Multi-objective optimal design of a novel 6-DOF spray-painting robot

Robotica ◽  
2021 ◽  
pp. 1-15
Author(s):  
Jun Wu ◽  
Xiaojian Wang ◽  
Binbin Zhang ◽  
Tian Huang
Keyword(s):  
Optimal Design ◽  
Performance Index ◽  
Virtual Work ◽  
Kinematic Model ◽  
Spray Painting ◽  
Virtual Work Principle ◽  
Multi Objective ◽  
Pareto Optimal Set ◽  
Painting Robot ◽  
Optimal Set

Abstract This paper deals with the multi-objective optimal design of a novel 6-degree of freedom (DOF) hybrid spray-painting robot. Its kinematic model is obtained by dividing it into serial and parallel parts. The dynamic equation is formulated by virtual work principle. A performance index for evaluating the compactness of robot is presented. Taking compactness, motion/force transmissibility, and energy consumption as performance indices, the optimal geometric parameters of the robot are selected in the Pareto-optimal set by constructing a comprehensive performance index. This paper is very useful for the development of the spray-painting robot.

Optimal Design of the Three-Degree-of-Freedom Parallel Manipulator in a Spray-Painting Equipment

Robotica ◽  
2019 ◽  
Vol 38 (6) ◽  
pp. 1064-1081
Author(s):  
Guang Yu ◽  
Jun Wu ◽  
Liping Wang ◽  
Ying Gao
Keyword(s):  
Optimal Design ◽  
Parallel Manipulator ◽  
Optimization Design ◽  
Virtual Work ◽  
Kinematic Model ◽  
Geometrical Parameters ◽  
Parallel Mechanisms ◽  
Spray Painting ◽  
Conditioning Performance ◽  
Accuracy Performance

SUMMARYSpray-painting equipments are important for the automatic spraying of long conical objects such as rocket fairing. This paper proposes a spray-painting equipment that consists of a feed worktable, a gantry frame and two serial–parallel mechanisms and investigates the optimal design of PRR–PRR parallel manipulator in serial–parallel mechanisms. Based on the kinematic model of the parallel manipulator, the conditioning performance, workspace and accuracy performance indices are defined. The dynamic model is derived using virtual work principle and dynamic evaluation index is defined. The conditioning performance, workspace, accuracy performance and dynamic performance are involved in multi-objective optimization design to determine the optimal geometrical parameters of the parallel manipulator. Furthermore, the geometrical parameters of the gantry frame are optimized. An example is given to show how to determine these parameters by taking a long object with conical surface as painted object.

Multi-Objective Optimization Analysis of Motor Cooling System in Articulated Dump Truck

2011 ◽  
Vol 383-390 ◽  
pp. 4715-4720
Author(s):  
Yan Zhang ◽  
Yan Hua Shen ◽  
Wen Ming Zhang
Keyword(s):  
Field Distribution ◽  
Cooling System ◽  
Dump Truck ◽  
Water Cooling ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Pareto Optimal Set ◽  
Temperature And Pressure ◽  
Water Cooling System ◽  
Optimal Set

In order to ensure the reliable and safe operation of the electric driving motor of the articulated dump truck, water cooling system is installed for each motor. For the best performance of the water cooling system, not only the heat transfer should be enhanced to maintain the motor in relatively low temperature, but also the pressure drop in the water cooling system should be reduced to save energy by reducing the power consumption of the pump. In this paper, the numerical simulation of the cooling progress is completed and the temperature and pressure field distribution are obtained. The multi-objective optimization model is established which involves the cooling system structure, temperature field distribution and pressure field distribution. To improve the computational efficiency, the surrogate model of the simulation about the cooling process is established based on the Response Surface Methodology (RSM). After the multi-objective optimization, the Pareto optimal set is obtained. The proper design point, which could make the average temperature and pressure drop of the cooling system relative desirable, is chosen from the Pareto optimal set.

scholarly journals Multi-Objective Optimization of Impedance Parameters in a Prosthesis Test Robot

2015 ◽  
Author(s):  
Poya Khalaf ◽  
Hanz Richter ◽  
Antonie J. van den Bogert ◽  
Dan Simon
Keyword(s):  
Control System ◽  
Motion Tracking ◽  
Optimization Problem ◽  
Peak Load ◽  
Pareto Optimal ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Pareto Optimal Set ◽  
Rms Error ◽  
Optimal Set

We design a control system for a prosthesis test robot that was previously developed for transfemoral prosthesis design and test. The robot’s control system aims to mimic human walking in the sagittal plane. It has been seen in previous work that trajectory control alone fails to produce human-like forces. Therefore, we utilize an impedance controller to achieve reasonable tracking of motion and force simultaneously. However, these objectives conflict. Impedance control design can therefore be viewed as a multi-objective optimization problem. We use an evolutionary multi-objective strategy called Multi-Objective Invasive Weed Optimization (MOIWO) to design the impedance controller. The multi-objective optimization problem admits a set of equally valid alternative solutions known as the Pareto optimal set. We use a pseudo weight vector approach to select a single solution from the Pareto optimal set. Simulation results show that a solution that is selected for pure motion tracking performs very accurate motion tracking (RMS error of 0.06 cm) but fails to produce the desired forces (RMS error of 70% peak load). On the other hand, a solution that is selected for pure force tracking successfully tracks the desired force (RMS error of 12.7% peak load) at the expense of motion trajectory errors (RMS error of 4.5 cm).

