Models and Algorithms for Optimal Vision-Based Surveillance and Exploration of Complex Environments

The PI plans to develop the mathematics and corresponding algorithms to determine optimal locations to observe and map out complex unknown domains with moving obstacles. This project is motivated by the increasing number of sensor-equipped mobile robotic devices and unmanned vehicles required to perform surveillance missions. In many of these missions, efficiency is essential — maximizing the information gain with minimal measurements by the sensors (observations) reduces data transmission, power consumption and increases robustness and capacity.

Such optimization is the principal aim of the research program. This grant will support 1 graduate student per year for the 3-year duration of the grant.

The PI will develop iterative greedy algorithms that determine observation locations to optimize each step's information gain. The "gain" is formulated as an integral operator on the domain shape and is costly to compute. The PI proposes developing Deep Learning approaches that make computations feasible, particularly when the domain shapes are at best partially known.

The proposed algorithms will require generating training data by offline numerical simulations. This approach is crucial to sample high-dimensional shape space in a manner consistent with the dynamical processes dictated by the governing mathematical algorithms. This project includes three main thrusts: 1) Development of non-myopic greedy algorithms and study their properties and efficiency. 2) Development of Deep Learning models and data generation for learning the gain functions. 3) Development of mathematical understanding of U-net used the Deep Learning models used in the project.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.