We propose a shortest trajectory planning algorithm implementation for Unmanned Aerial Vehicles (UAVs) on an embedded GPU. Our goal is the development of a fast, energyefficient global planner for multi-rotor UAVs supporting human operator during rescue missions. The work is based on OpenCL parallel non-deterministic version of the Dijkstra algorithm to solve the Single Source Shortest Path (SSSP). Our planner is suitable for real-time path re-computation in dynamically varying environments of up to 200 m 2 . Results demonstrate the efficacy of the approach, showing speedups of up to 74x, saving up to ∼ 98% of energy versus the sequential benchmark, while reaching near-optimal path selection, keeping the average path cost error smaller than 1.2%.
Another example is REF which proposes a shortest trajectory planning for UAVs on an embedded GPU.
An energy-efficient parallel algorithm for real-time near-optimal UAV path planning
CF '16
Computer Science