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Multi-objective dynamic optimization with genetic algorithms for automatic parking

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Abstract

This paper addresses the problem of automatic parking by a back-wheel drive vehicle, using a biomimetic model based on direct coupling between vehicle perceptions and actions. This problem is solved by means of a bio-inspired approach in which the vehicle controller does not need to know the car kinematics and dynamic, neither does it call for a priori knowledge of the environment map. The key point in the proposed approach is the definition of performance indices that for automatic parking happen to be functions of the strategic orientations to be injected, in real time, to the car-like robot controller. This solution leads to a dynamic multi-objective optimization problem, which is extremely hard to be dealt analytically. A genetic algorithm is therefore applied, thanks to which we obtain a very simple and efficient solution.

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Authors and Affiliations

  1. Department of Artificial Intelligence, Faculty of Compute Science, Universidad Politécnica de Madrid, Campus de Montegancedo, 28660, Madrid, Spain

    Darío Maravall & Javier de Lope

Authors
  1. Darío Maravall
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  2. Javier de Lope
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Correspondence to Darío Maravall.

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Maravall, D., de Lope, J. Multi-objective dynamic optimization with genetic algorithms for automatic parking. Soft Comput 11, 249–257 (2007). https://doi.org/10.1007/s00500-006-0066-6

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  • DOI: https://doi.org/10.1007/s00500-006-0066-6

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