Custom distribution for sampling-based motion planning

Sampling-based motion planning algorithms are widely used in robotics because they are very effective in high-dimensional spaces. However, the success rate and quality of the solutions are determined by an adequate selection of their parameters such as the distance between states, the local planner, and the sampling distribution. For robots with large configuration spaces or dynamic restrictions, selecting these parameters is a challenging task. This paper proposes a method for improving the performance to a set of the most popular sampling-based algorithms, the Rapidlyexploring Random Trees (RRTs) by adjusting the sampling method. The idea is to replace the uniform probability density function (U-PDF) with a custom distribution (C-PDF) learned from previously successful queries in similar tasks. With a few samples, our method builds a custom distribution that allows the RRT to grow to promising states that will lead to a solution. We tested our method in several autonomous driving tasks such as parking maneuvers, obstacle clearance and under narrow passages scenarios. The results show that the proGabriel O. Flores-Aquino E-mail: gfloresa0500@alumno.ipn.mx J. Irving Vasquez-Gomez E-mail: jvasquezg@ipn.mx O. Octavio Gutierrez-Frias E-mail: ogutierrezf@ipn.mx Instituto Politécnico Nacional (IPN), Sección de Estudios de Posgrado e Investigación de la Unidad Profesional Interdisciplinaria en Ingenieŕıa y Tecnoloǵıas Avanzadas (UPIITA), Ciudad de México, México 2 Instituto Politécnico Nacional (IPN), Centro de Inovación y Desarrollo Tecnológico en Cómputo (CIDETEC), Ciudad de México, México Consejo Nacional de Ciencia y Tecnoloǵıa (CONACYT) Ciudad de México, México posed method outperforms the original RRT and several improved versions in terms of success rate, tree density and computation time. In addition, the proposed method requires a relatively small set of examples, unlike current deep learning techniques that require a vast amount of examples.

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