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

Abstract

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.