Impressionist Algorithms for Autonomous Multi-Robot Systems: Flocking as a Case Study.

Robot swarms have the potential to revolutionize areas ranging from warehouse management and agriculture to underwater and space exploration. However, there remains a substantial gap between theory and robot implementation. While algorithms might assume reliable communication, perfect sensing, and instantaneous cognition, most robots have lossy or even no communication, imperfect sensing, and limited cognition speed. In our previous work on implicit vision-based coordination, we demonstrated autonomous three-dimensional behaviors underwater by removing the need for radio communication between robots. Here we explore impressionist algorithms, capable of working with even more minimal information where traditional algorithms are prone to fail. Our case study focuses on classic flocking behaviors, where a robot swarm must coordinate group motion. We demonstrate that reliable alignment, dispersion, and milling can be achieved with only infrequent and imperfect sensory impressions. In simulation studies and theoretical analyses, we investigate the effect of systematically reducing spatial and temporal fidelity of individual information on the success metrics for the group; we also demonstrate physical experiments with Blueswarm robots using simple color detection. Our results show the potential of impressionist algorithms that operate on simpler neighborhood-awareness metrics and still achieve desired global goals.