Intelligent gripper design and application for automated part recognition and gripping
- Authors: Wang, Jianqiang
- Date: 2002
- Subjects: Automatic control , Robots, Industrial , Robotics
- Language: English
- Type: Thesis , Doctoral , DTech (Engineering)
- Identifier: vital:10816 , http://hdl.handle.net/10948/102 , Automatic control , Robots, Industrial , Robotics
- Description: Intelligent gripping may be achieved through gripper design, automated part recognition, intelligent algorithm for control of the gripper, and on-line decision-making based on sensory data. A generic framework which integrates sensory data, part recognition, decision-making and gripper control to achieve intelligent gripping based on ABB industrial robot is constructed. The three-fingered gripper actuated by a linear servo actuator designed and developed in this project for precise speed and position control is capable of handling a large variety of objects. Generic algorithms for intelligent part recognition are developed. Edge vector representation is discussed. Object geometric features are extracted. Fuzzy logic is successfully utilized to enhance the intelligence of the system. The generic fuzzy logic algorithm, which may also find application in other fields, is presented. Model-based gripping planning algorithm which is capable of extracting object grasp features from its geometric features and reasoning out grasp model for objects with different geometry is proposed. Manipulator trajectory planning solves the problem of generating robot programs automatically. Object-oriented programming technique based on Visual C++ MFC is used to constitute the system software so as to ensure the compatibility, expandability and modular programming design. Hierarchical architecture for intelligent gripping is discussed, which partitions the robot’s functionalities into high-level (modeling, recognizing, planning and perception) layers, and low-level (sensing, interfacing and execute) layers. Individual system modules are integrated seamlessly to constitute the intelligent gripping system.
- Full Text:
- Date Issued: 2002
- Authors: Wang, Jianqiang
- Date: 2002
- Subjects: Automatic control , Robots, Industrial , Robotics
- Language: English
- Type: Thesis , Doctoral , DTech (Engineering)
- Identifier: vital:10816 , http://hdl.handle.net/10948/102 , Automatic control , Robots, Industrial , Robotics
- Description: Intelligent gripping may be achieved through gripper design, automated part recognition, intelligent algorithm for control of the gripper, and on-line decision-making based on sensory data. A generic framework which integrates sensory data, part recognition, decision-making and gripper control to achieve intelligent gripping based on ABB industrial robot is constructed. The three-fingered gripper actuated by a linear servo actuator designed and developed in this project for precise speed and position control is capable of handling a large variety of objects. Generic algorithms for intelligent part recognition are developed. Edge vector representation is discussed. Object geometric features are extracted. Fuzzy logic is successfully utilized to enhance the intelligence of the system. The generic fuzzy logic algorithm, which may also find application in other fields, is presented. Model-based gripping planning algorithm which is capable of extracting object grasp features from its geometric features and reasoning out grasp model for objects with different geometry is proposed. Manipulator trajectory planning solves the problem of generating robot programs automatically. Object-oriented programming technique based on Visual C++ MFC is used to constitute the system software so as to ensure the compatibility, expandability and modular programming design. Hierarchical architecture for intelligent gripping is discussed, which partitions the robot’s functionalities into high-level (modeling, recognizing, planning and perception) layers, and low-level (sensing, interfacing and execute) layers. Individual system modules are integrated seamlessly to constitute the intelligent gripping system.
- Full Text:
- Date Issued: 2002
Monitoring a diagnosis for control of an intelligent machining process
- Authors: Van Niekerk, Theo
- Date: 2001
- Subjects: Expert systems (Computer science) -- Industrial applications , System design
- Language: English
- Type: Thesis , Doctoral , DTech (Engineering)
- Identifier: vital:10814 , http://hdl.handle.net/10948/70 , Expert systems (Computer science) -- Industrial applications , System design
- Description: A multi-level modular control scheme to realize integrated process monitoring, diagnosis and control for intelligent machining is proposed and implemented. PC-based hardware architecture to manipulate machining process cutting parameters, using a PMAC interface card as well as sensing processes performance parameters through sampling, and processing by means of DSP interface cards is presented. Controller hardware, to interface the PC-based PMAC interface card to a machining process for the direct control of speed, feed and depth of cut, is described. Sensors to directly measure on-line process performance parameters, including cutting forces, cutting sound, tool-workpiece vibration, cutting temperature and spindle current are described. The indirect measurement of performance parameter surface roughness and tool wear monitoring, through the use of NF sensor fusion modeling, is described and verified. An object based software architecture, with corresponding user interfaces (using Microsoft Visual C++ Foundation Classes and implemented C++ classes for sending motion control commands to the PMAC and receiving processed on-line sensor data from the DSP) is explained. The software structure indicates all the components necessary for integrating the monitoring, diagnosis and control scheme. C-based software code executed on the DSP for real-time sampling, filtering and FFT processing of sensor signals, is explained. Making use of experimental data and regression analysis, analytical relationships between cutting parameters (independent) and each of the performance parameters (dependent) are obtained and used to simulate the machining process. A fuzzy relation that contains values determined from statistical data (indicating the strength of connection between the independent and dependent variables) is proposed. The fuzzy relation forms the basis of a diagnostic scheme that is able to intelligently determine which independent variable to change when a machining performance parameter exceeds control limits. The intelligent diagnosis scheme is extensively tested using the machining process simulation.
- Full Text:
- Date Issued: 2001
- Authors: Van Niekerk, Theo
- Date: 2001
- Subjects: Expert systems (Computer science) -- Industrial applications , System design
- Language: English
- Type: Thesis , Doctoral , DTech (Engineering)
- Identifier: vital:10814 , http://hdl.handle.net/10948/70 , Expert systems (Computer science) -- Industrial applications , System design
- Description: A multi-level modular control scheme to realize integrated process monitoring, diagnosis and control for intelligent machining is proposed and implemented. PC-based hardware architecture to manipulate machining process cutting parameters, using a PMAC interface card as well as sensing processes performance parameters through sampling, and processing by means of DSP interface cards is presented. Controller hardware, to interface the PC-based PMAC interface card to a machining process for the direct control of speed, feed and depth of cut, is described. Sensors to directly measure on-line process performance parameters, including cutting forces, cutting sound, tool-workpiece vibration, cutting temperature and spindle current are described. The indirect measurement of performance parameter surface roughness and tool wear monitoring, through the use of NF sensor fusion modeling, is described and verified. An object based software architecture, with corresponding user interfaces (using Microsoft Visual C++ Foundation Classes and implemented C++ classes for sending motion control commands to the PMAC and receiving processed on-line sensor data from the DSP) is explained. The software structure indicates all the components necessary for integrating the monitoring, diagnosis and control scheme. C-based software code executed on the DSP for real-time sampling, filtering and FFT processing of sensor signals, is explained. Making use of experimental data and regression analysis, analytical relationships between cutting parameters (independent) and each of the performance parameters (dependent) are obtained and used to simulate the machining process. A fuzzy relation that contains values determined from statistical data (indicating the strength of connection between the independent and dependent variables) is proposed. The fuzzy relation forms the basis of a diagnostic scheme that is able to intelligently determine which independent variable to change when a machining performance parameter exceeds control limits. The intelligent diagnosis scheme is extensively tested using the machining process simulation.
- Full Text:
- Date Issued: 2001
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