Soft robots, inspired by elephant trunks or octopus arms, offer extraordinary flexibility to bend, twist, and elongate in ways that rigid robots cannot. However, their motion planning remains a challenge, especially in cluttered environments with obstacles, due to their highly nonlinear and infinite-dimensional kinematics. Here, we present a graph-based path planning tool for an elephant-trunk-inspired soft robot designed with three artificial muscle fibers that allow for continuous deformation through contraction. Using a biomechanical model that integrates morphoelastic and active filament theories, we precompute a shape library and construct a k-nearest neighbor graph in \emph{shape space}, ensuring that each node corresponds to a valid robot shape. For the graph, we use signed distance functions to prune nodes and edges colliding with obstacles, and define multi-objective edge costs based on geometric distance and actuation effort, enabling energy-aware planning with collision avoidance. We demonstrate that our algorithm reliably avoids obstacles and generates feasible paths within milliseconds from precomputed graphs using Dijkstra's algorithm. We show that including energy costs can drastically reduce the actuation effort compared to geometry-only planning, at the expense of longer tip trajectories. Our results highlight the potential of shape-space graph search for fast and reliable path planning in the field of soft robotics, paving the way for real-time applications in surgical, industrial, and assistive settings.

翻译：受象鼻或章鱼触手启发的软体机器人，具备刚性机器人无法实现的非凡柔韧性，能够弯曲、扭转和伸长。然而，由于其高度非线性且无限维的运动学特性，其运动规划仍面临挑战，尤其是在存在障碍物的杂乱环境中。本文提出了一种基于图的路径规划工具，用于一种受象鼻启发的软体机器人，该机器人设计有三根人造肌肉纤维，可通过收缩实现连续形变。利用整合了形态弹性和主动细丝理论的生物力学模型，我们预先计算了一个形状库，并在\emph{形状空间}中构建了一个k近邻图，确保每个节点对应一个有效的机器人形状。对于该图，我们使用符号距离函数来剪除与障碍物发生碰撞的节点和边，并基于几何距离和驱动努力定义多目标边代价，从而实现具有能量意识的无碰撞规划。我们证明，我们的算法能够可靠地避开障碍物，并利用Dijkstra算法在毫秒级时间内从预计算图中生成可行路径。我们表明，与仅基于几何的规划相比，纳入能量代价可以显著降低驱动努力，代价是末端轨迹更长。我们的结果凸显了形状空间图搜索在软体机器人领域实现快速可靠路径规划的潜力，为手术、工业和辅助场景中的实时应用铺平了道路。