{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T17:50:12Z","timestamp":1764784212695,"version":"3.38.0"},"reference-count":77,"publisher":"SAGE Publications","issue":"12-13","license":[{"start":{"date-parts":[[2019,3,27]],"date-time":"2019-03-27T00:00:00Z","timestamp":1553644800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["The International Journal of Robotics Research"],"published-print":{"date-parts":[[2019,10]]},"abstract":"<jats:p> This paper defines the research area of Diversity-enhanced Autonomy in Robot Teams (DART), a novel paradigm for the creation and design of policies for multi-robot coordination. Although current approaches to multi-robot coordination have been successful in structured, well-understood environments, they have not been successful in unstructured, uncertain environments, such as disaster response. Although robot hardware has advanced significantly in the past decade, the way we solve multi-robot problems has not. Even with significant advances in the field of multi-robot systems, the same problem-solving paradigm has remained: assumptions are made to simplify the problem, and a solution is optimized for those assumptions and deployed to the entire team. This results in brittle solutions that prove incapable if the original assumptions are invalidated. This paper introduces a new multi-robot problem-solving paradigm which uses a diverse set of control policies that work together synergistically within the same team of robots. Such an approach will make multi-robot systems more robust in unstructured and uncertain environments, such as in disaster response, environmental monitoring, and military applications, and allow multi-robot systems to extend beyond the highly structured and highly controlled environments where they are successful today. <\/jats:p>","DOI":"10.1177\/0278364919839137","type":"journal-article","created":{"date-parts":[[2019,3,27]],"date-time":"2019-03-27T21:09:21Z","timestamp":1553720961000},"page":"1329-1337","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":12,"title":["DART: Diversity-enhanced Autonomy in Robot Teams"],"prefix":"10.1177","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8394-6912","authenticated-orcid":false,"given":"Nora","family":"Ayanian","sequence":"first","affiliation":[{"name":"University of Southern California, USA"}]}],"member":"179","published-online":{"date-parts":[[2019,3,27]]},"reference":[{"key":"bibr1-0278364919839137","first-page":"3389","volume-title":"IEEE\/RSJ International Conference on Intelligent Robots and Systems","author":"Agha-mohammadi A","year":"2014"},{"key":"bibr2-0278364919839137","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2015.7139350"},{"key":"bibr3-0278364919839137","first-page":"1273","volume-title":"International Conference on Autonomous Agents and Multi-agent Systems","author":"Amato C","year":"2014"},{"key":"bibr4-0278364919839137","doi-asserted-by":"publisher","DOI":"10.1287\/orsc.3.3.321"},{"volume-title":"AAAI Workshop on Multiagent Learning","year":"1997","author":"Balch T","key":"bibr5-0278364919839137"},{"key":"bibr6-0278364919839137","doi-asserted-by":"publisher","DOI":"10.1023\/A:1008973424594"},{"key":"bibr7-0278364919839137","unstructured":"Barret B (2016) Disney\u2019s latest attraction? 300 drones flying in formation. 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