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We present an incremental scalable motion planning algorithm for finding maximally informative trajectories for decentralized mobile robots. These robots are deployed to observe an unknown spatial field, where the informativeness of observations is specified as a density function. Existing works that are typically restricted to discrete domains and synchronous planning often scale poorly depending on the size of the problem. Our goal is to design a distributed control law in continuous domains and an asynchronous communication strategy to guide a team of cooperative robots to visit the most informative locations within a limited mission duration. Our proposed Asynchronous Information Gathering with Bayesian Optimization (AsyncIGBO) algorithm extends ideas from asynchronous Bayesian Optimization (BO) to efficiently sample from a density function. It then combines them with decentralized reactive motion planning techniques to achieve efficient multi-robot information gathering activities. We provide a theoretical justification for our algorithm by deriving an asymptotic no-regret analysis with respect to a known spatial field. Our proposed algorithm is extensively validated through simulation and real-world experiment results with multiple robots.
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Refuel Scheduling for Multirobot Charging-on-Demand
In this paper, we consider the refuel scheduling problem for a team of ground robots deployed in "aislelike" environments wherein the robots are constrained to move along rows. In order to maintain a minimum service rate or throughput for the ground robots, we investigate the problem of scheduling a team of mobile charging stations deployed to replace the batteries on-board the ground robots without any interruption in their task. We propose two scheduling schemes for the mobile chargers to serve the ground robots for long-term service, and derive the parameters associated with the system required for persistent uninterrupted operation.
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- Award ID(s):
- 1816343
- PAR ID:
- 10351068
- Date Published:
- Journal Name:
- International Conference on Intelligent Robots and Systems
- Page Range / eLocation ID:
- 5825 to 5830
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/IROS51168.2021.9636815
