Name
Abstract | ||
|---|---|---|
The MCA tuning problem consists in finding the best values for the parameters/coefficients of Motion Cueing Algorithms (MCA). MCA are used to control the movements of robotic motion platforms employed to generate inertial cues in vehicle simulators. This problem is traditionally approached with a manual pilot-in-the-loop subjective tuning, based on the opinion of several pilots/drivers. Instead, this paper proposes applying Particle Swarm Optimization (PSO) to solve this problem, using simulated motion platforms and objective indicators rather than subjective opinions. Results show that PSO-based tuning can provide a suitable solution for this complex optimization problem. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1145/3067695.3075990 | GECCO (Companion) |
Keywords | Field | DocType |
Optimization, particle swarm, PSO, MCA, tuning, simulation | Particle swarm optimization,Inertial frame of reference,Mathematical optimization,Computer science,Algorithm,Multi-swarm optimization,Artificial intelligence,Optimization problem,Machine learning | Conference |
Citations | PageRank | References |
0 | 0.34 | 4 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sergio Casas | 1 | 21 | 9.66 |
| Cristina Portalés | 2 | 21 | 8.26 |
| Inmaculada Coma | 3 | 50 | 7.46 |
| Marcos Fernández | 4 | 45 | 11.23 |