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Exploring robots may fail due to environmental hazards. Thus, robots need to account for the possibility of failure to plan the best exploration paths. Optimizing expected utility enables robots to find plans that balance achievable reward with the inherent risks of exploration. Moreover, when robots rendezvous and communicate to exchange observations, they increase the probability that at least one robot is able to return with the map. Optimal exploration is NP-hard, so we apply a constraint-based approach to enable highly-engineered solution techniques. We model exploration under the possibility of robot failure and communication constraints as an integer, linear program and a generalization of the Vehicle Routing Problem. Empirically, we show that for several scenarios, this formulation produces paths within 50% of a theoretical optimum and achieves twice as much reward as a baseline greedy approach.
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Robot Team Data Collection with Anywhere Communication
Using robots to collect data is an effective way to obtain information from the environment and communicate it to a static base station. Furthermore, robots have the capability to communicate with one another, potentially decreasing the time for data to reach the base station. We present a Mixed Integer Linear Program that reasons about discrete routing choices, continuous robot paths, and their effect on the latency of the data collection task. We analyze our formulation, discuss optimization challenges inherent to the data collection problem, and propose a factored formulation that finds optimal answers more efficiently. Our work is able to find paths that reduce latency by up to 101% compared to treating all robots independently in our tested scenarios.
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- Award ID(s):
- 1823245
- PAR ID:
- 10456067
- Date Published:
- Journal Name:
- Proceedings of the International Conference on Intelligent Robots and Systems
- ISSN:
- 2153-0866
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
