This project develops the theory and algorithms for autonomous navigation of mobile sensing platforms, such as unmanned ground, aerial, and underwater vehicles, so that the collected information is maximized while constraints on movement capabilities and energy expenditure are accommodated. This work facilitates the use of autonomous robots in environmental monitoring, search and rescue, surveillance and security, among other applications of societal importance. The algorithms are evaluated in field trials using unique gliding robotic fish. The project provides training for both graduate and undergraduate students, including those from underrepresented groups. Through showcasing at the Museum of Science and Industry in Chicago and offering of an open-source robotic fish education kit, the project promotes the interest of K-12 students and the general public in science and engineering. The project further facilitates transfer of software and hardware for robotic sensing to the market.
The goal of this project is to bridge the gap between the theory and practice in information-driven mobile sensing and to develop a principled theoretic and algorithmic framework for autonomous exploration in uncertain, specifically underwater, environments. The approach exploits the concept of ergodic exploration, where the underlying optimization problem is solved using methods from nonlinear optimal control. The research consists of: 1) Establishing a rigorous theoretical framework for ergodic exploration for guaranteeing solution well-posedness and stability, and developing synthesis methods for real-time control; 2) exploring active probing of flow conditions via auxiliary measurement of tracer agents to mitigate environmental uncertainty; 3) investigating collaborative exploration schemes that strike balance among performance, complexity, and robustness; and 4) conducting field experiments to validate the framework using a group of gliding robotic fish to monitor harmful algal blooms and localize sources of chemical spills.
