Mkhatshwa, Sindiso and Nitschke, Geoff (2024) Body and Brain Quality-Diversity in Robot Swarms, ACM Transactions on Evolutionary Learning and Optimization, 1-28, ACM.
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Abstract
In biological societies, complex interactions between the behavior and morphology of evolving organisms and their environment have given rise to a wide range of complex and diverse social structures. Similarly, in artificial counterparts such as swarm-robotics systems, collective behaviors emerge via the interconnected dynamics of robot morphology (sensorymotor configuration), behavior (controller), and environment (task). Various studies have demonstrated morphological and behavioral diversity enables biological groups to exhibit adaptive, robust, and resilient collective behavior across changing environments. However, in artificial (swarm robotic) systems there is little research on the impact of changing environments on morphological and behavioral (body-brain) diversity in emergent collective behavior, and the benefits of such diversity. T his study uses evolutionary collective robotics as an experimental platform to investigate the impact of increasing task environment complexity (collective behavior task difficulty) on the evolution and benefits of morphological and behavioral diversity in robotic swarms. Results indicate that body-brain evolution using coupled behavior and morphology diversity maintenance yields higher behavioral and morphological diversity, which is beneficial for collective behavior task performance across task environments. Results also indicate that such behavioral and morphological diversity maintenance coupled with body-brain evolution produces neuro-morpho complexity that does not increase concomitantly with task complexity.
| Item Type: | Journal article (paginated) |
|---|---|
| Uncontrolled Keywords: | Swarm-Robotics, Controller-Morphology Adaptation, Quality-Diversity, Behavioral and Morphological Diversity Maintenance |
| Subjects: | Computing methodologies > Artificial intelligence |
| Date Deposited: | 08 Aug 2024 08:45 |
| Last Modified: | 08 Aug 2024 08:45 |
| URI: | https://pubs.cs.uct.ac.za/id/eprint/1664 |
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