In this work we propose a novel fully distributed approach to endow robots in a swarm with awareness of their relative position with respect to the rest of the swarm. Such spatial awareness can be used to support spatially differentiated task allocation or for pattern formation. In particular, we aim to partition the robots in the swarm in two (or more) distinct and spatially segregated groups. The distributed approach we propose only relies on local wireless communications and is based on a combination of distributed consensus and load balancing. We propose two metrics to measure the effectiveness of the obtained partitioning and we test the performance and the scalability of our algorithm in extensive simulation experiments. We also validate it in a small set of experiments with real robots.
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The purpose of the demonstrator is to present a novel system for gesture-based interaction between humans and a swarm of mobile robots. The human interacts with the swarm by showing hand gestures using an orange glove. Following initial hand glove detection, the robots move to adapt their positions and viewpoints. The purpose is to improve individual sensing performance and maximize the gesture information mutually gathered by the swarm as a whole. Using multi-hop message relaying, robots spread their opinions and the associated confidence about the issued hand gesture throughout the swarm. To let the robots in the swarm integrate and weight the different opinions, we developed a distributed consensus protocol. When a robot has gathered enough evidence, it takes a decision for the hand gesture, and sends it into the swarm. Different decisions compete with each other. The one assessed with the highest confidence eventually wins. When consensus is reached about the hand gesture, the swarm acts accordingly, for example by moving to a location, or splitting into groups. The working of the system is shown and explained in the video accessible at the following address:http://www.idsia.ch/ gianni/SwarmRobotics/aamasdemo.zip.