A Self-Guided Approach for Navigation in a Minimalistic Foraging Robotic Swarm
This addresses the challenge of decentralized navigation for robotic swarms with restricted capabilities, though it is incremental as it builds on existing pheromone-based methods.
The paper tackles the problem of swarm foraging with minimal robot capabilities by using a biologically inspired design where agents act as foragers or beacons, and it shows that the swarm self-organizes to converge to trajectories around the shortest path in an unknown environment.
We present a biologically inspired design for swarm foraging based on ant's pheromone deployment, where the swarm is assumed to have very restricted capabilities. The robots do not require global or relative position measurements and the swarm is fully decentralized and needs no infrastructure in place. Additionally, the system only requires one-hop communication over the robot network, we do not make any assumptions about the connectivity of the communication graph and the transmission of information and computation is scalable versus the number of agents. This is done by letting the agents in the swarm act as foragers or as guiding agents (beacons). We present experimental results computed for a swarm of Elisa-3 robots on a simulator, and show how the swarm self-organizes to solve a foraging problem over an unknown environment, converging to trajectories around the shortest path. At last, we discuss the limitations of such a system and propose how the foraging efficiency can be increased.