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Body and brain quality-diversity in robot swarms
Various studies have shown that diverse groups perform better, solve problems more adeptly, and are more resilient. However, in evolutionary robotics, evolving group diversity is a difficult task that frequently calls for geographic isolation, a division of labor mechanism, and a careful choice of p...
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| Format: | Thesis |
| Language: | English English |
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Department of Computer Science
2026
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| _version_ | 1867613144931631105 |
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| access_status_str | Open Access |
| author | Mkhatshwa, Sindiso |
| author2 | Nitschke, Geoff |
| author_browse | Mkhatshwa, Sindiso Nitschke, Geoff |
| author_facet | Nitschke, Geoff Mkhatshwa, Sindiso |
| author_sort | Mkhatshwa, Sindiso |
| collection | Thesis |
| description | Various studies have shown that diverse groups perform better, solve problems more adeptly, and are more resilient. However, in evolutionary robotics, evolving group diversity is a difficult task that frequently calls for geographic isolation, a division of labor mechanism, and a careful choice of parameters. According to recent research, decentralized Quality Diversity (QD) algorithms can generate behavioral diversity across a swarm without requiring geographical isolation or a division of labor mechanism. Despite the fact that these findings represent an essential first step in the quest to find a mechanism to evolve behavioral diversity across a swarm in physical robot tasks, little research has been done on evolving behavior-morphology diversity across a robot swarm given cooperative tasks. To address this issue, we investigate the application of a decentralized QD algorithm (EDQD) to generate group diversity given an increasingly challenging collective behavior task in order to determine the circumstances in which it succeeds and fails. We further develop Double-Map EDQD-M, an algorithm that combines morphology characterization and behavior characterization (body-brain diversity maintenance). Results indicate that body-brain diversity maintenance yielded significantly higher behavioral and morphological diversity in evolved swarms overall, which was beneficial in the most complex task environment. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/43209 |
| institution | University of Cape Town (South Africa) |
| language | English eng |
| last_indexed | 2026-06-10T12:31:28.055Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2026 |
| publishDateRange | 2026 |
| publishDateSort | 2026 |
| publisher | Department of Computer Science |
| publisherStr | Department of Computer Science |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/43209 Body and brain quality-diversity in robot swarms Mkhatshwa, Sindiso Nitschke, Geoff Quality Diversity QD algorithm Various studies have shown that diverse groups perform better, solve problems more adeptly, and are more resilient. However, in evolutionary robotics, evolving group diversity is a difficult task that frequently calls for geographic isolation, a division of labor mechanism, and a careful choice of parameters. According to recent research, decentralized Quality Diversity (QD) algorithms can generate behavioral diversity across a swarm without requiring geographical isolation or a division of labor mechanism. Despite the fact that these findings represent an essential first step in the quest to find a mechanism to evolve behavioral diversity across a swarm in physical robot tasks, little research has been done on evolving behavior-morphology diversity across a robot swarm given cooperative tasks. To address this issue, we investigate the application of a decentralized QD algorithm (EDQD) to generate group diversity given an increasingly challenging collective behavior task in order to determine the circumstances in which it succeeds and fails. We further develop Double-Map EDQD-M, an algorithm that combines morphology characterization and behavior characterization (body-brain diversity maintenance). Results indicate that body-brain diversity maintenance yielded significantly higher behavioral and morphological diversity in evolved swarms overall, which was beneficial in the most complex task environment. 2026-05-08T12:11:19Z 2026-05-08T12:11:19Z 2023 2026-05-08T12:05:32Z Thesis / Dissertation Masters Masters http://hdl.handle.net/11427/43209 en eng application/pdf Department of Computer Science Faculty of Science University of Cape Town |
| spellingShingle | Quality Diversity QD algorithm Mkhatshwa, Sindiso Body and brain quality-diversity in robot swarms |
| thesis_degree_str | Master's |
| title | Body and brain quality-diversity in robot swarms |
| title_full | Body and brain quality-diversity in robot swarms |
| title_fullStr | Body and brain quality-diversity in robot swarms |
| title_full_unstemmed | Body and brain quality-diversity in robot swarms |
| title_short | Body and brain quality-diversity in robot swarms |
| title_sort | body and brain quality diversity in robot swarms |
| topic | Quality Diversity QD algorithm |
| url | http://hdl.handle.net/11427/43209 |
| work_keys_str_mv | AT mkhatshwasindiso bodyandbrainqualitydiversityinrobotswarms |