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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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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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