Multiobjective optimization-based trajectory planning for laser 3D scanner robots

被引：0
作者：
Huang, Yumeng [1 ]
Liu, Guangyu [1 ]
Yu, Wujia [1 ]
Yu, Shanen [1 ]
机构：
[1] Hangzhou Dianzi Univ, Sch Automat, Baiyang St, Hanzghou 310018, Zhejiang, Peoples R China
来源：
INTERNATIONAL JOURNAL OF INTELLIGENT ROBOTICS AND APPLICATIONS | 2024年 / 8卷 / 04期
基金：
中国国家自然科学基金;
关键词：
Trajectory planning; Multi-objective optimization; B-spline interpolation; Industrial robots; Kinematic constraints4; ALGORITHM;
D O I：
10.1007/s41315-024-00357-8
中图分类号：
TP24 [机器人技术];
学科分类号：
080202 ; 1405 ;
摘要：
In our industrial material defect detecting processes, the multi criteria is considered in two-level motion planning structure. Firstly, the feed speed of the end-effector should be programmed in optimal time for satisfying the requirement of high efficiency. Secondly, the planned joint velocities and accelaration are characterized by high-order derivatives to guarantee smooth motion, taking into account the kinematic constraints. Last but not least, energy consumption of the robot's movement is a focus during designing trajectories. The Pareto optimal method is applied to solve the trajectory planning problem. The results of the experiments suggest that the Pareto approach can realize effective multi-objective optimization and deliver a group of Pareto solutions for decision makers. Based on the actual requirements, suitable Pareto-optimal trajectory can be achieved and the practical operation of the industrial robot is good.
引用
收藏
页码：993 / 1007
页数：15
相关论文
共 37 条
[1]  
Boscariol R.P., 2020, INT C IFTOMM ITALY, P318
[2]   Tolerance Synthesis of Delta-like Parallel Robots Using a Nonlinear Optimisation Method [J].
Brahmia, Allaoua ;
Kerboua, Adlen ;
Kelaiaia, Ridha ;
Latreche, Ameur .
APPLIED SCIENCES-BASEL, 2023, 13 (19)
[3]  
Butler S.D., 2017, THESIS RICE U
[4]  
Craig J. J., 1986, Introduction to Robotics: Mechanics and Control
[5]  
de Boor Carl., 1978, A Practical Guide to Splines, DOI DOI 10.1007/978-1-4612-6333-3
[6]   A fast and elitist multiobjective genetic algorithm: NSGA-II [J].
Deb, K ;
Pratap, A ;
Agarwal, S ;
Meyarivan, T .
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2002, 6 (02) :182-197
[7]  
Deb K., 2016, Multi-Objective Optimization, VDecision Sciences, P161
[8]   Convex time-optimal robot path following with Cartesian acceleration and inertial force and torque constraints [J].
Debrouwere, Frederik ;
Van Loock, Wannes ;
Pipeleers, Goele ;
Diehl, Moritz ;
Swevers, Jan ;
De Schutter, Joris .
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART I-JOURNAL OF SYSTEMS AND CONTROL ENGINEERING, 2013, 227 (10) :724-732
[9]   Development of the Multi-Objective Adaptive Guided Differential Evolution and optimization of the MO-ACOPF for wind/PV/tidal energy sources [J].
Duman, Serhat ;
Akbel, Mustafa ;
Kahraman, Hamdi Tolga .
APPLIED SOFT COMPUTING, 2021, 112
[10]  
Elber G., 1992, GRAPHICS GEMS 3 IBM, P133, DOI [10.1016/B978-0-08-050755-2.50037-3, DOI 10.1016/B978-0-08-050755-2.50037-3]
1234