Cooperative behavior of a heterogeneous robot team for planetary exploration using deep reinforcement learning
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Bioinspired techniques enhance adaptability in swarms and refine precision in manipulators, while supporting uptime in agriculture.
“Bioinspired techniques, detailed in Section Key Technical Methods in Hybrid FTC, enhance adaptability in swarms [6] and refine precision in manipulators [18], while supporting uptime in agriculture [8].”
From Hybrid Fault-Tolerant Control in Cooperative Robotics: Advances in Resilience and Scalability (2025) · cites this paper as [6]
There is a tension between theoretical potential and practical deployment, particularly in resource-intensive settings like space exploration and high-stakes healthcare.
“These challenges highlight a tension between theoretical potential and practical deployment, particularly in resourceintensive settings like space exploration [6] and high-stakes healthcare [13].”
From Hybrid Fault-Tolerant Control in Cooperative Robotics: Advances in Resilience and Scalability (2025) · cites this paper as [6]
Cites
Proximal Policy Optimization Algorithms
Schulman, J., Wolski, F., Dhariwal, P., Radford, A., & Klimov, O. (2017) · arXiv · doi:10.48550/arxiv.1707.06347 · Cited by 10,625 · see citing works
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Hybrid Fault-Tolerant Control in Cooperative Robotics: Advances in Resilience and Scalability
Urrea, C. (2025) · Actuators · doi:10.3390/act14040177 · Cited by 19 · see citing works
“Bioinspired techniques, detailed in Section Key Technical Methods in Hybrid FTC, enhance adaptability in swarms [6] and refine precision in manipulators [18], while supporting uptime in agriculture [8].”
Influential
- Authors
- Barth, A., & Ma, O.
- Affiliations
- University of Cincinnati (US)
- Corresponding author
- Barth, Andrew
- ORCID
- Venue
- Acta Astronautica
- Publisher
- Elsevier BV
- Published
- 2024-01
- Volume
- 214
- Pages
- 689-700
- ISSN
- 0094-5765 (ISSN-L)
- OpenAlex
- W4388752710
- Cited by
- 15
- References
- 1
- Citations by year
- 2026: 8 · 2025: 4 · 2024: 3
- FWCI
- 1.33
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Acta Astronautica (acceptedVersion)
- Funders
- University of Cincinnati
- Air Force Office of Scientific Research (FA9550-23-1-0440, 9550-23-1-0440)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Physical Sciences › Engineering › Mechanical Engineering › Modular Robots and Swarm Intelligence
- Physical Sciences › Computer Science › Computer Networks and Communications › Distributed Control Multi-Agent Systems
- Keywords
- heterogeneous robot teams, multi-robot cooperation, planetary surface exploration, deep reinforcement learning, reward function design, decentralized control, landing craft, rover
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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