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Original Article
Yanbiao Li
College of Mechanical Engineering
Corresponding Author
ORCiD: https://orcid.org/0000-0001-9768-0687
License:
This work is licensed under a CC BY 4.0 License. Read Full License
A multi-objective trajectory planning approach based on an improved elitist non-dominated sorting genetic algorithm (INSGA-II) is proposed. Trajectory function is planned with a new composite polynomial that by combining of quintic polynomials with cubic Bezier curves. Then, an INSGA-II, by introducing three genetic operators: ranking group selection (RGS), direction-based crossover (DBX) and adaptive precision-contrallable mutation (APCM), is developed to optimize travelling time and torque fluctuation. Inverted generational distance, hypervolume and optimizer overhead are selected to evaluate the convergence, diversity and computational effort of algorithms. The optimal solution is determined via fuzzy comprehensive evaluation to obtain the optimal trajectory. Taking a serial-parallel hybrid manipulator as instance, the velocity and acceleration profiles obtained using this composite polynomial are compared with those obtained using a quintic B-spline method. The effectiveness and practicability of the proposed method are verified by simulation results.
Keywords
hybrid manipulator, bezier curve, improved optimization algorithm, trajectory planning, multi-objective optimization
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CURRENT STATUS: Published
View the published article at Chinese Journal of Mechanical Engineering.
Version 1
Posted 03 Dec, 2020
No community comments so far
Editorial decision: Minor revision
On 21 Aug, 2021
Review #2 received
Received 08 Mar, 2021
Reviewer #2 agreed
On 07 Feb, 2021
Review #1 received
Received 28 Jan, 2021
Reviewers invited
Invitations sent on 03 Jan, 2021
Reviewer #1 agreed
On 03 Jan, 2021
First submitted
On 26 Nov, 2020
Editor assigned
On 26 Nov, 2020
Submission checks complete
On 26 Nov, 2020
Editor invited
On 26 Nov, 2020
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Subject Areas
Mechanical Engineering
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A new preprint design is coming!
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See the published version of this preprint at Chinese Journal of Mechanical Engineering.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Original Article
Yanbiao Li
College of Mechanical Engineering
Corresponding Author
ORCiD: https://orcid.org/0000-0001-9768-0687
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
View the published article at Chinese Journal of Mechanical Engineering.
Version 1
Posted 03 Dec, 2020
No community comments so far
Editorial decision: Minor revision
On 21 Aug, 2021
Review #2 received
Received 08 Mar, 2021
Reviewer #2 agreed
On 07 Feb, 2021
Review #1 received
Received 28 Jan, 2021
Reviewers invited
Invitations sent on 03 Jan, 2021
Reviewer #1 agreed
On 03 Jan, 2021
First submitted
On 26 Nov, 2020
Editor assigned
On 26 Nov, 2020
Submission checks complete
On 26 Nov, 2020
Editor invited
On 26 Nov, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Mechanical Engineering
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at Chinese Journal of Mechanical Engineering.
A multi-objective trajectory planning approach based on an improved elitist non-dominated sorting genetic algorithm (INSGA-II) is proposed. Trajectory function is planned with a new composite polynomial that by combining of quintic polynomials with cubic Bezier curves. Then, an INSGA-II, by introducing three genetic operators: ranking group selection (RGS), direction-based crossover (DBX) and adaptive precision-contrallable mutation (APCM), is developed to optimize travelling time and torque fluctuation. Inverted generational distance, hypervolume and optimizer overhead are selected to evaluate the convergence, diversity and computational effort of algorithms. The optimal solution is determined via fuzzy comprehensive evaluation to obtain the optimal trajectory. Taking a serial-parallel hybrid manipulator as instance, the velocity and acceleration profiles obtained using this composite polynomial are compared with those obtained using a quintic B-spline method. The effectiveness and practicability of the proposed method are verified by simulation results.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
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