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In this paper, we present a graphics processing unit (GPU) based implementation of a receding horizon solution to the optimal sensor scheduling problem. The optimal sensor scheduling problem can be posed as a Partially Observed Markov Decision Process (POMDP) whose solution is given by an Information Space (I-space) Dynamic Programming (DP) problem. In references [1], [2], we proposed a simulation...
In this paper, we present a receding horizon solution to the problem of optimal scheduling for multiple sensors monitoring a group of dynamical targets. The term ‘target’ is used here in the classic sense of being the object that is being sensed or observed by the sensors. This problem is motivated by the Space Situational Awareness (SSA) problem. The multi-sensor optimal scheduling problem can be...
In this paper, generalized versions of the probabilistic sampling-based planners, i.e., probabilistic roadmaps and rapidly exploring random tree, are presented. The generalized planners, i.e., generalized probabilistic roadmap and the generalized rapidly exploring random tree, result in hybrid hierarchical feedback planners that are robust to the uncertainties in the robot motion model and in the...
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