the planning & control of robot dexterous manipulation
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The Planning & Control of Robot Dexterous Manipulation
Li Han, Zexiang Li, Jeff Trinkle, Zhiqiang Qin, Shilong Jiang
Dept. of Computer Science Texas A&M University
Dept. of Electrical and Electronic EngineeringHong Kong Univ. of Science and Technology
Rodin
Dexterous Manipulation
• Tasks: a robotic hand– grasps an object, and
– moves the object from a start configuration to a goal configuration.
• Assumptions– Quasi-Static Systems
– Rigid Body Motions• preserve distances and orientations
– Known System and Environment Parameters
SAMM (O. Khatib, USA) Katharina (Germany)
Dexterous Manipulation Systems
Japan
Fixture (K. Goldberg)Digital Actor (J.-C. Latombe)
Cellular Man. (Sci. American)AerCam (NASA)
Applications
Overview
• Problem Statement• Force and Motion Feasibility Issues• Manipulation Planning and Control• Experimental Result• Summary
HKUST Hand (Z. Li)
Dexterous Manipulation
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• Feasible States– Closure: Variety or Manifold
• Feasible Velocities: Tangent Vectors• Feasible Forces: Co-Tangent Vectors
– Collision-Free
Dexterous Manipulation
• Manipulation Planner• Manipulation Controller• Feasible States
– Grasp Statics: Force
– Manipulation Kinematics: Motion
start goal
Grasp Statics and Friction Cones
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: FrictionCoulomb
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Linear Matrix Inequality (LMI)
Numerical Results
• Convex Programming Involving LMIs
(S. Boyd’s Convex Programming Group at Stanford)• Feasibility and Optimization: < 7.82ms (HP/Convex)
Manipulation Kinematics
Grasp Kinematics
Manipulation Kinematics:
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• Plan an object trajectory
• Use generalized inverse method to find a “best”possible joint trajectory
• Infeasible Object Trajectory?
• Contact Motion?
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KinematicspoV
Unreliable Manipulation Plan
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Modular Control System Architecture
• Manipulation Objectives– Move the object
– Improve the grasp
Experimental System & Result
Future Work
• Large Scale Object Manipulation in a Crowded Environment– Regrasping and Dexterous Manipulation Planning
• Dynamic Constraints• Uncertainty and Robustness• Applications …
Conclusion
• Grasp Statics – Linear Matrix Inequalities for Nonlinear Friction Cones
– Convex Programming
• Manipulation Kinematics – Tangent Space (Feasibility Constraints)
– Inclusion of all kinematic variables
• A Modular Control System Architecture• Manipulation Planning
– “Local” Motion in a Clear Environment
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