Research

Coordinated Detection and Tracking of Hazards with UAVs and USVs

In collaboration with Schmale Lab, we are developing a network of UAVs for detecting and tracking hazardous agents in aquatic environments. In particular, we are devising algorithms for (1) autonomous detection and tracking of spatiotemporal plumes with UAVs; and (2) coordination to selectively deploy USVs that can sample and in-situ characterize the nature of hazards.


Assignment, Routing, and Coordination of Heterogeneous Robot Teams

Assignment

We study two sensor assignment problems for multi-target tracking with the goal of improving the observability of the underlying estimator. In the restricted version of the problem, we focus on assigning unique pairs of sensors to each target. We present a 1/3-approximation algorithm for this problem. In the general version, the sensors must form teams to track individual targets. We do not force any specific constraints on the size of each team, instead assume that the value function is monotonically increasing and is submodular. A greedy algorithm that yields a 1/2-approximation.

Routing

We study the problem of planning a tour for an energy-limited Unmanned Aerial Vehicle (UAV) to visit a set of sites in the least amount of time. We envision scenarios where the UAV can be recharged along the way either by landing on stationary recharging stations or on Unmanned Ground Vehicles (UGVs) acting as mobile recharging stations. This leads to a new variant of the Traveling Salesperson Problem (TSP) with mobile recharging stations. We present an algorithm that finds not only the order in which to visit the sites but also when and where to land on the charging stations to recharge. Our algorithm plans tours for the UGVs as well as determines best locations to place stationary charging stations.

Coordination

We present an algorithm to explore an orthogonal polygon using a team of p robots. Our algorithm is based on a single-robot polygon exploration algorithm and a tree exploration algorithm. We show that the exploration time of our algorithm is competitive (as a function of p) with respect to the offline optimal exploration algorithm. In addition to theoretical analysis, we discuss how this strategy can be adapted to real-world settings.


Bridge Inspection with UAVs

The goal of this project is to develop guidance algorithms for small unmanned aircraft to operate in proximity to large-scale civil infrastructure. The UAV flight will initially facilitate making photogrammetric measurements to enable the formulation of three-dimensional models of different portions bridges. The project intends to initiate a creation of a library of photogrammetric models can be made for different structure types, which can be used to identify potential high-risk areas and prioritize flight and inspection time accordingly.