6 accepted papers
Scalability is a significant challenge for robot swarms. Generally, larger groups of cooperating robots produce more inter-robot collisions, and in swarm robot foraging, larger search arenas result in larger travel costs. This paper demonstrates a scale-invariant swarm foraging algorithm that ensure…
Designing resource-collection algorithms for relatively simple robots that are effective given the noise and uncertainty of the real world is a challenge in swarm robotics. This paper describes the performance of two algorithms for collective robot foraging: the stochastic central-place foraging alg…
Central-place foraging (CPF) is a canonical task in collective robotics with applications to planetary exploration, automated mining, warehousing, and search and rescue operations. We compare the performance of three Central-Place Foraging Algorithms (CPFAs), variants of which have been shown to wor…
As robot swarms become more viable, efficient solutions to fundamental tasks such as swarm search and collection are required. We propose the distributed deterministic spiral algorithm (DDSA) which generalises a spiral search pattern to robot swarms. While being an effective search strategy in its o…
Finding and retrieving resources in unmapped environments is an important and difficult challenge for robot swarms. Central-place foraging algorithms can be tuned to produce efficient collective strategies for different resource distributions. However, efficiency decreases as swarm size scales up: l…
The complete collection of resources from a predefined search area is a challenging task for autonomous robot swarms. Because naturally-occurring resources are likely to be distributed in clusters, foraging robot swarms can identify and exploit these resource clusters to improve collection efficienc…