Artículos de revistas
Constrained Optimization With Stochastic Feasibility Regions Applied To Vehicle Path Planning
Registro en:
1573-2916
Journal Of Global Optimization. SPRINGER, n. 64, n. 4, p. 803 - 823.
0925-5001
WOS:000373017300011
10.1007/s10898-015-0353-9
Autor
Zambom
AZ; Collazos
JAA; Dias
R
Institución
Resumen
In real-time trajectory planning for unmanned vehicles, on-board sensors, radars and other instruments are used to collect information on possible obstacles to be avoided and pathways to be followed. Since, in practice, observations of the sensors have measurement errors, the stochasticity of the data has to be incorporated into the models. In this paper, we consider using a genetic algorithm for the constrained optimization problem of finding the trajectory with minimum length between two locations, avoiding the obstacles on the way. To incorporate the variability of the sensor readings, we propose a modified genetic algorithm, addressing the stochasticity of the feasible regions. In this way, the probability that a possible solution in the search space, say x, is feasible can be derived from the random observations of obstacles and pathways, creating a real-time data learning algorithm. By building a confidence region from the observed data such that its border intersects with the solution point x, the level of the confidence region defines the probability that x is feasible. We propose using a smooth penalty function based on the Gaussian distribution, facilitating the borders of the feasible regions to be reached by the algorithm. 64 803 823 11th International Symposium on Generalized Convexity and Monotonicity AUG 25-30, 2014 IMPA, Rio de Janeiro, BRAZIL