Journal
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
Volume 19, Issue 4, Pages 2750-2761Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TASE.2021.3111888
Keywords
Robots; Robot kinematics; Task analysis; Uncertainty; Planning; Optimization; Large-scale systems; Artificial intelligence; autonomous robot network; greedy solution; integrated task assignment and path planning; optimization of large-scale systems
Categories
Funding
- Natural Science Foundation of China [62073222, U1913204]
- Shanghai Municipal Education Commission
- Shanghai Education Development Foundation [19SG08]
- Shenzhen Science and Technology Program [JSGG20201103094400002]
- Science and Technology Commission of Shanghai Municipality [21511101900]
- NVIDIA Corporation
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This article addresses the integrated task assignment, path planning, and coordination problem for large-scale autonomous robot networks, introducing a novel generalized conflict graph and an integrated optimization problem. The superiority of this approach is demonstrated through comprehensive comparisons with existing state-of-the-art methods.
Artificial intelligence-enhanced autonomous unmanned systems, such as large-scale autonomous robot networks, are widely used in logistic and industrial applications. In this article, we address the integrated task assignment, path planning, and coordination problem applied for large-scale robot networks with the existence of uncertainties. In particular, a novel generalized conflict graph is designed which encodes the traveling time cost of the subsequent path planning result of each task-robot assignment and also includes the predicted path conflicts of each two assignments. An integrated optimization problem which aims to minimize the total traveling cost and potential path conflicts simultaneously is first formulated and then transformed into a linear programming instance to obtain the optimal solution. In particular, to satisfy the real-time requirement in large-scale systems, a greedy solution is presented which has the near-optimal performance but can decrease the computational complexity by orders of magnitude. The optimality, scalability, robustness, and efficiency of our approach are demonstrated by comprehensive comparisons with existing state-of-the-art approaches.
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