Optimization of CDPR Path Planning using a Branch and Bound Algorithm

Cable-driven Parallel Robot (CDPR) is an old idea but its application in agriculture is relatively new and currently increasing. CDPR can be used in farming in performing different tasks including weeding, seeding, imaging, and nutrition checking. In these application time and energy are costs that is expected to be minimized. This paper presents the idea of using the Traveling Salesman Problem (TSP) for optimizing the path planning of CDPR for distance, time, and energy. The suggested technique can be applied to CDPR usage in agriculture to carry out autonomous weeding, sowing, and nutrition monitoring or in any other fields where CDPR is required to visit a set of waypoints and accomplish some tasks. In this work, we have formulated a TSP for CDPR to be used in agricultural usage, solved the TSP using a Branch and Bound algorithm to simulate trajectory planning, verified the path planning in a physical CDPR prototype, and presented a performance comparison between optimized path using our proposed method and a Greedy algorithm. The outcomes of the simulation and the experimental CDPR setup support the viability of the suggested route planning approach in carrying out respective CDPR functions.