Sigmoid Limiting Functions and Potential Field Based Autonomous Air Refueling Path Planning for UAVs
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In this paper autonomous air-refueling (AAR) path planning for Unmanned Aerial Vehicles (UAVs) has been discussed and an enhanced approach has been put forward. AAR path planning for UAVs was designed and the basic model of the pattern was put forward in our previous work (Cetin and Yilmaz 2013). Additionally to our previous works, the deficiencies of the previous approach, like smooth maneuvers in the tanker approach and the boundary functions of the potential zones has been handled, furthermore special pattern parameters are added to the approach which makes it suitable for different kind of UAVs that has variable flight speed and turn radius parameters. An important originality of the approach is using of sigmoid limiting functions while modeling dynamic behaviors of the potential fields that are based on path planning algorithms. In order to use the AAR path planning approach in a real time application, the computation is performed in Graphical Processing Units (GPUs) based parallel architecture by benefiting from many cores in General Purpose Graphical Processing Units (GPGPU) as described in previous research (Cetin and Yilmaz 2013). With the addition of the sigmoid limiting functions instead of logical binary boundary functions computation needs of the autonomous approach become higher point and the only way to use the approach in the real time applications is benefiting of the parallel computing approach. The comparison of the boundary functions as computational performance and path outputs are discussed with the simulation results in this paper. Simulation results are proved that this novel autonomous parallel path planning approach is successful and it would be used in real time applications like AAR mission.
KeywordsAutonomous air refueling path planning Potential field based path planning Parallel potential field computation Sigmoid limiting and membership functions GPGPU
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