Real-Time and Accurate Collision-Free Teleoperation Via Differentiable Constraint-Based Trajectory Planning
Max Grobbel , Tristan Schneider , Daniel Fl ̈ogel, S ̈oren Hohmann
AI summary
Problem
Non-haptic teleoperation devices only control the end-effector, causing frequent collisions, while existing optimal-control trajectory planners either sacrifice geometric accuracy with sphere approximations or degrade convergence and speed by approximating derivatives.
Approach
The method models the robot with capsules and environmental obstacles as polytopes, then embeds a duality-derived set of differentiable collision constraints into a model predictive trajectory planner for real-time optimization.
Key results
- Lower computation times than state-of-the-art teleoperation methods
- Accurate geometric modeling of robots and obstacles using capsules and polytopes
- Smoother, collision-free end-effector tracking in real-world UR5e experiments
- Successful real-time integration of differentiable collision constraints into a predictive controller
Why it matters
Provides a practical, high-fidelity collision avoidance solution for real-time teleoperation, benefiting fields like remote surgery and hazardous environment manipulation.
Abstract
In teleoperation, the human operator typically controls only the end-effector pose, which often leads to self- collisions of the manipulator and collisions with environmental obstacles, since joints and links are not controlled individually. A common strategy to mitigate this issue is to enhance the op- erator’s input using optimal-control-based trajectory planning. As derivative-based solvers require differentiable constraints, existing approaches either approximate robots and obstacles with spheres, reducing geometric accuracy, or approximate derivatives, degrading convergence and increasing computation times. We address these limitations by adapting a recent for- mulation of differentiable collision-avoidance constraints, based on duality in convex optimization, to the teleoperation setting. The robot is approximated with capsules and the environment with polytopes. We compare the resulting trajectory planning method against state-of-the-art techniques in simulation with varying numbers of obstacles and evaluate it on a UR5e manipulator in a real-world teleoperation test. Results show that our approach achieves lower computation times while enabling more accurate obstacle modeling, leading to smoother and collision-free end-effector teleoperation.