Xiangrong Xu

LIPSA Hand: A Novel Underactuated Hand with Linearly Parallel and Self-adaptive Grasp

This paper proposes a novel robotic hand which contains linearly parallel and self-adaptive grasping modes, called LIPSA hand. The LIPSA hand is composed of a common basement, two fingers, two motors and the transmission mechanism. The LIPSA hand contains three modes: linearly parallel (LIP) gripping mode, self-adaptive (SA) grasping mode and the LIP-SA hybrid grasp mode. One finger linearly parallel grip the object and the other finger self-adaptive grasp the object on the same time. It is the object’s position, size, shape and weight that the mode which the LIPSA hand chooses depends on. The LIPSA hand is wide range in grasp, light in weight, small in size and low in cost. The Analysis results show that the LIPSA hand is valid and can be used in the various environment.

A method of trajectory planning for Ground Mobile Robot based on ant colony algorithm

Intelligent robot such as Unmanned Ground Mobile Robot or Vehicle is a kind of automatic equipment without a manual operation to complete the task. This paper presents a method of trajectory planning for intelligent robots based on the ant colony algorithm under an environment with obstacles. The robot can autonomously avoid obstacles from the starting point to reach the target point during maneuver. The ant colony algorithm is an evolutionary algorithm that by simulating ants foraging in nature. By using ant colony algorithm, this paper gets an optimal or suboptimal motion trajectory and path from starting point to the target point in a static environment based on rasterization environment model.

Research of repeatable positioning accuracy of automatic optical inspection

It is very important that the beginning positioning in the inspection of PCB board in automated optical inspection. The accuracy of the original position influence the later checking result of the components in the PCB board, it is crucial to do research the positioning accuracy repeatedly. There are many factors affect the position accuracy. Which including different checking algorithm, the outside environment such as temperature, humidity, brightness and the size of PCB board. This article is mainly focus on the repeatedly process and results of positioning accuracy in two different algorithm between color segmentation algorithm and template matching algorithm, and then analyzing and discussing on these result.

Trajectory planning of Unmanned Aerial Vehicle based on A* algorithm

The trajectory planning of Unmanned Aerial Vehicle (UAV) and Aerial Robots generally refers to a series of optimization problems. This paper presents a method of trajectory planning and design for UAV based on the A* algorithm. Using grids to process the trajectory path planning of UAV under the environment with presence of obstacles, and then search the shortest path from the initial point to the target point based on rasterized environment using A* algorithm. The simulation of trajectory planning is implemented in the Microsoft Visual C++ 6.0 developing environment. Through the simulation of trajectory planning, it can be proved that UAV can find the shortest path from the initial point to the target point based on A* algorithm.

A method for path planning of autonomous robot using A∗ algorithm

This paper presents a method of trajectory planning and design for Unmanned Aerial Vehicle (UAV) based on the A* algorithm. Using grids to process the trajectory path planning of UAV under the environment with presence of obstacles, and then find the shortest path from the initial point to the target point based on rasterized environment using A* algorithm. The simulation of trajectory planning is realized in the Microsoft Visual C++ 6.0 developing environment. Through the simulation of trajectory planning, we can prove that UAV can find the shortest path from the initial point to the target point based on A* algorithm.

COSA-E hand: A coupled and self-adaptive hand with eccentric wheel mechanisms

This paper introduces a novel coupled and self-adaptive underactuated hand (COSA-E hand). COSA-E hand has 8 degrees of freedom and three fingers. It is more humanoid compared to traditional self-adaptive hands and more secure compared to traditional coupled hands. The hybrid mode is realized by the eccentric wheel and sliding chute mechanism. Geometric analysis gives the self-adaptive and coupled zones of the COSA-E hand. Force analysis shows the force distribution based on the situation and location of objects. Simulation results are given. Because of the reliability and adaptability of the COSA-E hand, it is suitable for a wide use.

A novel robot hand with the magneto-rheological fluid solidification

The conventional passively underactuated hand can self-adaptively grasp an object under the reaction force produced by other active joints or the grasped objects, but it may reject the object if the force disappears, and cannot grasp independently. In order to overcome this serious disadvantage, a novel kind design of the passively self-adaptive underactuated hand is proposed, called the magneto-rheological fluid (MRF) hand. The MRF can be instantaneously solidified while a fitful magnetic field being produced, and liquidized shortly after the magnetic field disappearing. Based on this characteristic, the MRF is applied to a self-adaptive hand which can solidify the shape of the joint grasping the object and keep the grasping force under the help of springs. The MRF Hand is actuated initially by the reaction force from the grasped object and locked by the solidified MRF ultimately. The MRF Hand can keep the shape of the grasping object securely during the grasping process.

Collision-free path planning of Unmanned Aerial robots based on A* algorithm

Trajectory planning of robots is not only an important research field of robotics, but also an challenging combination of artificial intelligence and robotics. Trajectory planning is a key technology of Unmanned Aerial Vehicle (UAV). It is also a prerequisite for autonomous navigation of UAV. Under the static environment distributing of obstacles, seeking a shortest path from the starting point to the target point for global path planning has important scientific significance. This paper presents a method for collision-free trajectory planning and design of UAV. Using grids to process the trajectory environment of UAV and then find the shortest path from the initial point to the target point based on rasterization environment using A* algorithm. The simulation of trajectory planning is implemented under the Microsoft Visual C++ 6.0 development environment. From the simulation of trajectory planning, we can clearly prove that UAV can seek a shortest path from the initial point to the target point based on A* algorithm.

A new approach for stability analysis of fuzzy control systems

This paper presents new sufficient conditions to verify the stability of fuzzy control systems. Compared to the existing conditions, these new approaches contribute the following advantages: (1) only the individual active zone of each control rule or independent sub-spaces are considered respectively instead of the whole input space taken into account in Tanaka and Sugeno method, which thus simplifies the condition to verify stability; (2) the new conditions are applicable to a broader range of fuzzy control systems such as type-I ones and type-II ones with nonlinear-consequent parts, which can not be handled with Tanaka and Sugenos method. Some examples are used to demonstrate these properties.

A preference-based fuzzy behaviors method for autonomous robot navigation in dense environment

One of the current challenges in the development of robot navigation systems is making them capable of intelligent and suitable responses to the complex and changing environments. The navigation of the robots behavior becomes very challenging when it is under complex and dynamic environments. Autonomous navigation systems for mobile robots have been well developed for a wide range of planar ground-based tasks. This paper presents a novel prefence-based fuzzy behavioral scheme for navigating an unmanned vehicle in dense 3-D environments. The 3-D navigation problem is divided into several identical 2-D navigation sub-problems, each of which is solved by using preference-based fuzzy behaviors. A new defuzzification algorithm that steers the robot by finding the centroid of a 3-D convex region of maximum volume in the 3-D solution region is developed. A fuzzy speed control system is also developed to ensure efficient and safe navigation.