- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Ant Colony Optimization and image model-based robot manipulator system for pick-and-place tasks
摘要： A visual servo control system combines with the model-based image segmentation and an Ant Colony Optimization (ACO) algorithm to design an excellent six-Degree-of-Freedom (6-DOF) robot manipulator for solving the complicated combinations of pick-and-place tasks. A simple but efficient vision-based segmentation methodology is developed to extract the object information by getting appropriate feature of the controlled platform when the robot is tracking the manipulated image patterns. The evolutionary ACO learning algorithm explores the near-optimal path selections to drive the 6 DOF robot arm kinematics model for completing the Pick-and-Place tasks as soon as possible. Inverse orientation kinematic machine is proposed to successfully guide the robot manipulator into the desired position. Several software simulations include image segmentations, the shortest path selection, and the performance validation in various experiments. These results are described and presented to demonstrate that the designed image model-based robot manipulator wins the excellent Pick-and-Place task. Not only the software simulation, the practical robot synchronously performed in real-world to reach the higher feasible functions in the eye-to-hand experiments.
关键词： image segmentation,pick-and-place task,Ant Colony Optimization,eye-to-hand,Robot manipulator
更新于2025-09-23 15:23:52
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[IEEE 2018 25th IEEE International Conference on Image Processing (ICIP) - Athens (2018.10.7-2018.10.10)] 2018 25th IEEE International Conference on Image Processing (ICIP) - A Fast Palette Reordering Technique Based on GPU-Optimized Genetic Algorithms
摘要： Color re-indexing is one of main approaches for improving the loss-less compression of color indexed images. Zero-order entropy reduction of indexes matrix is the key to obtain high compression ratio. However, obtaining the optimal re-indexed palette is a challenging problem that cannot be solved by brute-force approaches. In this paper we propose a novel re-indexing approach where the Travelling Salesman Problem is solved through Ant Colony Optimization. Our method is proved to achieve high quality results by outperforming state-of-art ones in terms of compression gain. Additionally, we exploit clustering and GPU computing to make our solution extremely fast.
关键词： Ant Colony Optimization,Data compression,Color,Entropy,Image coding
更新于2025-09-19 17:15:36
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[IEEE IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium - Yokohama, Japan (2019.7.28-2019.8.2)] IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium - Structural Optimization of Receiving System Based on Optimal Field of View for Shallow Sea Laser Measurement
摘要： Recently, mobile networking systems have been designed with more complexity of infrastructure and higher diversity of associated devices and resources, as well as more dynamical formations of networks, due to the fast development of current Internet and mobile communication industry. In such emerging mobile heterogeneous networks (HetNets), there are a large number of technical challenges focusing on the efficient organization, management, maintenance, and optimization, over the complicated system resources. In particular, HetNets have attracted great interest from academia and industry in deploying more effective solutions based on artificial intelligence (AI) techniques, e.g., machine learning, bio-inspired algorithms, fuzzy neural network, and so on, because AI techniques can naturally handle the problems of large-scale complex systems, such as HetNets towards more intelligent and automatic-evolving ones. In this paper, we discuss the state-of-the-art AI-based techniques for evolving the smarter HetNets infrastructure and systems, focusing on the research issues of self-configuration, self-healing, and self-optimization, respectively. A detailed taxonomy of the related AI-based techniques of HetNets is also shown by discussing the pros and cons for various AI-based techniques for different problems in HetNets. Opening research issues and pending challenges are concluded as well, which can provide guidelines for future research work.
关键词： ant colony optimization,self-organization networks,heterogeneous networks,genetic algorithms,Artificial intelligence
更新于2025-09-19 17:13:59
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[IEEE 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Chicago, IL, USA (2019.6.16-2019.6.21)] 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Line-to-Line Faults Detection for Photovoltaic Arrays Based on I-V Curve Using Pattern Recognition
摘要： In this paper, an identi?cation problem for nonlinear models is explored and a novel fuzzy identi?cation method based on the ant colony optimization algorithm is proposed. First, a modi?ed cluster validity criterion with a fuzzy c-regression model is adopted to ?nd appropriate rule numbers of the Takagi-Sugeno fuzzy model. Then, the ant colony optimization algorithm is adopted and the sifted initial membership function and the consequent parameters of the fuzzy model are obtained. Through an improved fuzzy c-regression model and the orthogonal least-squares method, the premise structure and the consequent parameters can be obtained to establish the Takagi-Sugeno fuzzy model. Some examples are illustrated to show that the proposed method provides better approximation results and robustness than those obtained using some of the existing methods.
