Simultaneous task placement and sequence optimization in an inspection robotic cell

Robotica ◽  
2021 ◽  
pp. 1-21
Author(s):  
MohammadHadi FarzanehKaloorazi ◽  
Ilian A. Bonev ◽  
Lionel Birglen
Keyword(s):  
Combinatorial Optimization ◽  
Cycle Time ◽  
Continuous Optimization ◽  
Optimization Method ◽  
Traveling Salesman Problems ◽  
Inspection Process ◽  
Serial Robot ◽  
Unpredictable Environment ◽  
Task Placement ◽  
Robot Configurations

Abstract In this article, we improve the efficiency of a turbine blade inspection robotic workcell. The workcell consists of a stationary camera and a 6-axis serial robot that is holding a blade and presenting different zones of the blade to the camera for inspection. The problem at hand consists of a 6-DOF (degree of freedom) continuous optimization of the camera placement and a discrete combinatorial optimization of the sequence of inspection poses (images). For each image, all robot configurations (up to eight) are taken into consideration. A novel combined approach is introduced, called blind dynamic particle swarm optimization (BD-PSO), to simultaneously obtain the optimal design for both domains. The objective is to minimize the cycle time of the inspection process, while avoiding any collisions. Even though PSO is vastly used in engineering problems, the novelty of our combinatorial optimization method is in its ability to be used efficiently in traveling salesman problems where the distances between the cities are unknown and subject to change. This highly unpredictable environment is the case of the inspection cell where the cycle time between two images will change for different camera placements.

Combinatorial Optimization Method Based on Evaluation of Proximate Optimality Using Correlation Coefficient

2015 ◽  
Vol 135 (4) ◽  
pp. 466-467 ◽  
Author(s):  
Masahide Morita ◽  
Hiroki Ochiai ◽  
Kenichi Tamura ◽  
Junichi Tsuchiya ◽  
Keiichiro Yasuda
Keyword(s):  
Combinatorial Optimization ◽  
Correlation Coefficient ◽  
Optimization Method

Optimization Method of Cycle Time in Signalized Intersection

CICTP 2017 ◽  
2018 ◽  
Author(s):  
Xiaotong Ma ◽  
Hong Chen ◽  
Danting Zhao ◽  
Siyu Yang ◽  
Zhanguo Song
Keyword(s):  
Cycle Time ◽  
Optimization Method ◽  
Signalized Intersection

Motion Planning of an Innovative Serial Robot for Energy Consumption Reduction With “On Fly” Pieces Grasping

2008 ◽  
Author(s):  
Andrea Menegolo ◽  
Roberto Bussola ◽  
Diego Tosi
Keyword(s):  
Energy Consumption ◽  
Motion Planning ◽  
Cycle Time ◽  
Moving Objects ◽  
Vision System ◽  
Rotation Velocity ◽  
Simulation Software ◽  
Serial Robot ◽  
Planning Algorithm ◽  
Point To Point

The following study deals with the on-line motion planning of an innovative SCARA like robot with unlimited joint rotations. The application field is the robotic interception of moving objects randomly distributed on a conveyor and detected by a vision system. A motion planning algorithm was developed in order to achieve a satisfactory cycle time and energy consumption. The algorithm is based on the evaluation of the inertial actions arisen in the robot structure during the pick and place motions and it aims to keep constant the rotation velocity of the first joint during the motion, the grasping and the discarding phases. Since the algorithm must be applied run time and the number of the reachable pieces can be high, a particular care was dedicated to the computational burden reduction. Subsequently to an analytic study of the kinematical constraints and the criteria definition for the choice of which piece to grasp, a devoted simulation software was developed. The software allows the control and the evaluation of the effects of all the main parameters on the system behavior and a comparison of the cycle time and the energy consumption between the proposed algorithm and a standard point-to-point motion strategy.

A survey of unsupervised learning in medical image registration

2022 ◽  
Vol 2 (1) ◽  
pp. 0-0
Keyword(s):  
Deep Learning ◽  
Image Registration ◽  
Unsupervised Learning ◽  
Medical Image ◽  
Continuous Optimization ◽  
Optimization Method ◽  
Clinical Applications ◽  
Future Research ◽  
Medical Image Registration ◽  
Short Time

Medical image registration has important value in actual clinical applications. From the traditional time-consuming iterative similarity optimization method to the short time-consuming supervised deep learning to today's unsupervised learning, the continuous optimization of the registration strategy makes it more feasible in clinical applications. This survey mainly focuses on unsupervised learning methods and introduces the latest solutions for different registration relationships. The registration for inter-modality is a more challenging topic. The application of unsupervised learning in registration for inter-modality is the focus of this article. In addition, this survey also proposes ideas for future research methods to show directions of the future research.

