Wireless RF Interface Research Articles

Development of The Multi-Level Fusion based Security System

The article develops the multi-level fusion based intellig ent security system to be applied in home automation. The system contains some detection modules, a supervised computer, an elevator and an experimental platform. The detection modules have active detection modules and passive detection modules that are designed in our research team. The control unit of all detection modules (active and passive) is HOLTEK microchip. Mobile robots are active detection modules, and are classified more and more teams acco rding to the detection functions. Each mobile robot of the team robot system transmits the real-time event signals to the supervi sed computer and the other mobile robots via wireless RF interface, and moves to any floor of the experimental platform using the elevator. The passive detection modules contain wire/wireless detection modules. If the event occurrence, the passive detection modules can decide the event to be true or false in the level one using weighted average algorithm, and transmits the position of the event to the supervised computer and mobile robots. Mobile robots can detect the event to be true or false using Dempster-Shafter (D-S) evidence theory, and transmits the relation location of the event to the supervised computer, too. The supervised computer decides the event to be true according to the feedback signals using D-S evidence theory, and controls the other mobile robots moving to the event location for double check, and recognizes the final decision output using D-S evidence theo ry in the level two. Finally, we present some experimental results using the active and passive detection modules to detect the fire sourc e on the experimental platform

Based on Short Motion Paths and Artificial Intelligence Method for Chinese Chess Game

The article develops the decision rules to win each set of the Chinese chess game using evaluation algorithm and artificial intelligence method, and uses the mobile robot to be instead of the chess, and presents the movement scenarios using the shortest motion paths for mobile robots. Player can play the Chinese chess game according to the game rules with the supervised computer. The supervised computer decides the optimal motion path to win the set using artificial intelligence method, and controls mobile robots according to the programmed motion paths of the assigned chesses moving on the platform via wireless RF interface. We uses enhance A* searching algorithm to solve the shortest path problem of the assigned chess, and solve the collision problems of the motion paths for two mobile robots moving on the platform simultaneously. We implement a famous set to be called “wild horses run in farm” using the proposed method. First we use simulation method to display the motion paths of the assigned chesses for the player and the supervised computer. Then the supervised computer implements the simulation results on the chessboard platform using mobile robots. Mobile robots move on the chessboard platform according to the programmed motion paths and is guided to move on the centre line of the corridor, and avoid the obstacles (chesses), and detect the cross point of the platform using three reflective IR modules.

An Interactive Auto-recharging System for Mobile Robots

The article describes a new auto-charging system designed to enhance successful docking rates for mobile robots while at the same time monitoring the status of the charging current between the mobile robot and the docking station. The communication interface of the mobile robot and the docking station uses a wireless RF interface. The docking station is designed with one active degree of freedom and two passive degrees of freedom. Users tune the location of the charging pins on the docking station to make it possible for the mobile robot to execute an auto-recharging process. The auto-recharging process uses multiple sensors and a laser range finder located on the mobile robot. The laser range finder searches for a landmark to guide the mobile robot towards the docking station. In the experiment, the power of the mobile robot is under its threshold value. The mobile robot transmits a charging command to the docking station via a wireless RF interface. The docking station transmits a location command back to the mobile robot via this wireless RF interface. The mobile robot uses its laser range finder to search for the landmark of the assigned docking station and programs a motion trajectory to move forward to the docking station. The docking station supplies a charging current to the mobile robot by means of a charger. The power detection module simultaneously detects the charging current and the voltage values of the charging process in both the docking station and the mobile robot. The system can monitor the status of the charging process between the docking station and the mobile robot at any time. The power of the mobile robot is enough to be detected by the power detection module. When the charging process is complete, the docking station turns off the charging current and triggers the mobile robot to leave the docking station via the 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.

