An Intelligent Inference System for Robot Hand Optimal Grasp Preshaping

Baysal, Cabbar Veysel; Erkmen, Aydan M.

doi:10.1080/18756891.2010.9727731

Cited by 5 publications

(2 citation statements)

References 9 publications

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“…Since the development of the first industrial robot in the early 1960s, this area of knowledge has progressively become interesting. Accordingly, research on such areas as robot grasping [1][2] , sensor [3][4] , robot arm [5][6] , etc., are gradually emerging. After more than 50 years of development, robots have entered all aspects of our lives.…”

Section: Background Of Intelligent Handling Robot Designmentioning

confidence: 99%

Designing an Intelligent Handling Robot for Performing Tasks Using QR Codes

Wang

2022

Proceedings of the 2022 International Conference on Smart Manufacturing and Material Processing (SMMP2022)

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With the increasing demand in intelligent manufacturing, an evolving prospective is anticipated for intelligent handling robots. Therefore, the present study aims at developing an intelligent handling robot which is capable of acquiring and fulfilling certain tasks from QR codes, such as handing objects with different shapes and colors. To this end, multiple photoelectric tubes were placed during the process. Subsequently, after ADC data acquisition for multiple times, channels were configured on a STM 32 board. Moreover, a photocell was connected with a single chip microcomputer. Accordingly, the values were obtained by photoelectric pair tubes. To identify QR codes, a camera module was installed. Furthermore, DC motors with encoders and a mechanical arm with steering gears were used as the source of power for the wheels and as the means of grabbing objects, respectively. At the same time, a universal algorithm was written to plan the robot’s walking routes.

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Section: Background Of Intelligent Handling Robot Designmentioning

confidence: 99%

Designing an Intelligent Handling Robot for Performing Tasks Using QR Codes

Wang

2022

Proceedings of the 2022 International Conference on Smart Manufacturing and Material Processing (SMMP2022)

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“…The tolerance of the compositional loops is divided into a, b, c, d, e, f, g, seven classes [9,10,11]. The tolerance of every class is shown in table.1.…”

Section: Simulation Of the Inject Performance For The Axial Tolerancementioning

confidence: 99%

Simulation of Assembly Tolerance and Characteristics of High Pressure Common Rail Injector

Lu¹,

Tang²,

Yong³

et al. 2011

IJCIS

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Fuel injector is the key part of a high-pressure common rail fuel injection system. Its manufacturing precision and assembly quality affect system's property and performance. According to the characteristics and demands of assembly of the fuel injector, an intelligent optimization algorithm is proposed to resolve the problem of assembly sequence planning. Based on geometric modeling, assembly dimension chain of the injector control chamber is established, and the relationship between assembly tolerance and volume change of control chamber is analyzed. The optimization model of the assembly is established. The impact of assembly tolerance on injector's performance is simulated according to the optimization algorithm. The simulation result shows that quantity of injection fuel changes correspondingly with the change of assembly tolerance, while injection rate and pressure do not change significantly, and the response rate of needle considerably slow. Similarly, the leakage rate of fuel in control chamber is calculated, indicating that the assembly tolerance has obvious impact on fuel leakage and its rate. The study illuminates that injector's assembly tolerance has prominent effect on injection.

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