Torque limited path following by on-line trajectory time scaling

Nielsen, Lars; Dahl, Ola

doi:10.1109/robot.1989.100131

Cited by 37 publications

(78 citation statements)

References 5 publications

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“…The problem of generating time-optimal, dynamicallyfeasible trajectory of a given path without considering contact was initially solved by Bobrow et al [4] and Shin and McKay [26] and later enhanced by [7,8,18,27]. Recent work has extended the time-scaling problem to handle frictional contact [15].…”

Section: Related Workmentioning

confidence: 99%

Robust Trajectory Optimization Under Frictional Contact with Iterative Learning

Luo

Hauser²

2015

Robotics: Science and Systems XI

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Abstract-Optimization is often difficult to apply to robots due to the presence of modeling errors, which may cause constraints to be violated during execution on a real robot. This work presents a method to optimize trajectories with large modeling errors using a combination of robust optimization and parameter learning. In particular it considers the problem of computing a dynamically-feasible trajectory along a fixed path under frictional contact, where friction is uncertain and actuator effort is noisy. It introduces a robust time-scaling method that is able to accept confidence intervals on uncertain parameters, and uses a convex parameterization that allows dynamically-feasible motions under contact to be computed in seconds. This is combined with an iterative learning method that uses feedback from execution to learn confidence bounds on modeling parameters. Experiments on a manipulator performing a "waiter" task, on which an object is moved on a carried tray as quickly as possible, demonstrate this method can compensate for modeling uncertainties within a handful of iterations.

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Section: Related Workmentioning

confidence: 99%

Robust Trajectory Optimization Under Frictional Contact with Iterative Learning

Luo

Hauser²

2015

Robotics: Science and Systems XI

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“…(Pfeiffer & Johanni, 1987)) and also will be necessary in the results exposed in this chaper. The motion specification is completed by specifying a timing law (Hollerbach, 1984): (Dahl, 1990).…”

Section: D Qs Dsmentioning

confidence: 99%

“…The path acceleration σ in equation (11) is expressed in terms of the time-scaling factor () ct , the parameter () st , and successive time derivatives. In this way, instead of constraining the path acceleration () t σ , as done in (Dahl & Nielsen, 1990;Dahl, 1992), for enforcing the control torque into the admissible limits, it is possible to constrain the scaling acceleration () ct using the limits of torque input for each joint i , that is to say …”

Section: Tracking Control Of On-line Time-scaled Reference Trajectoriesmentioning

confidence: 99%

“…It is noteworthy that this approach has been extended to the cases of multiple robots in cooperative tasks (Moon & Ahmad, 1991) and robot manipulators with elastic joints (De Luca & Farina, 2002). In order to tackle the drawback of the assumption that the robot model in exactly known, Dahl and Nielsen (1990) proposed a control algorithm that result in the tracking of a time-scaled trajectory obtained from a specified geometric path and a modified on-line velocity profile. The method considers an internal loop that limits the slope of the path velocity when the torque input is saturated.…”

Section: Introductionmentioning

confidence: 99%

“…This is achieved through the control of a time-scaling factor, which is motivated by the ideas introduced by Hollerbach (1984). The new method does not require the specification of a velocity profile, as in (Dahl & Nielsen, 1990;Dahl, 1992;Arai et al, 1994). Instead, we formulate the problem departing from the specification of a desired path and a desired timing law.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Robot Control Using On-Line Modification of Reference Trajectories

Moreno–Valenzuela¹

2008

Robot Manipulators

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Dimensional analysis for robust control of planar vehicle dynamics

Hencey

Alleyne

2007

Intl J Robust & Nonlinear

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SUMMARYMost engineered systems with similar functions, which are designed under strict external constraints, share similar dynamics. Using a dimensionless model as system representation, a dimensionless robust controller can be designed and implemented for a class of dynamically similar systems that are different in physical dimension. Dimensionless transformations of timescale, inputs and outputs determine a nominal plant model and plant-to-plant uncertainties in a dimensionless form. Using parameter-dependent normalization, a normalized dimensionless model can be derived that has lower levels of plant-to-plant uncertainty than previous formulations (IEEE Trans. Contr. Syst. Technol. 2005; 13(4):624-630). The benefit of this dimensional analysis is demonstrated by the dynamic analysis of different types of planar vehicle systems. Numerical results show that, by using dimensional analysis, it is possible to obtain a controller that is robust to plant-to-plant parametric uncertainties among specific classes of systems designed with widely varying physical dimensions.

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Torque limited path following by on-line trajectory time scaling

Cited by 37 publications

References 5 publications

Robust Trajectory Optimization Under Frictional Contact with Iterative Learning

Robust Trajectory Optimization Under Frictional Contact with Iterative Learning

Robot Control Using On-Line Modification of Reference Trajectories

Dimensional analysis for robust control of planar vehicle dynamics

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