Kuo Lan Su

Design an Auto-Recharging System for Mobile Robots

The article develops an auto-charging system for mobile robots, and programs a new docking processing to enhance successful rate. The system contains a docking station and a mobile robot. The docking station contains a docking structure, a limit switch, a charger, two power detection modules and two wireless RF modules. The mobile robot contains a power detection module (voltage and current), an auto-switch, a wireless RF module, a charging connection structure and a laser range finder. The docking structure is designed with one active degree of freedom and two passive degrees of freedom. The power detection module is controlled by HOLTEK microchip. We calculate the power values using the redundant management method and statistical signal prediction method, and develop an auto-recharging processing using multiple sensors and laser range finder for mobile robots. The processing can enhances the successful rate to guide the mobile robot moving to the docking station. In the experimental results, the power of the mobile robot is under the threshold value. The mobile robot transmits the charging command to the docking station via wireless RF interface, and searches the landmark of the docking station using laser range finder (SICK). The laser range finder guides the mobile robot approach to the docking station. The mobile robot touches the docking station to trig the power detection device. The docking station supplies the power to the mobile robot by charger, and detects the current and voltage values of the charging processing. The charging current of the docking station is under the threshold value. The docking station turns off the charging current, and trigs the mobile robot leaving the docking station via wireless RF interface.

Enhance A* Searching Algorithm Applying in Multiple Robot System

The article programs the shortest motion paths of the multiple mobile robots to be applied in the Chinese chess game, and presents the movement scenario of the chess using mobile robots on the grid based chessboard platform. Users play the chess game using the mouse to obey the evaluation algorithm on the user interface. The user interface programs the motion paths that are the shortest displacement using enhance A* searching algorithm and solves the collision problem of the programmed motion paths for the assigned chesses to and reprogram the new motion paths using enhance A* searching algorithm, too. The supervised computer controls mobile robots according to the programmed motion paths of the assigned chess moving on the platform via wireless RF interface. In the experimental results, we use simulation method to search the motion paths of the assigned chesses on the user interface, and implement the simulation results on the chessboard platform using mobile robots. Mobile robots move on the platform according to the programmed motion paths from the start points to the target points and avoid the collision points.

An evolutionary Lagrange method for batch process optimal design

The optimal design of the multiproduct batch processes is one of the most important decision-making problems in the manufacturing industry. This decision-making problem can be formulated as a mixed-integer non-linear programming (MINLP) problem. In this paper, we propose an evolutionary Lagrange method to deal with such an MINLP problem. The computational results demonstrate the effectiveness of the algorithm in solving the optimal design problem of the multiproduct batch processes.

An Evolutionary Lagrange Method for Mechanical Design

A novel application to mechanical optimal design is presented in this paper. Here, an evolutionary algorithm, called mixed-integer differential evolution (MIHDE), is used to solve general mixed-integer optimization problems. However, most of real-world mixed-integer optimization problems frequently consist of equality and/or inequality constraints. In order to effectively handle constraints, an evolutionary Lagrange method based on MIHDE is implemented to solve the mixed-integer constrained optimization problems. Finally, the evolutionary Lagrange method is applied to a mechanical design problem. The satisfactory results are achieved, and demonstrate that the evolutionary Lagrange method can effectively solve the optimal mechanical design problem.

Development of a Tracking System on the Mobile Robot

The purpose of this paper is to develop an intelligent mobile robot using image processing technology. The mobile robot is composed of a visual tracking system, a loading platform, a balance control system, a PC-based controller, four ultrasonic sensors and a power system. We develop a PC based control system for image processing and path planning. The mobile robot can track a moving target and adjust the loading platform by the balance control system simultaneously. The Image processing based on OpenCV use two different tracking methods, MTLT (Match Template Learning Tracking) and TLD (Tracking, Learning and Detection), to track moving targets. The efficiencies of both methods for tracking the moving target on the mobile robot are compared in this paper. The loading platform control system uses HOLTEK Semiconductor Companys HT66F Series 8-bit microprocessor as the processor, and receives the feedback data from the FAS-A inclinometer sensor. The controller of the loading platform uses the PID control law according to the feedback signals of the inclinometer sensor, and controls the rotation speed of the platform motor to tune the balance level. Keywords— Intelligent mobile robot, Image processing, OpenCV, MTLT, TLD, HOLTEK, FAS-A inclinometer sensor, PID control.

