AI, 2020)
Snake robotics is an important research topic with applications in a wide range of fields including inspection in confined spaces, search-and-rescue, and disaster response. Snake robots are well-suited to these applications because of their versatility and adaptability to unstructured and high-risk environments. However, compared to their biological counterparts, rigid snake robots have kinematic limitations that reduce their effectiveness in negotiating tight passageways. Due to their continuously deformable body, soft robotic snakes offer a solution that can address this discrepancy between traditional snake robots and biological snakes. To achieve functional autonomy, this paper combines soft mobile robot modeling, proprioceptive feedback control, and motion planning. We propose a pressure-operated soft robotic snake with a high degree of modularity, with our customized embedded flexible local curvature sensing. On this platform, we introduce the use of iterative learning control using feedback from the on-board curvature sensors to enable the snake to control its locomotion direction. We also present a motion planning and trajectory tracking algorithm using an adaptive bounding box, which allows for efficient motion planning that still takes into account the kinematic state of the soft robotic snake. We test this algorithm experimentally, and demonstrate its performance for obstacle avoidance scenarios.