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Type: Artigo de periódico
Title: Constrained Optimization With Stochastic Feasibility Regions Applied To Vehicle Path Planning
Author: Zambom
AZ; Collazos
JAA; Dias
Abstract: 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.
Subject: Constrained Optimization
Stochastic Feasible Regions
Penalty Function
Autonomous Vehicle
Nonparametric Curve Estimation
Citation: Journal Of Global Optimization. SPRINGER, n. 64, n. 4, p. 803 - 823.
Rights: fechado
Identifier DOI: 10.1007/s10898-015-0353-9
Date Issue: 2016
Appears in Collections:Unicamp - Artigos e Outros Documentos

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