Ming Cong

Kinematic Model and Analysis of an Actuation Redundant Parallel Robot With Higher Kinematic Pairs for Jaw Movement

A jaw movement robot that can simulate jaw movement and reaction forces in temporomandibular joints (TMJs) of a man will find many applications in dentistry, food science, and biomechanics. The TMJ is the most sophisticated joint in the human body, and its compound movements are not given sufficient consideration when a jaw robot is designed. Based on the biological finding about the mastication system and its motion characteristics, this paper proposes an actuation redundant parallel mechanism for the jaw movement robot and designs the actuation systems and models the TMJ in a higher pair kinematic joint. The prototype of the proposed jaw movement robot is presented, consisting of six prismatic-universal-spherical linkages for muscle groups of mastication and two point contacts for left and right TMJs. This robot has four degrees of freedom but is driven by six actuators. Each prismatic-universal-spherical linkage is made up of a rotary motor, a prismatic joint, a universal joint, and a spherical joint. The closed-form solution to the kinematics is found. This novel robot is evaluated by simulations of kinematics, workspace, and a chewing movement experiment.

Robotic cognitive behavior control based on biology-inspired Episodic memory

This paper proposes a framework called Episodic memory-driving Markov decision processes (EM-MDPs) for incremental self-learning of robotic experience and cognitive behavior control under uncertainty. The framework simulates the organization process of episodic memory by introducing the neuron stimulation mechanism. Firstly, episode model is built, and the activation and stimulation mechanism of state neurons is proposed based on cognitive neuroscience. Secondly, episodic self-learning is also proposed by utilizing sparse distributed memory (SDM) through Hebbian rules, to realize memory real-time storage, incremental accumulation and integration. Finally, a robotic cognitive behavior control approach is established. Neuron synaptic potential is introduced for event localization. Robot can evaluate the past events sequence, predict the current state and plan the desired behavior. Two main challenges in robot behavior control under uncertainty are addressed in the paper: high computational complexity and perceptual aliasing. The proposed system is evaluated in several real life environments for mobile robot. The applicability and the usefulness of the developed method are validated by the results obtained.

Time-Jerk Synthetic Optimal Trajectory Planning of Robot Based on Fuzzy Genetic Algorithm

A new approach based on fuzzy genetic algorithm is developed to find the time-jerk synthetic optimal trajectory of robot with a joint space scheme using cubic splines. In order to get the optimal trajectory, cubic splines are employed and derived under the constraint condition of velocity and acceleration of the first and last point, which assure overall continuity of velocity and accelerations on robot motion. Based on cubic splines, the mathematic model of time-jerk synthetic optimal trajectory planning is built taking into account both the execution time and minimax approach of jerk with kinematics constraints expressed as upper bounds on the absolute values of velocity and acceleration. For solving the mathematic model, we designed the set of fuzzy control rules and fuzzy genetic algorithm, using real-coding and elitism approach. Finally, the proposed optimal technique is tested in simulation on a three-degrees-of-freedom glass substrate handling robot. The simulation results show the effectiveness of the algorithm to solve the contradictory problem between high production efficiency and low arm vibration.

A biological-inspired episodic cognitive map building framework for mobile robot navigation:

This article proposes a self-learning method of robotic experience for building episodic cognitive map using biologically inspired episodic memory. The episodic cognitive map is used for robot navigation under uncertainty. Two main challenges which include high computational complexity and perceptual aliasing are addressed. The episodic memory-driving Markov decision process is proposed to simulate the organization of episodic memory by introducing neuron activation and stimulation mechanism. Episodic memory self-learning model and algorithm are presented for building the episodic cognitive map based on episodic memory-driving Markov decision process. Uncertain information is considered to improve mapping performance. The presented method can realize robotic memory real-time storage, incremental accumulation, integration and updating. Based on the episodic cognitive map, the predicted episodic trajectory can simply be computed by activation spreading of state neurons. The experimental results for a mobile robot indicate that the method can efficiently performs learning, localization, mapping and navigation in real-life office environments.

