Journal
IEEE ROBOTICS AND AUTOMATION LETTERS
Volume 4, Issue 4, Pages 3844-3851Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LRA.2019.2929983
Keywords
Industrial robots; intelligent and flexible manufacturing; planning; scheduling and coordination
Categories
Funding
- Boeing Research and Technology at the University of Washington, Seattle, WA, USA [BRT-1218-282]
- Boeing Advanced Research Center (BARC) at the University of Washington, Seattle, WA, USA
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Efficient utilization of cooperating robots in the assembly of aircraft structures relies on balancing the workload of the robots and ensuring collision-free scheduling. We cast this problem as that of allocating a large number of repetitive assembly tasks, such as drilling holes and installing fasteners, among multiple robots. Such task allocation is often formulated as a traveling salesman problem, which is NP-hard, implying that computing an exactly optimal solution is computationally prohibitive for real-world applications. The problem complexity is further exacerbated by intermittent robot failures necessitating real-time task reallocation. In this letter, we present an efficient method that exploits workpart geometry and problem structure to initially generate balanced and conflict-free robot schedules under nominal conditions. Subsequently, we deal with the failures by allowing the robots to first complete their nominal schedules and then employing a market-based optimizer to allocate the leftover tasks. Results show an improvement of 11.5% in schedule efficiency as compared to an optimized greedy multi-agent scheduler on a four robot system, which is especially promising for aircraft assembly processes that take many hours to complete. Moreover, the computation times are similar and small, typically hundreds of milliseconds.
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