Optimizing usage of limited resources while effectively exploring an area is vital in scenarios where sensing is expensive, has adverse effects, or is exhaustive. We approach this problem by reformulating ergodic search to optimize for when and where to take sensing measurements.

In many search based problems, a robot must take into account multiple factors such as scientific information gain, risk, and energy, and update its belief about these dynamic objectives as they evolve over time. We present an algorithm called Dynamic Multi-Objective Ergodic Search (D-MO-ES) that efficiently plans an ergodic trajectory on multiple changing objectives.

An improved understanding of the environment in which a robot would have to operate to be able to search the interior of a rubble pile would help roboticists develop better suited robotic platforms and control strategies. To this end, this work offers an approach to characterize and visualize the interior of a rubble pile and conduct a preliminary analysis of the occurrence of voids using data collected by responders at the 2021 Champlain Towers South Condominiums collapse in Surfside, FL.

This work introduces a new method to extend ergodic coverage to teams of heterogeneous agents with varied sensing and motion capabilities. It develops a multi-agent heterogeneous search approach that leverages the sensing and motion capabilities of different agents to improve search performance

This work explores methods to allow agents to learn where their strengths (as determined by their sensing and motion capabilities) lie, and allocate search subtasks accordingly. Specifically, we use deep reinforcement learning techniques to automatically identify and leverage synergies between agents with different sensor and motion models, in order to optimize for coverage of the region.