The goal of this project is to develop computational approaches for studying groups of agents with natural mobility and social interactions. The agents in such systems can move on their own due to complex interactions between biotic and abiotic factors, culminating in behaviors that can be studied and modeled using physical data, and ultimately controlled. The team will develop algorithms for adaptive data collection during a field experiment, post-experiment data analysis for modeling behavior, and control of the movement of the group using the learned models in field experiments and environmnet data from satellite imagery. The main motivation and application of this work is in the area of free-ranging animal ecology. Groups of animals such as herds are complex self-organized systems that are affected by many factors including age, group size, landscape topography, plant phenology, weather, and the environment.
This project combines the most recent advances in computer networking, modeling, and robot motion planning and control. The team will collect physical data for extensive periods of time in the field to define behavior models for herds of animals, using these models to develop planning and control algorithms for coordinating the location of these herds, and using the location control system to manage the stock density and optimize the use of land. This includes: (1) tracking the motion and activities of individual animals; (2) identifying natural group formation and behavior; (3) planning and controlling the location of individuals and groups; (4) managing the density of the group; and (5) using animal groups as networked information backbones. Although the models will be grounded in specific field data, the proposed methodology and resulting algorithms will be applicable to other societies of free-ranging animals and synthetic societies of agents.
This work will bring together computer science, robotics, and animal experts in a project that rests on the synergy between these domains. The work will be grounded on real data and the resulting models and algorithms will be used to define detailed plans for field experiments. The models will lead to a better understanding of animal behavior and control at the individual and group level, which has the potential to impact broadly agriculture and the environment. This research will develop methods to manipulate stocking density and turn foraging into a practical tool to remediate range ecosystems by controlling grazing patterns. This research will contribute to training of a new generation of students in computation for interaction with the physical world.
