AI Bibliography
AI Bibliography |
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Lee, C. Y. H., Best, G., & Hollinger, G. A. 2021, Stochastic assignment for deploying multiple marsupial robots. Paper presented at 2021 International Symposium on Multi-Robot and Multi-Agent Systems (MRS). |
| Resource type: Proceedings Article BibTeX citation key: Lee2021 View all bibliographic details  |
Categories: Artificial Intelligence, Biological Science, Cognitive Science, Complexity Science, Computer Science, Data Sciences, Engineering, General, Military Science Subcategories: Autonomous systems, Big data, Deep learning, Drones, Edge AI, Internet of things, Machine learning, Military research, Psychology of human-AI interaction, Robotics Creators: Best, Hollinger, Lee Publisher: Collection: 2021 International Symposium on Multi-Robot and Multi-Agent Systems (MRS) |
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| Abstract |
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Marsupial robot teams consist of carrier robots that transport and deploy multiple passenger robots, suchas a team of ground robots that carry and deploy multiple aerial robots, to rapidly explore complex environments. We specifically address the problem of planning the deployment times and locations of the carrier robots to best meet the objectives of a mission while reasoning over uncertain future observations and rewards. While prior work proposed optimal, polynomial-time solutions to single-carrier robot systems, the multiple-carrier robot deployment problem is fundamentally harder as it requires addressing conflicts and dependencies between deployments of multiple passenger robots. We propose a centralized heuristic search algorithm for the multiple-carrier robot deployment problem that combines Monte Carlo Tree Search with a dynamic programming-based solution to the Sequential Stochastic Assignment Problem as a rollout action-selection policy. Our results with both procedurally-generated data and data drawn from the DARPA Subterranean Challenge Urban Circuit show the viability of our approach and substantial exploration performance improvements over alternative algorithms.
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