Develop a multi-detection security system using multi-sensor fusion algorithms

The paper develops the multi-detection system using multi-level surveillance structure. The system contains active detection modules, passive detection modules, a supervised computer, an image system and an intelligent home. The passive detection modules contain wire/wireless detection modules and appliance control modules, and decide the event to be true or not using fusion algorithms, and transmit detection signals to the supervised computer via wire/wireless interface. Mobile robots are active detection modules and carry various sensors to search dangerous events. Each mobile robot transmits the real-time event signal to the supervised computer and the other mobile robots via wireless RF interface. The image system detects fire source using Otsu algorithm. The system integrates wire/wireless passive detection modules, mobile robots and image system to detect fire source using weighted average method. If the fire event occurs, the supervised computer calculates the dangerous grade using logical filter method according to the signals of detection modules in the first step, and transmits the position of the fire event to the other mobile robots. The assigned mobile robots move to the event location for double check autonomously, and transmit the detection results to the supervised computer. The supervised computer gives the final decision according to the feedback signals in the second step. Finally, we present some experimental scenarios using passive detection modules, active detection modules and image system for the fire detection in the intelligent home.

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.

Team Mobile Robots Based Intelligent Security System

The article develops a team mobile robots based intelligent security system being applied in home automation. The detection modules of the system include active security modules and passive security modules. The control unit of all security modules (active security modules and passive security modules) is HOLTEK microchip. The paper focuses on the active security modules (mobile robots). Mobile robots are classified more and more teams according to th e detection functions. Each team robot system includes many mobile robots to execute the same event detection. Each mobile robot has two variety interfaces, uses voice module to alarm users for event condition, and transmits the real-time event signals to the superv ised computer and the other mobile robots via wireless RF interface. If the event occurs, the mobile robot can transmit the relation location (floor number) of the event to the supervised computer. The supervised computer decides the location of the event according the feedback signals from mobile robots, controls the other mobile robots moving to the event location, and recognizes the final decision outp ut using Dempster-Shafter Evidence Theory. The belief value is over the threshold value and displays detection and decision output values on the monitor of the user interface. Finally, we present some experimental results using the team mobile robots for the fire detection and gas leakage detection on the experimental platform of the intelligent security system.

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.

Bayesian Estimation Algorithm Applying in Gas Detection Modules

The paper develops gas detection modules for the intelligent building. The modules use many gas sensors to detect environment of the home and building. The gas sensors of the detection modules are classified two types. One is competitiveness gas detection module, and uses the same sensors to detect gas leakage. The other is complementation gas detection module, and uses variety sensors to classify multiple gases. The paper uses Bayesian estimation algorithm to be applied in competitiveness gas detection module and complementation gas detection module, and implement the proposed algorithm to be nice for variety gas sensor combination method. In the competitiveness gas detection module, we use two gas sensors to improve the proposed algorithm to be right. In the complementation gas detection module, we use a NH3 sensor, an air pollution sensor, an alcohol sensor, a HS sensor, a smoke sensor, a CO sensor, a LPG sensor and a nature gas sensor, and can classify variety gases using Bayesian estimation algorithm. The controller of the two gas detection modules is HOLTEK microchip. The modules can communicate with the supervised computer via wire series interface or wireless RF interface, and cautions the user by the voice module. Finally, we present some experimental results to measure know and unknown gas using the two gas detection modules on the security system of the intelligent building.

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.

Mobile Robots Based Intelligent Fire Detection and Escaping System

The paper researches the fire sources and programs escaping path using mobile robots for intelligent building, and presents the movement scenario in the experimental platform. The mobile robot has the shape of cylinder and its diameter, height and weight is 10cm, 18cm and 1.5kg. The controller of the mobile robot is MCS-51 microchip, and acquires the detection signals from flame sensor and reflecting IR sensors, and receives the control command from the supervised compute via wireless RF interface. The mobile robot searches fire sources and obstacles moving in the experimental platform autonomously, and transmits the locations of the detected fire sources and obstacles to the supervised computer, too. The supervised computer uses Gauss probability distribution function to compute the risk values around the fire location, and uses Bayesian estimation algorithm to calculate the total risk values of each location of the experimental platform. We proposed A* searching algorithm to program escaping paths according to the risk distribution of each cross point in the platform. We develop user interface on the supervised computer for the fire detection and escaping system. The supervised computer displays the escaping path on the user interface, and guides peoples moving to the safety area using mobile robots. In the experimental results, we use two mobile robots searching the fire sources autonomously and locating the positions of the fire sources. The supervised computer programs the escaping paths using Bayesian estimation algorithm and A* searching algorithm on the user interface. The mobile robot guides people moving to the safety area according to the programmed escaping path.

