Pushkarini Agharkar

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We propose stochastic surveillance strategies for quickest detection of anomalies in discrete network environments. Our surveillance strategy is determined by optimizing the mean first passage time also known as the Kemeny constant of a Markov chain. We generalize the notion of mean first passage time to environments with heterogeneous travel and service(More)
This paper analyzes a discrete-time algorithm to synchronize a group of agents moving back and forth on a ring. Each agent or “bead” changes direction upon encountering another bead moving in the opposite direction. Communication is sporadic: two beads are able to exchange information only when they come sufficiently close. This allows agents to update(More)
This article provides analysis and optimization results for the mean first passage time, also known as the Kemeny constant, of a Markov chain. First, we generalize the notion of the Kemeny constant to environments with heterogeneous travel and service times, denote this generalization as the weighted Kemeny constant, and we characterize its properties.(More)
We introduce a novel dynamic vehicle routing problem termed the Radially Escaping Targets (RET) problem in which mobile targets appear uniformly randomly on a disk according to a stochastic process and move radially outward to escape the disk in a minimum amount of time. A single vehicle is assigned the task of intercepting the targets before they escape.(More)
This paper analyzes a discrete-time algorithm to synchronize a group of agents moving back and forth on a ring. Each agent or “bead” changes direction upon encountering another bead moving in the opposite direction. Communication is sporadic: two beads are able to exchange information only when they come sufficiently close. This allows agents to update(More)
Path Planning Algorithms for Robotic Agents by Pushkarini Agharkar The focus of this work is path planning algorithms for autonomous agents. Specifically, we study problems in three areas where path planning to direct the motion of autonomous agents is critical for their performance. The first problem is a vehicle routing problem in which mobile demands(More)
We study the problem of quickest detection of anomalies in an environment under extreme uncertainties in sensor measurements. The robotic roadmap corresponding to the environment can be represented as a graph with an arbitrary topology. We analyze the Ensemble CUSUM Algorithm for this surveillance problem. We quantify the delay in detection of anomalies(More)
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