The Planning & Control of Robot Dexterous Manipulation Li Han, Zexiang Li, Jeff Trinkle, Zhiqiang Qin, Shilong Jiang Dept. of Computer Science Texas A&M.

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Presentation transcript:

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 Engineering Hong 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 start goal 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 Grasp Force Feasibility and Optimization Problem

Grasp Statics and Friction Cones 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: Plan an object trajectory Use generalized inverse method to find a “best”possible joint trajectory Infeasible Object Trajectory? Contact Motion?

Unreliable Manipulation Plan

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