Simultaneous Path and Motion Planning Approaches for Cooperative Cable-Driven Transportation With Mobile Robots: A Comparative Study

Cooperative cable-driven transportation with mobile robots connects mobile robots to the payload with cables, rather than attaching mobile robots to the payload. A cooperative cable-driven transportation system can move over low obstacles and reduce the mutual influence of mobile robots through flexible connections. In a large-scale environment, a cooperative cable-driven transportation system could be regarded as a point and sophisticated path planning approaches are available. However, in a small-scale environment with space constraints and obstacles, a cooperative cable-driven transportation system cannot be regarded as a point and the simultaneous path and motion planning of the system is a changeling problem. To this end, this paper proposes a novel Reinforcement Learning (RL)-based planning approach for cooperative cable-driven transportation. The paper also develops sampling-based and optimization-based planning approaches based on the existing planning approaches for Cable-Driven Parallel Robots (CDPRs). The paper applies these planning approaches to cooperative cable-driven transportation scenarios in simulation and in the real world. The time consumption of these planning approaches and the quality of planning results are compared and analyzed. Based on the comparison of these planning approaches, the paper provides a guideline for the selection and development of planning approaches for cooperative cable-driven transportation with mobile robots. Note to Practitioners—This paper was motivated by the limited capability of a single mobile robot to transport a large payload and the advantages of a cooperative cable-driven transportation system in moving over low obstacles and reducing the mutual influence of mobile robots through flexible connections. This paper focuses on the simultaneous path and motion planning of a cooperative cable-driven transportation system with a given path segment in a small-scale environment. Existing studies of sampling-based, optimization-based, and Reinforcement Learning (RL)-based planning approaches for Cable-Driven Parallel Robots (CDPRs) usually avoid highlighting the weaknesses of a proposed planning approach or cannot be applied to a cooperative cable-driven transportation system. This study proposes an RL-based planning approach, for the first time, and develops sampling-based and optimization-based planning approaches for the simultaneous path and motion planning of a cooperative cable-driven transportation system. The implementation of the three planning approaches is presented. The time consumption and length of planned paths achieved by the three planning approaches are systematically compared in simulation and in the real world. With the presented implementation and comparison of three planning approaches, practitioners can select and implement a proper planning approach to a cooperative cable-driven transportation system according to their requirement of time consumption and length of planned paths. In future research, this study will continue to include additional indices, such as the smoothness of planned paths and wrench feasible workspace.