Concerted, Computing-Intense Novel MFL Approach Ensuring Reliability and Reducing the Need for Dig Verification

2020 ◽  
Author(s):  
Johannes Palmer ◽  
Aaron Schartner ◽  
Andrey Danilov ◽  
Vincent Tse
Keyword(s):  
Synthetic Data ◽  
Continuous Optimization ◽  
Optimization Method ◽  
Metal Loss ◽  
Empirical Formulas ◽  
Blind Test ◽  
High Data ◽  
Pure Number ◽  
Accuracy Increase ◽  
Pull Tests

Abstract Magnetic Flux Leakage (MFL) is a robust technology with high data coverage. Decades of continuous sizing improvement allowed for industry-accepted sizing reliability. The continuous optimization of sizing processes ensures accurate results in categorizing metal loss features. However, the identified selection of critical anomalies is not always optimal; sometimes anomalies are dug up too early or unnecessarily, this can be caused by the feature type in the field (true metal loss shape) being incorrectly identified which affects sizing and tolerance. In addition, there is the possibility for incorrectly identifying feature types causing false under-calls. Today, complex empirical formulas together with multifaceted lookup tables fed by pull tests, synthetic data, dig verifications, machine learning, artificial intelligence and last but not least human expertise translate MFL signals into metal loss assessments with high levels of success. Nevertheless, two important principal elements are limiting the possible MFL sizing optimization. One is the empirical character of the signal interpretation. The other is the implicitly induced data and result simplification. The reason to go this principal route for many years is simple: it is methodologically impossible to calculate the metal source geometry directly from the signals. In addition, the pure number of possible relevant geometries is so large that simplification is necessary and inevitable. Moreover, the second methodological reason is the ambiguity of the signal, which defines the target of metal loss sizing as the most probable solution. However, even under the best conditions, the most probable one is not necessarily the correct one. This paper describes a novel, fundamentally different approach as a basic alternative to the common MFL-analysis approach described above. A calculation process is presented, which overcomes the empirical nature of traditional approaches by using a result optimization method that relies on intense computing and avoids any simplification. Additionally, the strategy to overcome MFL ambiguity will be shown. Together with the operator, detailed blind-test examples demonstrate the enormous level of detail, repeatability and accuracy of this groundbreaking technological method with the potential to reduce tool tolerance, increase sizing accuracy, increase growth rate accuracy, and help optimize the dig program to target critical features with greater confidence.

scholarly journals A New Optimization Algorithm Based on Search and Rescue Operations

2019 ◽  
Vol 2019 ◽  
pp. 1-23 ◽  
Author(s):  
Amir Shabani ◽  
Behrouz Asgarian ◽  
Saeed Asil Gharebaghi ◽  
Miguel A. Salido ◽  
Adriana Giret
Keyword(s):  
Optimization Algorithm ◽  
Real World ◽  
Optimization Problems ◽  
Optimization Algorithms ◽  
Continuous Optimization ◽  
Optimization Method ◽  
Search And Rescue ◽  
Rank Test ◽  
Engineering Design Problems ◽  
Continuous Optimization Problems

In this paper, a new optimization algorithm called the search and rescue optimization algorithm (SAR) is proposed for solving single-objective continuous optimization problems. SAR is inspired by the explorations carried out by humans during search and rescue operations. The performance of SAR was evaluated on fifty-five optimization functions including a set of classic benchmark functions and a set of modern CEC 2013 benchmark functions from the literature. The obtained results were compared with twelve optimization algorithms including well-known optimization algorithms, recent variants of GA, DE, CMA-ES, and PSO, and recent metaheuristic algorithms. The Wilcoxon signed-rank test was used for some of the comparisons, and the convergence behavior of SAR was investigated. The statistical results indicated SAR is highly competitive with the compared algorithms. Also, in order to evaluate the application of SAR on real-world optimization problems, it was applied to three engineering design problems, and the results revealed that SAR is able to find more accurate solutions with fewer function evaluations in comparison with the other existing algorithms. Thus, the proposed algorithm can be considered an efficient optimization method for real-world optimization problems.

scholarly journals Continuous optimization and combinatorial optimization

2009 ◽  
Vol 5 (1) ◽  
pp. 1-2
Author(s):  
Liqun Qi ◽  
Li-Zhi Liao ◽  
Wenan Zang ◽  
Guanglu Zhou
Keyword(s):  
Combinatorial Optimization ◽  
Continuous Optimization
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