Speech-based formation control of the multi-robot system

The article presents multiple pattern formation control of the multi-robot system using A* searching algorithm, and avoids the collision points moving on the motion platform. We use speech recognition algorithm to decide the various pattern formations, and program mobile robots to present the movement scenario on the grid-based motion platform. We have been developed some pattern formations to be applied in game applications, such as long snake pattern formation, phalanx pattern formation, crane wing pattern formation, sword pattern formation, cone pattern formation and so on. The mobile robot contains a controller module, three IR sensor modules, a voice module, a wireless RF module, a compass module, and two DC servomotors. The controller of the mobile robot can acquire the detection signals from reflect IR sensor modules and the compass module, and decide the cross points of the aisle. The mobile robot receives the command from the supervised computer, and transmits the status of environment to the supervised computer via wireless RF interface. We develop the user interface of the multi-robot system to program motion paths for various pattern formation exchanges using the minimum displacement. Users can use speech to control the multiple mobile robots to execut pattern formation exchange. In the experimental results, users can speak the pattern formation. The speech recognition system receives the signal to decide the pattern formation. The multiple mobile robots can receive the pattern formation command from the supervised computer, and arrange the assigned pattern formation on the motion platform, and avoid other mobile robots.

Path planning of fire-escaping system for intelligent building

We present the path-planning techniques of the fire-escaping system for intelligent building, and use multiple mobile robots to present the experimental scenario. The fire-escaping system contains a supervised computer, an experimental platform, some fire-detection robots and some navigation robots. The mobile robot has the shape of a cylinder, and its diameter, height and weight are 10 cm, 15 cm and 1.5 kg, respectively. The mobile robot contains a controller module, two DC servomotors (including drivers), three IR sensor modules, a voice module and a wireless RF module. The controller of the mobile robot acquires the detection signals from reflective IR sensors through I/O pins and receives the command from the supervised computer via wireless RF interface. The fire-detection robot carries the flame sensor to detect fire sources moving on the grid-based experiment platform, and calculates the more safety escaping path using piecewise cubic Bezier curve on all probability escaping motion paths. Then the user interface uses A* searching algorithm to program escaping motion path to approach the Bezier curve on the grid-based platform. The navigation robot guides people moving to the safety area or exit door using the programmed escaping motion path. In the experimental results, the supervised computer programs the escaping paths using the proposed algorithms and presents movement scenario using the multiple smart mobile robots on the experimental platform. In the experimental scenario, the user interface transmits the motion command to the mobile robots moving on the grid-based platform, and locates the positions of fire sources by the fire-detection robots. The navigation robot guides people leaving the fire sources using the low-risk escaping motion path and moves to the exit door.

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.

Image Recognition Method Applying in Formation Control of Mobile Robots

The article develops multi-pattern formation exchange using A* searching algorithm, and programs the shortest motion paths for mobile robots. The system contains an image recognition system, a motion platform, some wireless RF modules and five mobile robots. We use Otsu algorithm to recognize the variety 2D bar code to classify variety pattern, and control five mobile robots to execute formation exchange, and present the movement scenario on the motion platform. We have been developed some pattern formations according to game applications, such as hook pattern formation, T pattern formation, L pattern formation, rectangle pattern formation, sward pattern formation and so on, and develop the user interface of the multi-robot system to program motion paths for variety pattern formation exchange on the supervised computer. The supervised computer programs pattern formation exchange according to the image recognition results, and controls mobile robots moving on the motion platform via wireless RF interface. In the experimental results, 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 on real-time.

