Large robotic swarms may be used to carry out tasks such as space exploration, mining, search & rescue operations and more. To enable their use in these fields, the individual robots within a swarm will need to be autonomic, capable of making their own decisions and adjusting their behaviour without relying on regular human intervention. This paper demonstrates the potential for autonomic self-adaptation within a swarm of foraging robots by investigating the performance of different cooperation strategies in different scenarios. The results show that the performances of the strategies are affected by operational conditions that can change over the course of a mission, and that the autonomic capability to self-adapt would prove beneficial. Additionally, the time-stepped simulation used here is compared to the performance of a previous approach using real-time simulation, with a view to identifying which approach is more suitable for embedding within a robot as a means of aiding that autonomic process through simulating potential options. The time-stepped simulation is found to be faster and more efficient, and therefore more suited to embedding.
|Number of pages||8|
|Publication status||Published (in print/issue) - 25 Oct 2020|
|Event||The Twelfth International Conference on Adaptive and Self-Adaptive Systems and Applications - Nice, France|
Duration: 25 Oct 2020 → 29 Oct 2020
Conference number: 12th
|Conference||The Twelfth International Conference on Adaptive and Self-Adaptive Systems and Applications|
|Abbreviated title||ADAPTIVE 2020|
|Period||25/10/20 → 29/10/20|
- Swarm Robotics
- Autonomic Computing
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Best Paper Award Adaptive 2020
McGuigan, Liam (Recipient), Saunders, Catherine (Recipient), Sterritt, Roy (Recipient) & Wilkie, George (Recipient), 29 Oct 2020