Using Laser Range Finder Obstacle Avoidance and Using TLD Positioning System of Mobile Robot

Robot self-localization and obstacle avoidance has been one of the important topics in robotics. The sensing system which is more mature and using a laser range finder (LRF). But the biggest drawback is the LRF detection range is a plane, And for some high reflectivity of the object, will produce incorrect reflection data. So when the obstacle is not the detection range, or due to high reflectance data will generate an error and the positioning of the robot obstacle avoidance function error. This paper is the use of TLD (Tracking-Learning-Detection) image recognition system, to assist LRF do positioning and obstacle avoidance. And this imaging system can also be used while the robot with object tracking functions

Development of the Escaping Programming System for Fire Environment

We present the path planning techniques of the fire escaping system using multiple mobile robots for intelligent building. The controller of the mobile robot is MCS-51 microchip, and acquires the detection signal from flame sensor through I/O pins, and receives the command from the supervised compute via wireless RF interface. The mobile robot transmits ID code, detection signal, location and orientation of the mobile robots to the supervised computer via wireless RF interface. We proposed A* searching algorithm to program escaping motion paths to guard peoples moving to the safety area using mobile robots, and develop user interface on the supervised computer for the fire escaping system. In the experimental results, the supervised computer locates the positions of fire sources by mobile robots, and programs the escaping paths on the user interface, and transmits the motion command to the mobile robots. The mobile robot guards peoples leaving the fire sources.

Target Searching of Mobile Robots Using Improved A* Searching Algorithm

The paper studies motion path planning problem using improved A* searching algorithm, and implements the movement scenario using mobile robot system. Solving the shortest path problem, Dijkstra algorithm can finds the shortest path to solve the path searching problem. The searching depth is not applicable in certain circumstances to be a disadvantage. The paper proposed improved A* searching algorithm using the intelligent node judgment method, it not only be able to retain the advantages of the traditional A* algorithm but also improve the shortcomings. It programs the on-time motion path in dynamic environment; it also programs the motion path more closed to the best path. Finally, the paper uses multiple strategies to improve the searching motion path to be the best motion path, and compares with the traditional A* algorithm to reduce the searching time.

Development of Intelligent Fire Detection Module Using Optic-Sensors

The paper proposes an adaptive fusion algorithm using competitiveness sensors for fire detection module, and uses computer simulation results to select the optimal weight values for each optic-sensor. Then we design the fire detection module using the tuned weight values of optic-sensors. The competitiveness flame sensor type is ultra-violet sensor (R2868). The controller of the module is HOLTEK microchip, and acquires the detection signals from the optic-sensors through I/O pins, and transmits the detection signals of all sensors to the computer via wire series interface. The adaptive fusion algorithm can tunes weight values according to decision output of the fusion center. The fusion algorithms of the fusion center use Bayesian estimated method to decide the fire event to be true or not. We set the improved weight values in the module for each optic-sensor. From the simulation and experimental implementation results, it demonstrates that the proposed algorithms can compute the adequate weight values.

Motion Planning of the Mobile Robot Using Laser Detection System

The paper programs motion paths using laser detection system for the mobile robot. The laser detection system contains a laser range finder and a laser positioning system. The mobile robot is constructed using aluminum frame, and has the shape of cylinder and its diameter, height and weight is 40 cm, 80cm and 40kg respectively. In the driver system and avoidance obstacle driver system, we use NI motion control card and MAXON drivers to control two DC servomotors, and detect obstacle using a laser range finder and sixteen reflective IR sensors. The mobile robot locates the position of the detected obstacles using a laser positioning system. The mobile robot can program the motion path using A* searching algorithm and avoids the detected obstacles to follow the programmed trajectory. We develop the user interface to display the positions of the detected obstacles. Finally, we implement the experimental results using the proposed method. The mobile robot moves to the target position from the start position autonomously. The mobile robot detects environment status using the laser detection system and avoids the detected obstacles to finish the assigned tasks.