An open-source motion planning framework for mobile manipulators using constraint-based task space control with linear MPC

We present an open source motion planning framework for ROS, which uses constraint and optimization based task space control to generate trajectories for the whole body of mobile manipulators. Motion goals are defined as constraints which are enforced on task space functions. They map the controllable degrees of freedom of a system onto custom task spaces, which can, but do not have to be, the Cartesian space. We use this expressive tool from motion control to pre-compute trajectories in order to utilize the fact that most robots offer controllers to follow such trajectories. As a result, our framework only requires a kinematic model of the robot to control it. In addition, we extend the constraint-based motion control approach with linear MPC to explicitly optimize for velocity, acceleration and jerk simultaneously, which allows us to enforce constraints on all derivatives in both joint and task space at the same time. As a result, we can reuse predefined motion goals on any robot without modifications. Our framework was tested on four different robots to show its generality.

Authors

• Simon Stelter
• Georg Bartels
• Michael Beetz

Keywords

• Robot kinematics
• Aerospace electronics
• Manipulators
• Probabilistic logic
• Trajectory
• Planning
• Task analysis
• Path Planning
• Model Predictive Control
• Task Space
• Mobile Manipulator
• Linear Model Predictive Control
• Task Space Control
• Motion Planning Framework
• Control Approach
• Joint Space
• Cartesian Space
• Trajectories In Order
• Control Variables
• Identity Matrix
• Local Minima
• Quadratic Programming
• Linear Constraints
• Prediction Horizon
• Slack Variables
• Trajectory Length
• Planning Algorithm
• Task Constraints
• Velocity Limits
• Model Predictive Control Problem
• Real Robot
• Reference Velocity
• Kinematic Chain
• Trajectory Generation
• Diagonal Weight Matrix
• Velocity Constraints
• C++ Code