Distributed motion planning algorithms for a collection of vehicles
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Unmanned Vehicles (UVs) currently perform a variety of tasks critical to a military mission. In future, they are envisioned to have the ability to accomplish a mission co-operatively and effectively with limited fuel onboard. In particular, they must search for targets, classify the potential targets detected, attack the classified targets and perform an assessment of the damage done to the targets. In some cases, UVs are themselves munitions. The targets considered in this thesis are stationary. The problem considered in this thesis, referred to as the UV problem, is the allotment of tasks to each UV along with the sequence in which they must be performed so that a maximum number of tasks are accomplished collectively. The maneuverability constraints on the UV are accounted for by treating them as Dubin's vehicles. Since the UVs considered are disposable with life spans governed by their fuel capacity, it is imperative to use their life as efficiently as possible. Thus, we need to develop a fuel-optimal (equivalently, distance optimal) motion plan for the collection of UVs. As the number of tasks to be performed and the number of vehicles performing these tasks grow, the number of ways in which the set of tasks can be distributed among the UVs increases combinatorially. The tasks a UV is required to perform are also subject to timing constraints. A UV cannot perform certain tasks before completing others. We consider a simplified version of the UV problem and do not take into account the timing constraints on the tasks to be performed on targets. We use linear programming and graph theory to find a solution to this simplified UV problem; in the graph theory approach, we develop an algorithm which is a generalization of the solution procedures available to solve the Traveling Salesman Problem (TSP). We provide an example UV problem illustrating the solution procedure developed in this thesis.
Pargaonkar, Sudhir Sharadrao (2003). Distributed motion planning algorithms for a collection of vehicles. Master's thesis, Texas A&M University. Texas A&M University. Available electronically from