Collision Avoidance in Model Predictive Control Using Velocity Damper

We propose an advanced method for controlling the motion of a manipulator robot with strict collision avoidance in dynamic environments, leveraging a velocity damper constraint. Unlike conventional distance-based constraints, which tend to saturate near obstacles to reach optimality, the velocity damper constraint considers both distance and relative velocity, ensuring a safer separation. This constraint is incorporated into a model predictive control framework and enforced as a hard constraint through analytical derivatives supplied to the numerical solver. The approach has been fully implemented on a Franka Emika Panda robot and validated through experimental trials, demonstrating effective collision avoidance during dynamic tasks and robustness to unmodeled disturbances. An efficient open-source implementation along examples are provided here: https://gepettoweb.laas.fr/articles/haffemayer2025.html.

Authors

• Arthur Haffemayer
• Armand Jordana
• Ludovic De Matteïs
• Krzysztof Wojciechowski
• Ludovic Righetti
• Florent Lamiraux
• Nicolas Mansard

Keywords

• Geometry
• Dynamics
• Shock absorbers
• Robustness
• Collision avoidance
• Computational complexity
• Manipulator dynamics
• Predictive control
• Model Predictive Control
• Hard Constraints
• Numerical Solver
• Model Predictive Control Framework
• Ellipsoid
• Graphics Processing Unit
• Time Derivative
• Inequality Constraints
• Path Planning
• Closest Point
• Dynamic Motion
• Trajectory Optimization
• Human Operator
• Obstacle Avoidance
• Skew-symmetric
• Lie Group
• Robot State
• End-effector Position
• Dynamic Obstacles
• Speed Of The Robot
• Target Pose