Real-Time Multi-Robot Motion Planning using Decomposed Sampling-Based Methods

Sammanfattning

This project proposes an adaptation of the dRRT* algorithm, a samplingbased multi-robot planner, for real-time industrial automation scenarios. The main objectives include optimizing computationally expensive sections of the algorithm, solving partially specified multi-robot problems, and evaluating the performance of the resulting method in various industry-like scenarios. The proposed algorithm, called Fast-dRRT*, aims to achieve highquality collision-free paths within strict time constraints. To accomplish this, the project introduces modifications to the dRRT* algorithm, such as the utilization of pre-computed swept volumes for efficient collision detection. The performance of four multi-robot planners, namely dRRT, ao-dRRT, dRRT*, and Fast-dRRT*, is evaluated through experiments on toy scenarios and industrial use cases. The results show that the proposed Fast-dRRT* algorithm outperforms the other planners in terms of finding solutions within the given time limits. It demonstrates improved efficiency, speed, and resilience to increased search spaces and the number of robots. The algorithm’s performance is further evaluated in real-world scenarios, including automotive assembly lines and warehouse automation, where it consistently outperforms dRRT* in terms of search speed and total planning time. Additionally, the algorithm successfully handles partially specified multi-robot problems, optimizing the overall movements’ cost. The study concludes that Fast-dRRT* is a promising option for real-time planning in industrial automation, offering reduced computation time and feasible solutions to complex multi-robot motion planning problems.