Pareto Front Estimation for Decision Making

2014 ◽  
Vol 22 (4) ◽  
pp. 651-678 ◽  
Author(s):  
Ioannis Giagkiozis ◽  
Peter J. Fleming
Keyword(s):  
Pareto Front ◽  
Computational Cost ◽  
Pareto Optimal ◽  
Pareto Optimal Solutions ◽  
Number Of Solutions ◽  
Multi Objective ◽  
Pareto Optimal Set ◽  
Problem Instances ◽  
Optimal Set ◽  
Optimisation Algorithms

The set of available multi-objective optimisation algorithms continues to grow. This fact can be partially attributed to their widespread use and applicability. However, this increase also suggests several issues remain to be addressed satisfactorily. One such issue is the diversity and the number of solutions available to the decision maker (DM). Even for algorithms very well suited for a particular problem, it is difficult—mainly due to the computational cost—to use a population large enough to ensure the likelihood of obtaining a solution close to the DM's preferences. In this paper we present a novel methodology that produces additional Pareto optimal solutions from a Pareto optimal set obtained at the end run of any multi-objective optimisation algorithm for two-objective and three-objective problem instances.

scholarly journals A Decomposition-Based Interactive Method for Multi-Objective Evolutionary Algorithm

2012 ◽  
Author(s):  
Nguye Long ◽  
Bui Thu Lam
Keyword(s):  
Optimization Problems ◽  
Decision Makers ◽  
Optimization Process ◽  
Multi Objective Optimization ◽  
Interactive Method ◽  
Multi Objective ◽  
Pareto Optimal Set ◽  
Single Solution ◽  
Optimal Set

Multi-objectivity has existed in many real-world optimization problems. In most multi-objective cases, objectives are often conflicting, there is no single solution being optimal with regards to all objectives. These problems are called Multi-objective Optimization Problems (MOPs). To date, there have been al large number of methods for solving MOPs including evolutionary methods (namly Multi-objective Evolutionary Algorithms MOEAs). With the use of a population of solutions for searching. MOEAs are naturally suitable for approximating optimal solutions (called the Pareto Optimal Set (POS) or the efficient set). There has been a popular trend in MOEAs considering the role of Decision Makers (DMs) during the optimization process (known as the human-in-loop) for checking, analyzing the results and giving the preference to guide the optimization process. This is call the interactive method.

Collective intelligence approaches in interactive evolutionary multi-objective optimization

2020 ◽  
Vol 28 (1) ◽  
pp. 95-108 ◽  
Author(s):  
Daniel Cinalli ◽  
Luis Martí ◽  
Nayat Sanchez-Pi ◽  
Ana Cristina Bicharra Garcia
Keyword(s):  
Collective Intelligence ◽  
Real Life ◽  
Decision Makers ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Pareto Optimal Set ◽  
Trade Offs ◽  
Life Problems ◽  
Conflicting Objectives ◽  
Optimal Set

Abstract Evolutionary multi-objective optimization algorithms (EMOAs) have been successfully applied in many real-life problems. EMOAs approximate the set of trade-offs between multiple conflicting objectives, known as the Pareto optimal set. Reference point approaches can alleviate the optimization process by highlighting relevant areas of the Pareto set and support the decision makers to take the more confident evaluation. One important drawback of this approaches is that they require an in-depth knowledge of the problem being solved in order to function correctly. Collective intelligence has been put forward as an alternative to deal with situations like these. This paper extends some well-known EMOAs to incorporate collective preferences and interactive techniques. Similarly, two new preference-based multi-objective optimization performance indicators are introduced in order to analyze the results produced by the proposed algorithms in the comparative experiments carried out.

Multi-Criteria Optimal Design of Parallel Manipulators Based on Natural Frequency

2010 ◽  
Vol 29-32 ◽  
pp. 2435-2442 ◽  
Author(s):  
Bang Jun Lv ◽  
S.J. Zhu ◽  
J.F. Xing
Keyword(s):  
Optimal Design ◽  
Natural Frequency ◽  
Parallel Manipulators ◽  
Average Frequency ◽  
Pareto Optimal Set ◽  
Large Size ◽  
Matrix Condition Number ◽  
Dimensionally Homogeneous Jacobian Matrix ◽  
Matrix Condition ◽  
Optimal Set

The Stewart manipulator has characteristics of low natural frequency, high cost and large size which make it difficult to obtain optimum performance with high dynamic response. The lowest natural frequency in the total workspace and average of six frequencies at home configuration of Stewart manipulator are introduced as indices to evaluate dynamic stability. Multi-criteria optimal design based on genetic algorithm (GA) was presented synthetically considering the workspace requirement, lowest natural frequency, average frequency and global dimensionally homogeneous Jacobian matrix condition number. An optimal result was obtained through standard GA using penalty function and the Pareto-optimal set was also obtained through parallel selection method.

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