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.

Fire Detection System Based-On Mobile Robots

The paper develops a fire detection system using mobile robots, and calculates the risk values of the escaping paths using Bayesian estimated method. Mobile robots contain two types moving in the platform. One is fire detection robot (FDR) to search fire sources. The other represents the people walking in the platform autonomously. The controller of the mobile robot detects fire source using flame sensor, and receives the motion command from the supervised compute via wireless RF interface. The mobile robot transmits ID code, position and orientation information, positions of fire sources to the supervised computer via wireless RF interface, too. We program the motion path of fire detection robots to search fire sources, and uses Gauss distribution function to describe the risk values of each fire source. The supervised computer uses Bayesian estimated algorithm to calculate the relation risk value of each cross point for multiple fire sources. In the fire condition, each FDR calculates shortest displacement from the people. The assigned FDR carries the people leaving the dangerous area. Then the user interface programs the escaping paths using A* searching algorithm for mobile robots. The mobile robot guides the people (mobile robot) leaving the fire area according the programmed safety escaping path.

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.

Motion Planning of Multiple Pattern Formation for Mobile Robots

The article designs the multiple pattern formation controls of the multi-robot system according to two arms’ gesture of the player, and uses flood fill searching algorithm and A* searching algorithm to program the motion paths. The inertia module detects two arms’ gesture of the player. We use the inertia module to be embedded in the two arms, and use mobile robots to present the movement scenario of pattern formation exchange on the grid based motion platform. We have been developed some pattern formations applying in the war game, such as rectangle pattern formation, long snake pattern formation, L pattern formation, sword pattern formation, cone pattern formation and so on. We develop the user interface for variety pattern formation exchange according to the minimum displacement on the supervised computer. The mobile robot receives the command from the supervised compute, and transmits the status of environment to the supervised computer via wireless RF interface. Players can use variety arms’ gesture to control the multiple mobile robots to executed pattern formation exchange. In the experimental results, the supervised computer can decides the arm gesture using fusion algorithms. Mobile robots can receive the pattern formation command from the supervised computer, and change the original pattern formation to the assigned pattern formation on the motion platform, and avoid other mobile robots.

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.

Develop a Vision Based Auto-Recharging System for Mobile Robots

The article develops a vision based auto-recharging 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 control device, a charger and a detection device and a wireless RF interface. The mobile robot contains a power detection module (voltage and current), an auto-switch, a wireless RF interface, a control system and a camera. The docking structure is designed with one active degree of freedom and two passive degrees of freedom. The active degree of freedom can move forward to contact the recharging connect points that are arranged in the mobile robot. The two passive degrees of freedom can rotation in the Z-axis and use compression spring moving on various docking condition. In image processing, the mobile robot uses a webcam to capture the real-time image; and transmits the image signal to the computer via USB interface, and uses Otsu algorithm to recognize the position of the docking station. In the experiment results, the system had been successfully guided the mobile robot moving to the docking station using the proposed method.

Intelligent Home-Automation Security System

The objective of the home security detection and appliance control system is to protect occupants against events -- many of which are attributable to human error. We have developed a module based security system for home automation. The structure of the security system is divided into many modules. Each module in the home -- automation security system we develop has a wireless RF and a speech interface. They contain fire detection module, intruder detection module, environment detection module, gas detection module, (AC) power detection and diagnosis module, appliance control module and team robots. In the security detection module, we use multisensor fusion algorithms to decide an exact output. In the application control module, we use two-wire communication via wireless RF interface. These modules have event-condition voice alarms, and send the real-time status to the master controller. In the smart team robot system, we have designed many smart robots for the security system. We have designed a general user interface (GUI) for the intelligent security system. The user interface can supervise these modules and team robots via wireless RF interface, and use wireless Internet and mobile phone for remote supervision.