Distributed Data Storage and Fusion for Collective Perception in Resource-Limited Mobile Robot Swarms
This work aims to improve collective perception accuracy and efficiency for resource-limited mobile robot swarms, particularly in scenarios with imperfect individual classifiers.
This paper addresses the challenge of distributed data storage and fusion for collective perception in resource-limited robot swarms. It proposes a decentralized shared data structure for efficient storage and retrieval of semantic annotations and a voting-based decentralized algorithm to reduce variance in imperfect classifications.
In this paper, we propose an approach to the distributed storage and fusion of data for collective perception in resource-limited robot swarms. We demonstrate our approach in a distributed semantic classification scenario. We consider a team of mobile robots, in which each robot runs a pre-trained classifier of known accuracy to annotate objects in the environment. We provide two main contributions: (i) a decentralized, shared data structure for efficient storage and retrieval of the semantic annotations, specifically designed for low-resource mobile robots; and (ii) a voting-based, decentralized algorithm to reduce the variance of the calculated annotations in presence of imperfect classification. We discuss theory and implementation of both contributions, and perform an extensive set of realistic simulated experiments to evaluate the performance of our approach.