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Trajectory Planning Based on Non-Convex Global Optimization for Serial Manipulators
School of Automation Science and Electrical Engineering, Beihang University Beijing, China.ORCID iD: 0000-0003-2474-7451
School of Automation Science and Electrical Engineering, Beihang University Beijing, China.
Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Italy.
Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Italy.
2020 (English)In: Applied Mathematical Modelling, ISSN 0307-904X, E-ISSN 1872-8480, Vol. 84, p. 89-105Article in journal (Refereed) Published
Abstract [en]

To perform specific tasks in dynamic environments, robots are required to rapidly update trajectories according to changing factors. A continuous trajectory planning methodology for serial manipulators based on non-convex global optimization is presented in this paper. First, a kinematic trajectory planning model based on non-convex optimization is constructed to balance motion rapidity and safety. Then, a model transformation method for the non-convex optimization model is presented. In this way, the accurate global solution can be obtained with an iterative solver starting from arbitrary initializations, which can greatly improve the computational accuracy and efficiency. Furthermore, an efficient initialization method for the iterative solver based on multivariable-multiple regression is presented, which further speeds up the solution process. The results show that trajectory planning efficiency is significantly enhanced by model transformation and initialization improvement for the iterative solver. Consequently, real-time continuous trajectory planning for serial manipulators with many degrees of freedom can be achieved, which lays a basis for performing dynamic tasks in complex environments.

Place, publisher, year, edition, pages
Elsevier, 2020. Vol. 84, p. 89-105
Keywords [en]
Real-time trajectory planning, Non-convex optimization, Global optimization, Machine learning, Robotics
National Category
Engineering and Technology Robotics
Identifiers
URN: urn:nbn:se:oru:diva-81353DOI: 10.1016/j.apm.2020.03.004Scopus ID: 2-s2.0-85083184469OAI: oai:DiVA.org:oru-81353DiVA, id: diva2:1426434
Available from: 2020-04-26 Created: 2020-04-26 Last updated: 2020-04-28Bibliographically approved

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Zhang, Shiyu

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CiteExportLink to record
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