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

Lange, Friedrich; Albu‐Schäffer, Alin

doi:10.1109/lra.2015.2506899

Cited by 23 publications

(26 citation statements)

References 22 publications

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“…An overshooting is then unavoidable. This has been shown in [35] where a force-sensor detects an unexpected contact during high speed motion. The chance of a path-accurate solution that meets all requirements increases with the span of time for which big accelerations or jerks can be predicted.…”

Section: Direct Scaling (Ds)mentioning

confidence: 84%

“…However, it is not assumed that the given points represent all sampled positions. A closer following of a desired path is achieved by [6,9,12,[23][24][25][26][27][28][29][30][31][32][33][34][35]). Ref.…”

Section: Previous Workmentioning

confidence: 99%

“…Unlike the other methods, Ref. [35] does not convert the kinematic constraints or the shape of the path to constraints on s, which then result in a velocity profile. Instead, it is proposed to predict future constraints and in doing so to modify the trajectory at the preceding time steps.…”

Section: Previous Workmentioning

confidence: 99%

“…Sect. 3 then reviews the Arc Length Interpolation (ALI) from [35]. New aspects for improving the performance are presented in Sect.…”

Section: Organization Of the Papermentioning

confidence: 99%

“…2.2. Instead of the direct position interpolation (DPI) used in [40], we now prefer the Arc Length Interpolation (ALI) as presented in [35].…”

Section: Arc Length Interpolation (Ali)mentioning

confidence: 99%

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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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Section: Direct Scaling (Ds)mentioning

confidence: 84%

Section: Previous Workmentioning

confidence: 99%

Section: Previous Workmentioning

confidence: 99%

“…Sect. 3 then reviews the Arc Length Interpolation (ALI) from [35]. New aspects for improving the performance are presented in Sect.…”

Section: Organization Of the Papermentioning

confidence: 99%

“…2.2. Instead of the direct position interpolation (DPI) used in [40], we now prefer the Arc Length Interpolation (ALI) as presented in [35].…”

Section: Arc Length Interpolation (Ali)mentioning

confidence: 99%

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

Lange

Albu-Schäffer

2016

Advanced Robotics

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Model predictive control of a collaborative manipulator considering dynamic obstacles

Krämer

Rösmann

Hoffmann

et al. 2020

Optim Control Appl Methods

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SummaryCollaborative robots have to adapt its motion plan to a dynamic environment and variation of task constraints. Currently, they detect collisions and interrupt or postpone their motion plan to prevent harm to humans or objects. The more advanced strategy proposed in this article uses online trajectory optimization to anticipate potential collisions, task variations, and to adapt the motion plan accordingly. The online trajectory planner pursues a model predictive control approach to account for dynamic motion objectives and constraints during task execution. The prediction model relates reference joint velocities to actual joint positions as an approximation of built‐in robot tracking controllers. The optimal control problem is solved with direct collocation based on a hypergraph structure, which represents the nonlinear program and allows to efficiently adapt to structural changes in the optimization problem caused by moving obstacles. To demonstrate the effectiveness of the approach, the robot imitates pick‐and‐place tasks while avoiding self‐collisions, semistatic, and dynamic obstacles, including a person. The analysis of the approach concerns computation time, constraint violations, and smoothness. It shows that after model identification, order reduction, and validation on the real robot, parallel integrators with compensation for input delays exhibit the best compromise between accuracy and computational complexity. The model predictive controller can successfully approach a moving target configuration without prior knowledge of the reference motion. The results show that pure hard constraints are not sufficient and lead to nonsmooth controls. In combination with soft constraints, which evaluate the proximity of obstacles, smooth and safe trajectories are planned.

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Proposition of On-Line Velocity Scaling Algorithm for Task Space Trajectories

Wojtyra

Woliński

2020

ROMANSY 23 - Robot Design, Dynamics and Control

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

Cited by 23 publications

References 22 publications

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

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

Model predictive control of a collaborative manipulator considering dynamic obstacles

Proposition of On-Line Velocity Scaling Algorithm for Task Space Trajectories

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