MEAM Ph.D. Thesis Defense: “Macroscopic Ensemble Methods for Multi Robot Task Assignment in Dynamic Environments”

With finite resources to complete tasks like monitoring, coverage, and search, the challenge lies in identifying and performing tasks that can change both in frequency and location. One potential solution is teams of robots equipped with the necessary capabilities to complete the desired tasks. However, robot teams require methods that effectively assign robots to tasks, which is also known as the Multi Robot Task Allocation (MRTA) problem. Existing MRTA approaches assign each individual robot to a task. This works well if the team is small (less than 20 robots) and if the individual task specification does not change (monitoring a static environment). Unfortunately, these solutions require solving a combinatorial optimization problem which has poor computational scalability as the team and number of tasks increase, and is further exacerbated by changing task or environment conditions. An alternative perspective is to look at how biologists study animal resource selection. Instead of posing the problem of assigning animals to resources, animals randomly select a resource weighted by the perceived resource value resulting in beneficial population configurations. Taking inspiration from biology, we model team-wide objectives using macroscopic ensemble allocation. These methods allow robots to select stationary task regions, are known to easily control large robot teams (more than 50 robots), and can even describe robot team heterogeneity. Nevertheless, macroscopic ensemble methods require extensions to effectively solve the MRTA problem for tasks where conditions change, e.g., monitoring spatiotemporal processes. The main contributions of this dissertation include online adaptive macroscopic allocation, distributed adaptive macroscopic allocation, and macroscopic allocation via robot-robot collaboration. Our results show robot teams monitoring spatial temporal environments using simulation and robot experiments.