Online Approach to Near Time-Optimal Task-Space Trajectory Planning
trajectory planning
AI summary
Problem
Traditional trajectory planning either sacrifices robot capabilities for computational efficiency using fixed limits, or fully exploits them but requires heavy pre-computation and long execution times, making it unsuitable for dynamic collaborative environments.
Approach
The method plans entirely in Cartesian Space on-the-fly by continuously evaluating the robot's state-dependent movement capacity using efficient polytope algebra and updating a time-optimal Trapezoidal Acceleration Profile for the remaining path at each execution step.
Key results
- Near time-optimal performance (only ~5% slower than offline TOPP-RA benchmark)
- Enables planning up to 100% of the robot's kinematic limits in real-time
- Reduces tracking error to under 4mm compared to traditional Cartesian methods
- Achieves sub-millisecond computation per step, enabling real-time replanning
Why it matters
Enables collaborative robots to safely and efficiently adapt to dynamic environments by fully exploiting their physical capabilities without sacrificing speed or accuracy.
Abstract
Conforming to safety standards often limits collabo- rative robots’ performance and size, restricting their applications despite their capabilities. Planning their motions in human environments involves a trade-off between optimal trajectory planning and quick adaptation to dynamic, unstructured spaces. Traditional trajectory planning methods either use simplified robot models and sacrifice robot’s abilities for computational efficiency, or exploit robots’ abilities fully but have high com- putational complexity and rely on substantial pre-computation. This paper introduces an approach for trajectory planning that exploits robot’s full motion abilities while planning on-the-fly. In each step of the trajectory execution, it evaluates robot’s movement ability using polytope algebra and calculates a time- optimal Trapezoidal Acceleration Profile (TAP) on the remaining trajectory. The method is shown to be near time-optimal (around 5% slower trajectories) by benchmarking it against the state- of-the-art time-optimal method TOPP-RA. The method allows reaching higher velocities (able to plan up to 100% of the robot’s kinematic limits) while at the same time lowering the tracking error (under 4mm) than traditional Cartesian Space planning methods. A mock-up experiment demonstrates its efficiency in collaborative waste sorting using a Franka Emika Panda robot.