TY - GEN
T1 - Achieving Multitasking Robots in Multi-Robot Tasks
AU - Smith, Winston
AU - Zhang, Yu
N1 - Funding Information:
ACKNOWLEDGMENTS We thank the anonymous reviewers for their helpful comments. This research is supported in part by the NSF grant IIS-1844524, the NASA grant NNX17AD06G, and the AFOSR grant FA9550-18-1-0067.
Publisher Copyright:
© 2021 IEEE
PY - 2021
Y1 - 2021
N2 - One simplifying assumption made in the existing and well-performing multi-robot systems is that the robots are single-tasking: each robot operates on a single task at any time. While this assumption is innocent to make in situations with sufficient resources such that robots can work independently, it becomes a restriction when they must share capabilities. In this paper, we consider multitasking robots with multi-robot tasks. Given a set of tasks, each achievable by a coalition of robots, our approach allows the coalitions to overlap by exploiting task synergies based on the physical constraints required to maintain these coalitions. The key contribution is a general and flexible framework that extends the current multi-robot systems to enable multitasking. The proposed approach is inspired by the information invariant theory, which orients around the equivalence of different information requirements. We map physical constraints to information requirements in our work, thereby allowing task synergies to be identified by reasoning about the relationships between such requirements. We show that our algorithm is sound and complete. Simulation results show its effectiveness under resource-constrained situations and in handling challenging scenarios in a realistic UAV simulator.
AB - One simplifying assumption made in the existing and well-performing multi-robot systems is that the robots are single-tasking: each robot operates on a single task at any time. While this assumption is innocent to make in situations with sufficient resources such that robots can work independently, it becomes a restriction when they must share capabilities. In this paper, we consider multitasking robots with multi-robot tasks. Given a set of tasks, each achievable by a coalition of robots, our approach allows the coalitions to overlap by exploiting task synergies based on the physical constraints required to maintain these coalitions. The key contribution is a general and flexible framework that extends the current multi-robot systems to enable multitasking. The proposed approach is inspired by the information invariant theory, which orients around the equivalence of different information requirements. We map physical constraints to information requirements in our work, thereby allowing task synergies to be identified by reasoning about the relationships between such requirements. We show that our algorithm is sound and complete. Simulation results show its effectiveness under resource-constrained situations and in handling challenging scenarios in a realistic UAV simulator.
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U2 - 10.1109/ICRA48506.2021.9561474
DO - 10.1109/ICRA48506.2021.9561474
M3 - Conference contribution
AN - SCOPUS:85125466532
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 8948
EP - 8954
BT - 2021 IEEE International Conference on Robotics and Automation, ICRA 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE International Conference on Robotics and Automation, ICRA 2021
Y2 - 30 May 2021 through 5 June 2021
ER -