Hybridizing Computational Intelligence and Computer Vision Techniques for Efficient Navigation and Marker Detection and Identification by a Swarm of Minirobotic Units

Swarm intelligence is a a branch of artificial intelligence grounded in the observation that swarms of simple individuals or agents with very limited intelligence can achieve remarkably intricate collective behaviors through decentralized low-level interactions among themselves and with environment. An illustrative manifestation of this concept is found in swarm robotics, wherein highly sophisticated robots are replaced by a swarm of simple and cost-effective micro-robots. In a prior study, the authors introduced Proteus II, a versatile and economical robotic unit tailored for swarm robotics applications. In this paper, we leverage a swarm of Proteus II units to tackle a navigation and marker detection and identification mission. Each robotic unit is tasked with traversing the environment to find the location of an individually-assigned graphical marker. To surmount this challenge, we adopt a hybrid approach combining swarm intelligence and computer vision techniques. We conduct a series of experiments encompassing both physical and virtual robotic units to evaluate the efficacy of our methodology. Our findings demonstrate the satisfactory performance of the proposed approach. In light of these results, we posit that our approach holds significant promise for advancing the field of swarm robotics. By harnessing the collective capabilities of simple robotic units, we pave the way for a multitude of future endeavors in this domain.