Fast Manipulability Maximization Using Continuous-Time Trajectory optimization

A significant challenge in manipulation motion planning is to ensure agility in the face of unpredictable changes during task execution. This requires the identification and possible modification of suitable joint-space trajectories, since the joint velocities required to achieve a specific endeffector motion vary with manipulator configuration. For a given manipulator configuration, the joint space-to-task space velocity mapping is characterized by a quantity known as the manipulability index. In contrast to previous control-based approaches, we examine the maximization of manipulability during planning as a way of achieving adaptable and safe joint space-to-task space motion mappings in various scenarios. By representing the manipulator trajectory as a continuous-time Gaussian process (GP), we are able to leverage recent advances in trajectory optimization to maximize the manipulability index during trajectory generation. Moreover, the sparsity of our chosen representation reduces the typically large computational cost associated with maximizing manipulability when additional constraints exist. Results from simulation studies and experiments with a real manipulator demonstrate increases in manipulability, while maintaining smooth trajectories with more dexterous (and therefore more agile) arm configurations.

Authors

• Filip Maric
• Oliver Limoyo
• Luka Petrovic
• Trevor Ablett
• Ivan Petrovic
• Jonathan Kelly

Keywords

• Gaussian processes
• Aerospace electronics
• Manipulators
• Planning
• Computational efficiency
• Indexes
• Trajectory optimization
• Intelligent robots
• Faces
• Continuous-time Trajectory
• Dexterity
• Task Execution
• Path Planning
• Joint Space
• Joint Velocity
• Trajectory Generation
• Interpolation
• Computation Time
• Cost Function
• Multiple Conditions
• Singular Value
• Parametrized
• Jacobian Matrix
• Quadratic Programming
• Configuration Space
• Posterior Mode
• Common Tasks
• Unexpected Changes
• End-effector Position
• Task Space
• Maximum A Posteriori
• Final Configuration
• Kinematic Chain
• Probabilistic Inference
• Kinematic Control
• Representative Trajectories
• Goal State
• Starting State