Motion planning for a landmine-detection robot

This article describes a landmine-detection system that contains a landmine-detection mobile robot and a following mobile robot. In this system, the landmine-detection robot goes ahead, and uses a landmine detector and a GPS module to find a landmine, records the coordinates of its location, and transmits these coordinates to the following mobile robot via a wireless RF interface. The following robot can record the location and orientation of the landmine-detection robot and all the landmines in the region. The following robot moves close to the landmine, and programs a path to avoid obstacles and landmines automatically. The driving system of the landmine-detection mobile robot uses a microprocessor dsPIC 30F4011 as the core, and controls two DC servomotors to program the motion path. The user interface of the landmine-detection robot and the following robot uses Borland C++ Builder language to receive the location data. In the experimental results, the landmine-detection robot records the location of landmines using a GPS module, and transmits the locations to the following robot via a wireless RF interface. The following robot avoids the landmines, and improves the safety of people or materials being carried through the landmine area.

Multi-robot-based intelligent security system

This article describes a multiple security module-based intelligent security system that has multiple communication interfaces which can be applied in home automation. The interfaces of the intelligent security system contain wired RS485, wireless RF, and Internet. The detection modules of the system have both active and passive security modules. The passive security modules contain wired security modules and wireless security modules. The control unit of all security modules is a HOLTEK microchip. Each security module has two different interfaces. They use voice modules to alarm users of an event, and to transmit real-time event signals to the supervising computer via the wired RS485 or wireless RF interface. If an event occurs, the supervising computer calculates its belief values using Dempster-Shafter evidence theory according to the passive wired and wireless security modules. If the belief value is over a set threshold, the supervising computer commands the mobile robot to move to the event location, and receives a signal from the mobile robot via the wireless RF interface. The supervising computer recognizes the final decision output using Dempster-Shafter evidence theory, and displays the detection and decision output values on the monitor of the user interface. Finally, we present some experimental results using wired passive security modules, wireless passive security modules, and active security modules for fire detection and gas leakage detection using the experimental platform of the intelligent security system.

A* search algorithm applied to a Chinese chess game

This article presents an A* search algorithm to be applied to path planning in a Chinese chess game, and uses multiple mobile robots to present the scenario. The mobile robots have a cylindrical shape, and their diameter, height, and weight are 8 cm, 15 cm, and 1.5 kg, respectively. The controller of the mobile robots is a MCS-51 chip. We play the Chinese chess game using multiple mobile robots according to the evaluation algorithm of the game, and calculate the displacement by the encoder of a DC servomotor. The A* search algorithm can solve the shortest-path problem for the mobile robots from the starting point to the target point on the chess board. The simulated results found the shortest path for the mobile robots (chess pieces) moving to target points from their starting points in a collision-free environment. Finally, we implemented the experimental results on a Chinese chess board using mobile robots. Users can play the Chinese chess game using the supervising computer via a wireless RF interface. The scenario of the feedback of the Chinese chess game to the user interface uses an image system.

Multilevel multisensory-based security system for an intelligent home

This article describes a multilevel multisensor-based security system that has multiple interfaces to be applied in an intelligent home. The security system contains four levels. There is a passive detection level, an active detection level, a system supervising level, and a remote supervising level. The control unit of these passive modules is a HOLTEK microchip. Each passive module has two different interfaces (wireless RF and voice). These modules can use a voice to alarm users than an event has occurred, and can transmit the real-time status and image signal to the active detection level and the system's supervising level via the wireless RF interface. The active detection level can communicate with other levels via the wireless RF interface or the wireless Internet. The remote supervising level, the supervising level, and the active detection level can communicate with other levels via the wireless Internet. The status of these modules can also be displayed on the monitor of the supervising computer. Finally, we present some experimental results using passive and active detection modules in the security system.

Developing a vision-based auto-recharging system for mobile robots

This article describes a vision-based auto-recharging system that guides a mobile robot moving toward a docking station. The system contains a docking station and a mobile robot. The docking station contains a docking structure, a control device, a charger, a safety 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 controller, and a camera. The controller of the power detection module is a Holtek chip. The docking structure is designed with one active degree of freedom and two passive degrees of freedom. For image processing, the mobile robot uses a webcam to capture a real-time image. The image signal is transmitted to the controller of the mobile robot via a USB interface. We use an Otsu algorithm to calculate the distance and orientation of the docking station from the mobile robot. In the experiment, the proposed algorithm guided the mobile robot to the docking station.