Adaptive Formation Control for Heterogeneous Robots With Limited Information
de Denus, Michael Andrew Rolland
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In many robotics tasks, it is advantageous for robots to assemble into formations. In many of these applications, it is useful for the robots to have differing capabilities (i.e., be heterogeneous). These differences are task specific, but the most obvious differences lie in sensing and locomotion capabilities. Groups of robots may also have only imperfect or partially-known information about one another as well. One key piece of information that robots lack is how many other robots are in the environment. This thesis describes a method for formation control that allows heterogeneous robots with limited information to dynamically assemble into formations, merge smaller formations together, and correct errors that may arise in the formation. The approach is shown to be scalable and robust against robot failure, and is evaluated in multiple simulated environments.