Modern manipulators are acclaimed for their precision but often struggle to operate in confined spaces. This limitation has driven the development of hyper-redundant and continuum robots. While these present unique advantages, they face challenges in, for instance, weight, mechanical complexity, modeling and costs. The Minimally Actuated Serial Robot (MASR) has been proposed as a light-weight, low-cost and simpler alternative where passive joints are actuated with a Mobile Actuator (MA) moving along the arm. Yet, Inverse Kinematics (IK) and a general motion planning algorithm for the MASR have not be addressed. In this letter, we propose the MASR-RRT* motion planning algorithm specifically developed for the unique kinematics of MASR. The main component of the algorithm is a data-based model for solving the IK problem while considering minimal traverse of the MA. The model is trained solely using the forward kinematics of the MASR and does not require real data. With the model as a local-connection mechanism, MASR-RRT* minimizes a cost function expressing the action time. In a comprehensive analysis, we show that MASR-RRT* is superior in performance to the straight-forward implementation of the standard RRT*. Experiments on a real robot in different environments with obstacles validate the proposed algorithm.

翻译：现代机械臂以其高精度著称，但在受限空间内作业时常面临困难。这一局限性推动了超冗余度机器人与连续体机器人的发展。尽管这些机器人展现出独特优势，但其在重量、机械复杂性、建模与成本等方面仍面临挑战。欠驱动串联机器人作为一种轻量化、低成本且结构更简单的替代方案被提出，其被动关节通过沿机械臂移动的移动驱动器进行驱动。然而，针对欠驱动串联机器人的逆运动学问题及通用运动规划算法尚未得到充分研究。本文提出专门针对欠驱动串联机器人独特运动学特性设计的MASR-RRT*运动规划算法。该算法的核心组件是基于数据的逆运动学求解模型，该模型在求解过程中同时考虑移动驱动器的最小行程。该模型仅使用欠驱动串联机器人的正运动学数据进行训练，无需真实实验数据。以该模型作为局部连接机制，MASR-RRT*能够最小化表征动作时间的代价函数。通过综合分析，我们证明MASR-RRT*在性能上显著优于直接采用标准RRT*算法的实施方案。在不同障碍物环境下的真实机器人实验中，该算法的有效性得到了验证。