Gaemus E. Collins

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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 fixedwing aircraft cruising at a constant speed and fixed(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 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 this(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)
Standard Form 298 (Rev. 8/98) Prescribed by ANSI Std. Z39.18 Form Approved OMB No. 0704-0188 The public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the(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 this(More)
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