Path planning for grasping operations using an adaptive PCA-based sampling method

Authors:
Jan Rosell;Raúl Suárez;Alexander Pérez
Affiliations:
Institute of Industrial and Control Engineering, Universitat Politècnica de Catalunya, Barcelona, Spain;Institute of Industrial and Control Engineering, Universitat Politècnica de Catalunya, Barcelona, Spain;Institute of Industrial and Control Engineering, Universitat Politècnica de Catalunya, Barcelona, Spain and Escuela Colombiana de Ingeniería "Julio Garavito", Bogotá D.C., Colombia
Venue:
Autonomous Robots
Year:
2013

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Abstract

The planning of collision-free paths for a hand-arm robotic system is a difficult issue due to the large number of degrees of freedom involved and the cluttered environment usually encountered near grasping configurations. To cope with this problem, this paper presents a novel importance sampling method based on the use of principal component analysis (PCA) to enlarge the probability of finding collision-free samples in these difficult regions of the configuration space with low clearance. By using collision-free samples near the goal, PCA is periodically applied in order to obtain a sampling volume near the goal that better covers the free space, improving the efficiency of sampling-based path planning methods. The approach has been tested with success on a hand-arm robotic system composed of a four-finger anthropomorphic mechanical hand (17 joints with 13 independent degrees of freedom) and an industrial robot (6 independent degrees of freedom).