We consider a Persistent Intelligence, Surveillance and Reconnaissance (PISR) routing problem, which includes collecting data from a set of specified task locations and delivering that data to a control station. Each task is assigned a refresh rate based on its priority, where higher priority tasks require higher refresh rates. The UAV team's objective is to minimize the maximum of the delivery times of all the tasks' data to the control station, while simultaneously, satisfying each task's revisit period constraint. The centralized path planning problem for this PISR routing problem is formulated using mixed integer linear programming and solved using a branch-and-cut algorithm. Heuristics are presented to find suboptimal feasible solutions that require much less computation time. The algorithms are tested on several instances and their performance is compared with respect to the optimal cost and computation time.S. G. Manyam is a NRC Fellow,

Abstract-We consider a cooperative vehicle routing problem for surveillance and reconnaissance missions with communication constraints between the vehicles. We propose a framework which involves a ground vehicle and an aerial vehicle; the vehicles travel cooperatively satisfying the communication limits, and visit a set of targets. We present a mixed integer linear programming (MILP) formulation and develop a branch-andcut algorithm to solve the path planning problem for the ground and air vehicles. The effectiveness of the proposed approach is corroborated through extensive computational experiments on several randomly generated instances.

The Dubins Traveling Salesman Problem (DTSP) has generated significant interest over the last decade due to its occurrence in several civil and military surveillance applications. Currently, there is no algorithm that can find an optimal solution to the problem. In addition, relaxing the motion constraints and solving the resulting Euclidean TSP (ETSP) provides the only lower bound available for the problem. However, in many problem instances, the lower bound computed by solving the ETSP is far below the cost of the feasible solutions obtained by some well-known algorithms for the DTSP.

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This paper deals with the problem of synthesizing feedforward control to aid the regulation of output of a nonlinear system in the presence of partially known exogenous inputs. The problem appears in many engineering applications including Rollto-Roll (R2R) manufacturing systems. Currently known methods for this problem either require the solution of a constrained partial differential equation or the preview information of the signal to be tracked. The novelty of this paper lies in synthesizing feedforward control as the solution of a system of differential-algebraic equations, which is considerably less complex and suitable for practical implementation. In this paper, we consider the problem of regulating the output while rejecting the disturbances and apply it to R2R manufacturing systems. The problem of tracking reference signals can also be handled with the suggested technique. We assume that the disturbance signal is the output of a known exogenous system with unknown initial conditions. A parameter identification scheme to estimate the unknown initial conditions is developed. The proposed technique is successfully applied to control of web tension in a large R2R machine which mimics most of the features of industrial R2R machines and contains real-time hardware and software that is used in industrial practice. Extensive experiments were conducted to evaluate the proposed scheme for web tension control under various experimental conditions, including different web speeds and materials. We will present and discuss the representative experimental results with the proposed technique and provide a comparison with an industrial PI control scheme to highlight the benefits of using the proposed scheme.