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ABSTRACT: This research is concerned with the design and analysis of a parallel kinematic manipulator (PKM) with three degrees of freedom (DOF). The proposed PKM combining the spatial rotational and translational degrees of freedom has varied advantages and good potential applications of materials handling. First, the static balancing of the parallel manipulator is investigated. The definition and methodology of static balancing are introduced. Two methods including adjusting kinematic parameters and counterweights are applied to the structure and the counterweights method leads to static balancing of the PKM. The conditions of static balancing are given. Then the dynamic model of the proposed PKM is deduced. It describes the relationship between the driving forces and the motion of the end-effector platform. Two approaches, the Newton-Euler and the Lagrange methods, are compared and the later one is selected to build the dynamic model of the 3-DOF tripod mechanism.
Robotics and Automation (ICRA), 2011 IEEE International Conference on; 06/2011
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Robotica. 01/2010; 28:349-357.
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Proceedings of the 48th IEEE Conference on Decision and Control, CDC 2009, combined withe the 28th Chinese Control Conference, December 16-18, 2009, Shanghai, China; 01/2009
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ABSTRACT: In this paper, the topic studied comprehensively is based on the constant stiffness mechanism of a clamp plate installed on a Parawrist robot and the optimization of the manipulatorpsilas functionality. Currently situated at the Ryerson University, the Parawrist robot is used for mold polishing applications to provide a smooth surface for a finished product. The content of this research focuses on improving the stiffness of the clamp plates previously installed on the Parawrist robot to counteract the out of plane movement of its legs when performing mold polishing operations. Finite element analysis is utilized as the tool to produce the results and obtain optimization design for the constant stiffness mechanism. The overall structure of the manipulator is also improved, and improvement techniques are established.
Information and Automation, 2008. ICIA 2008. International Conference on; 07/2008
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ABSTRACT: An EMG-driven arm wrestling robot (AWR) is being developed in our laboratories for the purposes of studying neuromuscular control of arm movements. The AWR arm have 2-DOF, integrated with mechanical arm, elbow/wrist force sensors, servo motor, encoder, 3-D MEMS accelerometer, and USB camera, is used to estimate tension developed by individual muscles based on recorded electromyograms (EMGs). The surface electromyographic signal form the upper limb is sampled from a real player in same conditions. By using the method of wavelet packet transformation (WPT) and auto regressive model (AR), the characteristics of EMG signals can be extracted. Artificial neural network is adopted to estimate the elbow joint torque. The effectiveness of the humanoid algorithm using torque control estimated via WRT and neural network is confirmed by experiments. The purpose of this paper is to describe the design objectives, fundamental components and implementation of our real-time, EMG-driven AWR arm.
Robotics and Biomimetics, 2006. ROBIO '06. IEEE International Conference on; 01/2007
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Advanced Intelligent Computing Theories and Applications. With Aspects of Theoretical and Methodological Issues, Third International Conference on Intelligent Computing, ICIC 2007, Qingdao, China, August 21-24, 2007, Proceedings; 01/2007
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I. J. Humanoid Robotics. 01/2007; 4:645-670.
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ABSTRACT: In this paper, we develop a novel robotic arm wrestling system integrated with mechanical arm, elbow/wrist force sensors, servo motor, encoder, 3-D MEMS accelerometer, and USB camera. The arm wrestling robot (AWR) is intended to play arm wrestling game with real human on a table for entertainment. The designing scenario of the prototype model's hardware is performed. Elbow/wrist force sensors, as a crucial device in the force sensing system, are described in details. Software is developed for device driven and interface. The surface electromyographic (EMG) signals from the upper limb are sampled when a real player competes with the force testing system. By using the method of wavelet packet transformation (WPT), the high-frequency noises can be eliminated effectively and the characteristics of EMG signals can be extracted. Artificial neural network is adopted to estimate the elbow joint torque. The effectiveness of the humanoid algorithm using torque control estimated via WRT and neural network is confirmed by experiments
Intelligent Robots and Systems, 2006 IEEE/RSJ International Conference on; 11/2006
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ABSTRACT: In this paper, we develop a novel digital-shot system for sensing the throwing force information of shot-put athletes in real time. The digital-shot has been designed and manufactured with the same external dimensions and weight as the normal shot for open females. The three axes integrated accelerometer, as a crucial device in the force sensing system, can acquire the kinetics data along three orthogonal directions with reasonably high accuracy. By using the method of wavelet transformation, the characteristics of acceleration signals during the shot-put period can be extracted. Artificial neural network is adopted to recognise the movement pattern in different phases of shot throwing. For supplying more effective training instruction, with the help of six axes ground reaction force measuring apparatus, high-speed photography and surface electromyographic signal remote measuring device etc, an integrated platform of shot-put athlete biomechanical information acquisition is constructed. Based on the fusion of multi-targets and multi-parameters information, the shot-put athletes coaching system is proposed, which not only can supply constructive guidance for athletes to improve their skills, but also provide a new research platform of motion human modeling.
