optimization-Based Human-in-the-Loop Manipulation Using Joint Space Polytopes

This paper presents a new method of maximizing the free space for a robot operating in a constrained environment under operator supervision. The objective is to make the resulting trajectories more robust to operator commands and/or changes in the environment. To represent the volume of free space, the constrained manipulability polytopes are used. These polytopes embed the distance to obstacles, the distance to joint limits and the distance to singular configurations. The volume of the resulting Cartesian polyhedron is used in an optimization-based motion planner to create the trajectories. Additionally, we show how fast collision-free inverse kinematic solutions can be obtained by exploiting the pre-computed inequality constraints. The proposed algorithm is validated in simulation and experimentally.

Authors

• Philip Long
• Tarik Kelestemur
• Aykut Özgün Önol
• Taskin Padir

Keywords

• End effectors
• Trajectory
• Kinematics
• Task analysis
• Collision avoidance
• Aerospace electronics
• Joint Space
• Free Space
• Inequality Constraints
• Inverse Kinematics
• Joint Limits
• Optimization Problem
• Dimensional Space
• Autonomic System
• Optimization Procedure
• Decision Variables
• Translational Motion
• Linear Constraints
• End-effector
• Cartesian Space
• Trajectory Generation
• Task Space
• Joint Configuration
• Convex Polytope
• Kinematic Chain
• Linear Inequality Constraints
• End-effector Pose
• Candidate Paths