title: Dual filtering in operational and joint spaces for reaching and grasping.
abstract: To study human movement generation, as well as to develop efficient control algorithms for humanoid or dexterous manipulation robots, overcoming the limits and drawbacks of inverse-kinematics-based methods is needed. Adequate methods must deal with high dimensionality, uncertainty, and must perform in real time (constraints shared by robots and humans). This paper introduces a Bayesian filtering method, hierarchically applied in the operational and joint spaces to break down the complexity of the problem. The method is validated in simulation on a robotic arm in a cluttered environment, with up to 51 degrees of freedom.
authors.affiliation: Pascal Institute, Clermont University, Clermont-Ferrand, France, leo.pio.lopez@gmail.com.
authors.forename: Léo
authors.initials: L
authors.lastname: Lopez
authors.forename: Jean-Charles
authors.initials: JC
authors.lastname: Quinton
authors.forename: Youcef
authors.initials: Y
authors.lastname: Mezouar
mesh_terms: D000465:Algorithms; D001132:Arm; D001499:Bayes Theorem; D001696:Biomechanical Phenomena; D003198:Computer Simulation; D005260:Female; D018737:Hand Strength / Q000502:physiology*; D006801:Humans; D008297:Male; D008954:Models, Biological; D009068:Movement / Q000502:physiology*; D012371:Robotics