Coaxial twin-shaft magnetic fluid seals applied in vacuum wafer-handling robot

Compared with traditional mechanical seals, magnetic fluid seals have unique characters of high airtightness, minimal friction torque requirements, pollution-free and long life-span, widely used in vacuum robots. With the rapid development of Integrate Circuit (IC), there is a stringent requirement for sealing wafer-handling robots when working in a vacuum environment. The parameters of magnetic fluid seals structure is very important in the vacuum robot design. This paper gives a magnetic fluid seal device for the robot. Firstly, the seal differential pressure formulas of magnetic fluid seal are deduced according to the theory of ferrohydrodynamics, which indicate that the magnetic field gradient in the sealing gap determines the seal capacity of magnetic fluid seal. Secondly, the magnetic analysis model of twin-shaft magnetic fluid seals structure is established. By analyzing the magnetic field distribution of dual magnetic fluid seal, the optimal value ranges of important parameters, including parameters of the permanent magnetic ring, the magnetic pole tooth, the outer shaft, the outer shaft sleeve and the axial relative position of two permanent magnetic rings, which affect the seal differential pressure, are obtained. A wafer-handling robot equipped with coaxial twin-shaft magnetic fluid rotary seals and bellows seal is devised and an optimized twin-shaft magnetic fluid seals experimental platform is built. Test result shows that when the speed of the two rotational shafts ranges from 0–500 r/min, the maximum burst pressure is about 0.24 MPa. Magnetic fluid rotary seals can provide satisfactory performance in the application of wafer-handling robot. The proposed coaxial twin-shaft magnetic fluid rotary seal provides the instruction to design high-speed vacuum robot.

Modeling and simulation of masticatory robot

To design a masticatory robot with accurate reproduction of jaw movement for a range of applications, such as dental training, jaw simulation, food texture analysis and speech therapy, this paper presents a mechanism model with fuzzy-PID control and simulates the model in Matlab/Simulink. The mechanism model of masticatory robot is founded according to the biological structure of masticatory system by SimMechanics. The fuzzy-PID controller is designed via Fuzzy Logic Toolbox to control the masticatory mechanism. The simulation results have showed that the movements of masticatory robot are similar to jaw movements.

Automatic path and trajectory planning for laser cladding robot based on CAD

The robot path planning for forming complex parts is one of the key techniques in coaxial laser cladding powder. In this paper, a method of automatic robot path generation from CAD (computer aided design) is proposed. The size information and topological information of the CAD model are extracted. The redundant vertexes are removed. The new vertexes are ordered and traversed by binary tree algorithm model. The topological relations between vertexes, edges and faces are established. The CAD model and the robot path are displayed with OpenGL. The comparison and analysis between contour planning and hyperbolic curve planning are completed. The simulation shows the feasibility and validity of the path generation based on CAD. A new way of automatic robot path generation from CAD model is given and the efficiency of the robot cladding is improved.

Predicting characteristic performance for arc welding process

Predicting characteristic performance of the arc welding process is an underlying task for online weld quality control. As a continuous yet complex high-thermal welding process, conventional modelling method can be ineffective in the presence of big uncertainties and noise. Besides, experiments and evaluations could be costly and very inefficient because of resource utilization, energy consumption, and dedicated human labor. Hence, we investigate the welding process modelling problem in this paper to understand how to tune the welding parameters to achieve the desired characteristic performance. Gaussian Process Regression, a non-parametric modeling technique, is exploited to model the relationship between the welding parameters and characteristic performance. Cross-validation method is employed to avoid overfitting problem. Gas tungsten arc welding experiments were performed and the experimental data were collected and utilized to validate the proposed modelling method. The predicted characteristic performance is compared with the original data and it shows that the proposed method can accurately predict the weld bead geometry. This paper opens a door for online parameter tuning to achieve optimal performance, hence the proposed method will innovate the welding process.

GMM-based visual attention for target selection of indoor robotic tasks

Purpose – Indoor robotic tasks frequently specify objects. For these applications, this paper aims to propose an object-based attention method using task-relevant feature for target selection. The task-relevant feature(s) are deduced from the learned object representation in semantic memory (SM), and low dimensional bias feature templates are obtained using Gaussian mixture model (GMM) to get an efficient attention process. This method can be used to select target in a scene which forms a task-specific representation of the environment and improves the scene understanding by driving the robot to a position in which the objects of interest can be detected with a smaller error probability. Design/methodology/approach – Task definition and object representation in SM are proposed, and bias feature templates are obtained using GMM deduction for features from high dimension to low dimension. Mean shift method is used to segment the visual scene into discrete proto-objects. Given a task-specific object, the top...

A Study of Magnetic Fluid Rotary Seals for Wafer Handling Robot

Magnetic fluid rotary seals have been shown to be effective in machinery operating in a vacuum chamber. Such seals have the advantages of simple design, zero leakage at almost any rotation speed and low friction. This paper presents the principle of magnetic fluid rotary seals technology. The seal differential pressure formulas of magnetic fluid rotary seals is deduced and obtained. The magnetic field distribution of the sealing gap flux densities is calculated by finite elements method through solving the magnetic vector potential with appropriate boundary conditions. Based on the magnetic field calculations, the influence of sealing gap and tooth parameters on seal pressure differential is analyzed. A coaxial twin-shaft magnetic fluid seal apparatus used for wafer handling robot is presented. It is shown that using the most advanced magnetic fluid technology and seal design technology, magnetic fluid rotary seals can provide satisfactory performance in this challenging new application.