Title :
A Learning Rule-Based Robotics Hand Optimal Force Closure
Author :
Al-Gallaf, E. Mattar
Author_Institution :
Dept. of Electr. & Electron. Eng., Univ. of Bahrain, Bahrain, Bahrain
Abstract :
This article presents an intelligent fuzzy rule-based approach for computing optimal set of joints torques, for manipulating a grasped object by a dexterous multi-fingered robotics hand. The intelligent approached followed here, is to let a learning fuzzy system to approximate a nonlinear force formulation for optimal contact forces. This has been achieved via following two major steps: The first was to formulate the optimal fingertips force distribution as a quadratic force optimization problem, hence to generate a large set of data. The second step was to involve a learning fuzzy system (Neuro- Fuzzy System) to learn the nonlinear relations governing fingertips forces (ℝ∈12 × 1) to hand joint torques (ℝ∈12 × 1). Simulation results show that the proposed Neuro-Fuzzy network do achieve optimal grasping force in real time.
Keywords :
dexterous manipulators; force control; fuzzy neural nets; fuzzy systems; learning (artificial intelligence); manipulator dynamics; torque; dexterous multi fingered robotics hand; intelligent fuzzy rule; joints torques; learning fuzzy system; learning rule; neuro fuzzy network; nonlinear force formulation; robotics hand optimal force closure; Fingers; Force; Friction; Grasping; Joints; Optimization; Robots; Dexterous Hands; Keywords- Grasping; Learning Neuro-fuzzy.;
Conference_Titel :
Computational Intelligence, Communication Systems and Networks (CICSyN), 2010 Second International Conference on
Conference_Location :
Liverpool
Print_ISBN :
978-1-4244-7837-8
Electronic_ISBN :
978-0-7695-4158-7
DOI :
10.1109/CICSyN.2010.57