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Hari, Sai Kanth

Publications and source records attributed to Hari, Sai Kanth.

Bounds on Optimal Revisit Times in Persistent Monitoring Missions With a Distinct and Remote Service Station</strong

We report persistent monitoring missions require an up-to-date knowledge of the changing state of the underlying environment. Unmannned aerial vehicles (UAVs) can be gainfully employed to continually visit a set of targets representing tasks (and locations) in the environment and collect data therein for long time periods. The enduring nature of these missions requires the UAV to be regularly recharged at a service station. In this article, we consider the case in which the service station is not colocated with any of the targets. An efficient monitoring requires the revisit time, defined as the maximum of the time elapsed between successive revisits to targets, to be minimized. Here, we consider the problem of determining UAV routes that lead to the minimum revisit time. The problem is NP-hard, and its computational difficulty increases with the fuel capacity of the UAV. We develop an algorithm to construct near-optimal solutions to the problem quickly when the fuel capacity exceeds a threshold. We also develop lower bounds to the optimal revisit time and use these bounds to demonstrate (through numerical simulations) that the constructed solutions are, on an average, at most 0.01% away from the optimum.

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An Approximation Algorithm for a Task Allocation, Sequencing and Scheduling Problem Involving a Human-Robot Team

Here we present an approximation algorithm for a Task Allocation, Sequencing and Scheduling Problem (TASSP) involving a team of human operators and robots. The robots have to travel to a given set of targets and collaboratively work on the tasks at the targets with the human operators. The problem aims to find a sequence of targets for each robot to visit and schedule the tasks at the targets with the human operators such that each target is visited exactly once by some robot, the scheduling constraints are satisfied and the maximum mission time of any robot is minimum. This problem is a generalization of the single Traveling Salesman Problem and is NP-Hard. Given k robots and m human operators, an algorithm is developed for solving the TASSP with an approximation ratio equal to 5/2- 1/k when m ≥ k and equal to 7/2 -1/k otherwise. Computational results are also presented to corroborate the performance of the proposed algorithm.

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