We wish to develop distributed algorithms for networked teams of robots that self-organize in response to the sensed environment. Such networks promise the ability to collect information over distributed, large-scale domains with minimum infrastructure maintenance. This technology will enable scientific studies on geological and ecological scales previously beyond reach, and provide tools for a host of security and surveillance applications.
Thus far we have focused on the task of controlling the robots so that their configuration optimizes the sampling of a sensory function. We consider a group of robots that is dispatched over a bounded environment of interest. The group's task is to sample a sensory function over the environment. The sensory function is an unknown continuous scalar field that can be measured locally by the robots, such as light intensity, temperature, sound intensity, or chemical concentration. We have developed a decentralized control solution that can accomplish this task. Using sensory measurements and neighbor positions, the network self-organizes by positioning individual robots to optimize the measurement of the sensory function. This enables the network to record observations about the sensory environment with varying resolution, so that areas with larger sensory signals receive higher-density data observations than areas that are quiet. Our work builds on several important results in this area, notably.