Comprehensive evaluation of the intelligence levels for unmanned swarms based on the collective OODA loop and group extension cloud model
Considering the increasing complexity of application scenarios, the evaluation of the intelligence levels for unmanned swarms has attracted scholarly attention. However, the existing evaluation studies cannot suitably reflect the intelligence of unmanned swarms, and they rarely provide a specific ev...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2022-12-01
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| Series: | Connection Science |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/09540091.2022.2026293 |
| Summary: | Considering the increasing complexity of application scenarios, the evaluation of the intelligence levels for unmanned swarms has attracted scholarly attention. However, the existing evaluation studies cannot suitably reflect the intelligence of unmanned swarms, and they rarely provide a specific evaluation process. By introducing the collective intelligent behavior model for unmanned swarms based on the collective Observe–Orient–Decide–Act (OODA) loop, this study constructs a comprehensive evaluation index system that can systematically reflect the overall intelligence of unmanned swarms in complex scenarios. Considering the fuzziness and randomness in the processes of weight calculation and level evaluation, this study proposes a comprehensive evaluation method of the intelligence levels for unmanned swarms based on a group extension cloud model. This method calculates the weights of various evaluation indexes at different layers by adopting the group extension analytic hierarchy process. Moreover, it obtains the comprehensive evaluation conclusion of the intelligence levels for unmanned swarms by adopting the cloud model. Applying the proposed method, this study evaluates two specific types of unmanned aerial vehicle swarms. The results show that the proposed method can more flexibly and accurately evaluate the intelligence levels of unmanned swarms than the previous fully qualitative methods. |
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| ISSN: | 0954-0091 1360-0494 |