Online approach to near time-optimal task-space trajectory planning
Résumé
Conforming to safety standards often limits collaborative 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 real-time responsiveness to dynamic, unstructured spaces. Traditional reactive trajectory planning methods use simplified robot models, while methods exploiting robots' abilities have high computational complexity or lack reactivity. This paper introduces an approach for real-time trajectory planning that exploits the robot's full motion abilities. 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 approach is compared to state-of-the-art methods, showing comparable execution time with better movement capacity utilization and lower tracking error. A mock-up experiment demonstrates its efficiency in collaborative waste sorting using a Franka Emika Panda robot.
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