Ant Colony algorithms are a set of biologically inspired algorithms used commonly to solve distributed optimization problems. Convergence has been proven in the context of optimization processes, but these proofs are not applicable in the framework of robotic control. In order to use Ant Colony algorithms to control robotic swarms, we present in this work more general results that prove asymptotic convergence of a multi-agent Ant Colony swarm moving in a weighted graph.
|Title of host publication||Proceedings of the 23rd International Conference on Hybrid Systems (HSCC 2020)|
|Subtitle of host publication||Computation and Control, part of CPS-IoT Week|
|Place of Publication||New York, NY, USA|
|Publisher||Association for Computing Machinery (ACM)|
|Number of pages||11|
|Publication status||Published - 2020|
|Event||HSCC '20: 23rd ACM International Conference on Hybrid Systems: Computation and Control - Sydney, Australia|
Duration: 21 Apr 2020 → 24 Apr 2020
|Conference||HSCC '20: 23rd ACM International Conference on Hybrid Systems: Computation and Control|
|Period||21/04/20 → 24/04/20|
Bibliographical noteGreen Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
- ant colony
- random walk
- swarm robotics