In this work, we address the deployment problem for a team of mobile guards that tries to maintain a line-ofsight with an unpredictable mobile intruder. First, we present a computationally efficient strategy for generating a set of points, called kernel points, that covers the entire polygon. We then introduce a polygon partitioning technique based on the location of the kernel points. Next, we propose control laws for a free guard to track an intruder in general polygonal environments based on the analysis of a pursuit-evasion game around a single corner [1]. Finally, we present several variations of the proposed control laws that include capture and search, and illustrate the improvement in the overall visual footprint of the team of mobile guards based on extensive simulations.

In this work, we address a visbility-based target tracking problem in a polygonal environment in which a group of mobile observers try to maintain a line-of-sight with a mobile intruder. We build a bridge between data mining and visibility-based tracking using a novel tiling scheme for the polygon. First, we propose a tracking strategy for a team of guards located on the tiles to dynamically track an intruder when complete coverage of the polygon cannot be ensured. Next, we propose a novel variant of the Voronoi Diagram to construct navigation strategies for a team of co-located guards to track an intruder from any initial position in the environment. We present empirical analysis to illustrate the efficacy of the proposed tiling scheme. Simulations and testbed demonstrations are present in a video attachment.

We consider the problem of generating a fixed path for a mobile observer in a polygonal environment that can maintain a line-of-sight with an unpredictable target. In contrast to purely off-line or on-line techniques, we propose a hierarchical tracking strategy in which an off-line path generation technique based on a RRT is coupled with an online feedback-control technique to generate trajectories for the mobile observer.

This work presents an efficient and implementable solution to the problem of joint task allocation and path planning in a multi-UAV platform. The sensing requirement associated with the task gives rise to an uncanny variant of the traditional vehicle routing problem with coverage/sensing constraints. As is the case in several multi-robot path-planning problems, our problem reduces to an mTSP problem. In order to tame the computational challenges associated with the problem, we propose a hierarchical solution that decouples the vehicle routing problem from the target allocation problem. As a tangible solution to the allocation problem, we use a clustering-based technique that incorporates temporal uncertainty in the cardinality and position of the robots. Finally, we implement the proposed techniques on our multi-quadcopter platforms.