Name
Abstract | ||
|---|---|---|
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, energy-efficient 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 m2. 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%. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1145/2903150.2911712 | Conf. Computing Frontiers |
Field | DocType | Citations |
Motion planning,Human operator,Shortest path problem,Efficient energy use,Computer science,Parallel algorithm,Constrained Shortest Path First,Real-time computing,K shortest path routing,Dijkstra's algorithm | Conference | 4 |
PageRank | References | Authors |
0.50 | 13 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daniele Palossi | 1 | 41 | 6.12 |
| Michele Furci | 2 | 29 | 3.62 |
| Roberto Naldi | 3 | 221 | 23.68 |
| Andrea Marongiu | 4 | 337 | 39.19 |
| Lorenzo Marconi | 5 | 845 | 93.46 |
| Luca Benini | 6 | 13116 | 1188.49 |