Gaemus E. Collins

Learn More
— This work focuses on enabling multiple UAVs to flock together in order to distribute and collectively perform a given sensing task. Flocking is performed in a leader-follower fashion, and the leader is assumed to already have an effective control policy for the particular task. The UAVs are small fixed-wing aircraft cruising at a constant speed and fixed(More)
— We present a receding-horizon cooperative search algorithm that jointly optimizes routes and sensor orientations for a team of autonomous agents searching for a mobile target. By sampling the region of interest at locations with high target probability, we reduce the continuous search problem to an optimization on a finite graph. Paths are computed on(More)
This paper presents a receding-horizon cooperative search algorithm that jointly optimizes routes and sensor orientations for a team of autonomous agents searching for a mobile target in a closed and bounded region. By sampling this region at locations with high target probability at each time step, we reduce the continuous search problem to a sequence of(More)
We address the problem of estimating the state of a multi-agent system based on measurements corrupted by impulsive noise and whose dynamics are subject to impulsive disturbances. The qualifier " impulsive " refers to the fact that noise and disturbances are relatively small most of the time, but occasionally take large values. Noise and disturbances are(More)
Substantial research has addressed the problems of automatic search, routing, and sensor tasking for UAVs, producing many good algorithms for each task. But UAV surveillance missions typically include combinations of these tasks, so an algorithm that can manage and control UAVs through multiple tasks is desired. The algorithm in this paper employs a(More)
  • 1