Census-Based Population Autonomy for Marine Robots: Theory and Experiments

Abstract

Collaborating groups of robots show promise due in their ability to complete missions more efficiently and with improved robustness, attributes that are particularly useful for systems operating in marine environments. A key issue is how to model, analyze, and design these multi-robot systems to realize the full benefits of collaboration even with limited communication, a challenging task since the domain of multi-robot autonomy encompasses both collective and individual behaviors. This thesis presents a layered model of multi-robot autonomy that uses the principle of census, or a weighted count of the inputs from neighbors, for collective decision-making coupled with multi-objective behavior optimization for individual decision-making. The census component is expressed as a nonlinear opinion dynamics model and the multi-objective behavior optimization is accomplished using interval programming. This model can be reduced to recover foundational algorithms in distributed optimization and control, while the full model enables new types of collective behaviors that are useful in real-world scenarios. To illustrate these points, a new method for distributed optimization of subgroup allocation is introduced where robots use a gradient descent algorithm to minimize portions of the cost functions that are locally known, while being influenced by the opinion states from neighbors to account for the unobservable costs. With this method the group can collectively use the information contained in the Hessian matrix of the total global cost. In addition, the critical issue of controlling subgroup size to minimize a collective cost signal is addressed, an initial step toward establishing a general definition of controllability of the nonlinear opinion dynamics model. The utility of this model is experimentally validated in three categorically different experiments with fleets of autonomous surface vehicles: an adaptive sampling scenario, a high value unit protection scenario, and a competitive game of capture the flag.