A technique for time-jerk optimal planning of robot trajectories
By Alessandro Gasparetto, Vanni Zanotto. 2008
338 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition, Control and Systems Engineering
View Paper: https://www.semanticscholar.org/paper/1df6aaf0e4641ddd71ebed63a52f651ba459652e
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
In Control and Systems Engineering
- Mohamed Abderahim
- Fares J. Abu-Dakka
- Iyad F. Assad
- Rasha M. Alkhdour
- Leonid B. Freidovich
- Yunong Zhang
- Hassan K. Khalil
Citations in the corpus
- Minimum jerk norm scheme applied to obstacle avoidance of redundant robot arm with jerk bounded and feedback control (2016, background): “Besides, low-jerk trajectories can be executed more rapidly and accurately [8].”
- Increasing the Level of Automation in the Forestry Logging Process with Crane Trajectory Planning and Control (2014, methodology): “…is a computationally efficient method that yields optimal solutions based on performance specifications and different constraints (Antonelli, Chiaverini, Curatella, and Marino, 2009; Bobrow, Dubowsky, and Gibson, 1985; Gasparetto and Zanotto, 2008; Pfeiffer and Johanni, 1987; Shin and McKay, 1985).”
- Statistical evaluation of an evolutionary algorithm for minimum time trajectory planning problem for industrial robots (2017, methodology): “These techniques are: i the Sequential Quadratic Program (SQP) using cu- bic spline [17], ii the Harmony Search algorithm (HS) [48], iii interval analysis using cubic spline [33], iv trigonometric spline [47], and v synchronized trigonometric S-curve [30].”