Trajectory generation for immediate path-accurate jerk-limited stopping of industrial robots

Lange, Friedrich; Suppa, Michael

doi:10.1109/icra.2015.7139463

Cited by 9 publications

(3 citation statements)

References 17 publications

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“…While TOPP is typically performed offline, there are also online methods capable of considering jerk constraints. In [12] and [13], a jerk-limited path tracking technique is proposed, however, without focusing on the traversing time. As mentioned in the introduction, it is also possible to compute a time-optimal trajectory from one kinematic state to another considering jerk constraints [3], [14]- [19].…”

Section: Related Workmentioning

confidence: 99%

Learning Time-optimized Path Tracking with or without Sensory Feedback

Kiemel

Kröger

2022

2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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In this paper, we present an iterative method to quickly traverse multi-dimensional paths considering jerk constraints. As a first step, we analyze the traversal of each individual path dimension. We derive a range of feasible target accelerations for each intermediate waypoint of a onedimensional path using a binary search algorithm. Computing a trajectory from waypoint to waypoint leads to the fastest progress on the path when selecting the highest feasible target acceleration. Similarly, it is possible to calculate a trajectory that leads to minimum progress along the path. This insight allows us to control the traversal of a one-dimensional path in such a way that a reference path length of a multi-dimensional path is approximately tracked over time. In order to improve the tracking accuracy, we propose an iterative scheme to adjust the temporal course of the selected reference path length. More precisely, the temporal region causing the largest position deviation is identified and updated at each iteration. In our evaluation, we thoroughly analyze the performance of our method using seven-dimensional reference paths with different path characteristics. We show that our method manages to quickly traverse the reference paths and compare the required traversing time and the resulting path accuracy with other stateof-the-art approaches.

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Section: Related Workmentioning

confidence: 99%

Learning Time-optimized Path Tracking with or without Sensory Feedback

Kiemel

Kröger

2022

2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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“…Another critical task for trajectory generation is stopping during constrained motion without exerting undesired forces, i.e., without leaving the previously defined path. This has been investigated in [10].…”

Section: Problem Formulationmentioning

confidence: 99%

Iterative path-accurate trajectory generation for fast sensor-based motion of robot arms

Lange

Albu-Schäffer

2016

Advanced Robotics

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Sensor-based trajectory generation of industrial robots can be seen as the task of, first, adaptation of a given robot program according to the actually sensed world, and second, its modification that complies with robot constraints regarding its velocity, acceleration, and jerk. The second task is investigated in this paper. Whenever the sensed trajectory violates a constraint, a transient trajectory is computed that, both, keeps the sensed path, and reaches the sensed trajectory as fast as possible while satisfying the constraints. This is done by an iteration of forward scaling and backtracking. In contrast to previous papers a new backtracking algorithm and an adaptation of the prediction length are presented that are favorable for high-speed trajectories. Arc Length Interpolation is used in order to improve the path accuracy. This is completed by provisions against cutting short corners or omitting of loops in the given path. The refined trajectory is computed within a single sampling step of 4 ms using a standard KUKA industrial robot.

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“…Forward scaling with ALI has already been presented for the dual problem of stopping along a given path [22]. That task is simpler, as it does without prediction and thus without backtracking.…”

Section: Introductionmentioning

confidence: 99%

Path-Accurate Online Trajectory Generation for Jerk-Limited Industrial Robots

Lange

Albu‐Schäffer

2016

IEEE Robot. Autom. Lett.

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Standard industrial controllers of robot arms define constraints on the velocity, the acceleration, and the jerk and abort execution in case any of them is violated. In addition to satisfying these constraints, the presented method tries to generate a trajectory that is path-accurate, i.e., that exactly tracks the 6-dof shape of the desired path in axis space. Furthermore, the computed trajectory is as close as possible to the original robot program. This is reached by forward scaling and backtracking until a feasible trajectory is obtained. In contrast to previous work, blending of subsequent segments of the desired path is prevented by interpolation of the arc length instead of direct position interpolation. Because of its time-efficiency the algorithm can be applied in each sampling step, e.g. every 4 ms for a standard KUKA robot with RSI interface. Experimental results demonstrate the approach. This is the author's version of an article that has been published in this journal. Changes were made to this version by the publisher prior to publication.The final version of record is available at http://dx.

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Trajectory generation for immediate path-accurate jerk-limited stopping of industrial robots

Cited by 9 publications

References 17 publications

Learning Time-optimized Path Tracking with or without Sensory Feedback

Learning Time-optimized Path Tracking with or without Sensory Feedback

Iterative path-accurate trajectory generation for fast sensor-based motion of robot arms

Path-Accurate Online Trajectory Generation for Jerk-Limited Industrial Robots

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