Fuzzy Logic Planning and Control for a Team of UAVs

J.F. Smith III (USA)


resource management, fuzzy logic, planning algorithms, decision support algorithms, cooperative behavior


A fuzzy logic resource allocation algorithm that enables a collection of unmanned air vehicles (UAVs) to automatically cooperate as they make meteorological measurements will be discussed. No human intervention during the measurement process is required. A fuzzy logic based planning algorithm determines the optimal trajectory and points each UAV will sample, while taking into account the UAVs’ risk, risk tolerance, reliability, and mission priority for sampling in certain regions. It also considers fuel limitations, mission cost, and related uncertainties. The real-time fuzzy control algorithm running on each UAV renders the UAVs autonomous allowing them to change course immediately without consulting with any commander, requests other UAVs to help, change the points that will be sampled when observing interesting phenomena, or to terminate the mission and return to base. The underlying optimization procedures including the fuzzy logic based cost function, the fuzzy logic decision rule for UAV path assignment, and the fuzzy algorithm that determines when a UAV should alter its mission to help another UAV are discussed. Simulations show the ability of the fuzzy algorithms to allow UAVs to effectively cooperate to increase the UAV team’s likelihood of success even when UAV measurement devices, propulsion or communications fail.

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