关键词： Takagi-Sugeno fuzzy model,Fuzzy system identi?cation,ant colony optimization algorithm (ACO),fuzzy c-regression model
更新于2025-09-19 17:13:59
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Peak Detection Based on FPGA Using Quasi-Newton Optimization Method for Femtosecond Laser Ranging
摘要： In this paper, an identi?cation problem for nonlinear models is explored and a novel fuzzy identi?cation method based on the ant colony optimization algorithm is proposed. First, a modi?ed cluster validity criterion with a fuzzy c-regression model is adopted to ?nd appropriate rule numbers of the Takagi-Sugeno fuzzy model. Then, the ant colony optimization algorithm is adopted and the sifted initial membership function and the consequent parameters of the fuzzy model are obtained. Through an improved fuzzy c-regression model and the orthogonal least-squares method, the premise structure and the consequent parameters can be obtained to establish the Takagi-Sugeno fuzzy model. Some examples are illustrated to show that the proposed method provides better approximation results and robustness than those obtained using some of the existing methods.
关键词： ant colony optimization algorithm (ACO),Takagi-Sugeno fuzzy model,fuzzy c-regression model,Fuzzy system identi?cation
更新于2025-09-19 17:13:59
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[IEEE 2019 34th Symposium on Microelectronics Technology and Devices (SBMicro) - Sao Paulo, Brazil (2019.8.26-2019.8.30)] 2019 34th Symposium on Microelectronics Technology and Devices (SBMicro) - Realistic Simulations and Design of GaAs Solar Cells produced by Molecular Beam Epitaxy
摘要： Recently, mobile networking systems have been designed with more complexity of infrastructure and higher diversity of associated devices and resources, as well as more dynamical formations of networks, due to the fast development of current Internet and mobile communication industry. In such emerging mobile heterogeneous networks (HetNets), there are a large number of technical challenges focusing on the efficient organization, management, maintenance, and optimization, over the complicated system resources. In particular, HetNets have attracted great interest from academia and industry in deploying more effective solutions based on artificial intelligence (AI) techniques, e.g., machine learning, bio-inspired algorithms, fuzzy neural network, and so on, because AI techniques can naturally handle the problems of large-scale complex systems, such as HetNets towards more intelligent and automatic-evolving ones. In this paper, we discuss the state-of-the-art AI-based techniques for evolving the smarter HetNets infrastructure and systems, focusing on the research issues of self-configuration, self-healing, and self-optimization, respectively. A detailed taxonomy of the related AI-based techniques of HetNets is also shown by discussing the pros and cons for various AI-based techniques for different problems in HetNets. Opening research issues and pending challenges are concluded as well, which can provide guidelines for future research work.
关键词： ant colony optimization,self-organization networks,heterogeneous networks,genetic algorithms,Artificial intelligence
更新于2025-09-19 17:13:59
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Multielectron Effect for High-Order Harmonic Generation From Molecule Irradiated by Bichromatic Counter-Rotating Circularly Polarized Laser Pulses
摘要： In this paper, an identi?cation problem for nonlinear models is explored and a novel fuzzy identi?cation method based on the ant colony optimization algorithm is proposed. First, a modi?ed cluster validity criterion with a fuzzy c-regression model is adopted to ?nd appropriate rule numbers of the Takagi-Sugeno fuzzy model. Then, the ant colony optimization algorithm is adopted and the sifted initial membership function and the consequent parameters of the fuzzy model are obtained. Through an improved fuzzy c-regression model and the orthogonal least-squares method, the premise structure and the consequent parameters can be obtained to establish the Takagi-Sugeno fuzzy model. Some examples are illustrated to show that the proposed method provides better approximation results and robustness than those obtained using some of the existing methods.
关键词： fuzzy c-regression model,Fuzzy system identi?cation,ant colony optimization algorithm (ACO),Takagi-Sugeno fuzzy model
更新于2025-09-19 17:13:59
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[IEEE 2018 IEEE 16th Intl Conf on Dependable, Autonomic and Secure Computing, 16th Intl Conf on Pervasive Intelligence and Computing, 4th Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech) - Athens, Greece (2018.8.12-2018.8.15)] 2018 IEEE 16th Intl Conf on Dependable, Autonomic and Secure Computing, 16th Intl Conf on Pervasive Intelligence and Computing, 4th Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech) - Road Extraction from High-Resolution Remotely Sensed Image Based on Improved Ant Colony Optimization Method
摘要： Based on improved ant colony optimization method, this paper presents a new developed approach to extract road from high-resolution panchromatic remotely sensed image. The former original ant colony optimization is used to extract the road network and its detail information is not exact. This paper improved the deployment of ants and the heuristic function to extract the complete road information. With the guidance of the neighbor gray level, ants are deployed at the edge of the image and move forward for reaching the opposite side. As ants spread pheromone along their paths, roads can be extracted based on aggregated pheromone levels. This paper analyzed the practicality of this method by measuring the correctness and completeness of the extracted roads. The experimental results prove that the improved ant colony optimization method can improve the quality of the extracted roads by raising the road’s correctness and retaining the road’s completeness.
关键词： ant colony optimization,pheromone,high-resolution remotely sensed image,road extraction
更新于2025-09-11 14:15:04