The adaptability of soft robots makes them ideal candidates to maneuver through unstructured environments. However, locomotion challenges arise due to complexities in modeling the body mechanics, actuation, and robot-environment dynamics. These factors contribute to the gap between their potential and actual autonomous field deployment. A closed-loop path planning framework for soft robot locomotion is critical to close the real-world realization gap. This paper presents a generic path planning framework applied to TerreSoRo (Tetra-Limb Terrestrial Soft Robot) with pose feedback. It employs a gait-based, lattice trajectory planner to facilitate navigation in the presence of obstacles. The locomotion gaits are synthesized using a data-driven optimization approach that allows for learning from the environment. The trajectory planner employs a greedy breadth-first search strategy to obtain a collision-free trajectory. The synthesized trajectory is a sequence of rotate-then-translate gait pairs. The control architecture integrates high-level and low-level controllers with real-time localization (using an overhead webcam). TerreSoRo successfully navigates environments with obstacles where path re-planning is performed. To best of our knowledge, this is the first instance of real-time, closed-loop path planning of a non-pneumatic soft robot.

翻译：软体机器人的适应性使其成为在非结构化环境中机动的理想选择。然而，由于身体力学、驱动方式以及机器人-环境动态建模的复杂性，运动控制面临挑战。这些因素导致其潜在能力与实际自主部署之间存在差距。软体机器人运动的闭环路径规划框架对于弥合这一现实差距至关重要。本文提出了一种应用于TerreSoRo（四肢体地面软体机器人）并带有位姿反馈的通用路径规划框架。该框架采用基于步态的晶格轨迹规划器，以支持在有障碍物环境中的导航。运动步态通过数据驱动优化方法合成，允许从环境中学习。轨迹规划器采用贪婪广度优先搜索策略获取无碰撞轨迹，合成的轨迹由一系列旋转-平移步态对组成。控制架构将高层与低层控制器相结合，并通过顶置摄像头实现实时定位。TerreSoRo成功完成了需路径重规划的障碍环境导航。据我们所知，这是首次实现非气动软体机器人的实时闭环路径规划。