Information Acquisition, 2006 IEEE International Conference on; 09/2006
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ABSTRACT: In this paper, the surface electromyographic (EMG) signals is acquired from the upper limb when the experimenter competes with the arm wrestling robot (AWR) which is integrated with mechanical arm, elbow/wrist force sensors, servo motor, encoder, 3D MEMS accelerometer, and USB camera. The arm wrestling robot (AWR) is intended to play arm wrestling game with human on a table with pegs for entertainment and human upper limbs muscle modeling. As the EMG signal is a measurement of the anatomical and physiological characteristic of the given muscle, the macroscopical movement patterns of the human body can be classified and recognized. By using the method of wavelet packet transformation (WPT), the high-frequency noises can be eliminated effectively and the characteristics of EMG signals can be extracted. Auto-regressive (AR) model is adopted to effectively simulate the stochastic and non-stationary time sequences using a series of AR coefficients with a typical order. Artificial neural network (ANN) is utilized to distinguish the different force levels and game grades in the scenario of arm-wrestling. To advance the training speed and accurate rate of the motion pattern classification, back-propagation (BP) neural network based on adaptive learning rate algorithm (ALR) is introduced. The advantage of ALR algorithm compared with standard BP algorithm is confirmed by experiments
Information Acquisition, 2006 IEEE International Conference on; 09/2006
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ABSTRACT: In this paper a novel prediction method of elbow torque from EMG signal using SVM is proposed. How to model the relations between EMG signals and various kinematical aspects of the movement behavior is a difficult problem in the researches of neurophysiology and biomechanics. Traditional prediction methods include using neural networks to model the relations. However, these methods suffer from several problems, such as local minima, the difficulty of the selection of the model, etc. To address these problems, support vector machine is adopted to construct the nonlinear model. The efficiency of our proposed method is proved by experiment results.
International Conference on Information Acquisition. 08/2006;
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ABSTRACT: Optimizing the system stiffness and dexterity of parallel manipulators by adjusting the geometrical parameters can be a difficult and time-consuming endeavor, especially when the variables are diverse and the objective functions are excessively complex. However, optimization techniques that are based on artificial intelligence approaches can be an effective solution for addressing this issue. Accordingly, this paper describes the implementation of genetic algorithms and artificial neural networks as an intelligent optimization tool for the dimensional synthesis of the spatial six degree-of-freedom (DOF) parallel manipulator. The objective functions of system stiffness and dexterity are derived according to kinematic analysis of the parallel mechanism. In particular, the neural network-based standard backpropagation learning algorithm and the Levenberg–Marquardt algorithm are utilized to approximate the analytical solutions of system stiffness and dexterity. Subsequently, genetic algorithms are derived from the objective functions described by the trained neural networks, which model various performance solutions. The multi-objective optimization (MOO) of performance indices is established by searching the Pareto-optimal frontier sets in the solution space. Consequently, the effectiveness of this method is validated by simulation.
Robotics and Computer-Integrated Manufacturing.
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ABSTRACT: Weightlifting is one of the most physically demanding sports. A successful lift is influenced by many factors. Among them, the ground reaction force (GRF) which is exerted on weight lifers during the motion process is crucial to the performance of a weight lifer, so the acquisition and analysis of GRF is very important to the scientific research of weightlifting. This paper describes GRF acquisition using a force platform and analysis by means of wavelet transform (WT) for the performance diagnosis of weight lifters. The differences of GRF between the skilled weight lifters and the learners are reported. The method allows to detect and to quantify details not easily perceivable by coaches through traditional techniques. By wavelet transform of the GRF, it is possible for biomechanics experts to analysis mechanical behaviours of athletes and to direct them to improve their skills. GRF acquisition and analysis provides a very important tool for modern athletes training.
International Conference on Information Acquisition.
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ABSTRACT: Kinetic information acquisition is paramount for the analysis and guidance of athletic sports. This paper presents a shockproof accelerometer-embedded shot, called digital-shot, developed for the acquisition of the multidimensional acceleration simultaneously from the motion of the shot-put for advanced training of field athletes. It has been designed and manufactured to meet the standard in external dimensions and weight specified by the International Association of Athletics Federations for open females. Using wavelet transformation, the characteristics of acceleration signals during the pushing phase can be extracted to provide constructive guidance for athletes to improve their skills. It also functions as a new platform for the development of advanced shot-put athletes training and coaching system and for the research of human motion modeling. The system has been validated in field experiment showing its feasibility, accuracy and effectiveness.
Sensors and Actuators A: Physical.
