Fuzzy Logic Based UAV Allocation and Coordination
A fuzzy logic resource allocation algorithm that enables a collection of unmanned aerial vehicles (UAVs) to automatically cooperate will be discussed. The goal of the UAVs’ coordinated effort is to measure the atmospheric index of refraction. Once in flig
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Abstract. A fuzzy logic resource allocation algorithm that enables a collection of unmanned aerial vehicles (UAVs) to automatically cooperate will be discussed. The goal of the UAVs’ coordinated effort is to measure the atmospheric index of refraction. Once in flight no human intervention 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, and change the points that will be sampled when observing interesting phenomena. Simulations show the ability of the control algorithm to allow UAVs to effectively cooperate to increase the UAV team’s likelihood of success. Keywords. Decision support systems, distributed control systems, fuzzy control, knowledge-based systems applications, software agents for intelligent control systems.
1 Introduction Knowledge of meteorological properties is fundamental to many decision processes. Due to personnel limitations and risks, it is useful if related measurement processes can be conducted in a fully automated fashion. Recently developed fuzzy logic based algorithms that allow a collection of unmanned aerial vehicles (UAVs) and an interferometer platform (IP) [9] to automatically collaborate will be discussed. The UAVs measure the index of refraction in real-time to help determine the position of an electromagnetic source (EMS). The IP is actually an airplane with an interferometer onboard that measures emissions from the electromagnetic source whose position is to be estimated. Each UAV has onboard its own fuzzy logic based real-time control algorithm. The control algorithm renders each UAV fully autonomous; no human intervention is necessary. The control algorithm aboard each UAV will allow it to determine its own course, change course to avoid danger, sample phenomena of interest that were not preplanned, and cooperate with other UAVs. Section 2 provides an overview of the meteorological sampling problem and a high level description of the planning and control algorithms that render the UAV team fully autonomous. Section 3 discusses the electromagnetic measurement space,
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J.F. Smith and T.H. Nguyen
UAV risk, and the planning algorithm. Section 3 also discusses the UAV path construction algorithm that determines the minimum number of UAVs required to complete the task, a fuzzy logic based approach for assigning paths to UAVs and which UAVs should be assigned to the overall mission. Section 4 describes the control algorithm that renders the UAVs autonomous. Section 4 also describes the priority for helping (PH) algorithm, a part of the control algorithm based on fuzzy logic that determines which UAV should help another U
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