Byoung-Suk Choi

A Hierarchical Algorithm for Indoor Mobile Robot Localization Using RFID Sensor Fusion

This paper addresses a radio-frequency identification (RFID)-based mobile robot localization which adopts RFID tags distributed in a space. Existing stand-alone RFID systems for mobile robot localization are hampered by many uncertainties. Therefore, we propose a novel algorithm that improves the localization by fusing an RFID system with an ultrasonic sensor system. The proposed system partially removes the uncertainties of RFID systems by using distance data obtained from ultrasonic sensors. We define a global position estimation (GPE) process using an RFID system and a local environment cognition (LEC) process using ultrasonic sensors. Then, a hierarchical localization algorithm is proposed to estimate the position of the mobile robot using both GPE and LEC. Finally, the utility of the proposed algorithm is demonstrated through experiments.

Energy-Efficient Coverage of Wireless Sensor Networks Using Ant Colony Optimization With Three Types of Pheromones

The Efficient-Energy Coverage (EEC) problem is an important issue when implementing Wireless Sensor Networks (WSNs) because of the need to limit energy use. In this paper, we propose a new approach to solving the EEC problem using a novel Ant Colony Optimization (ACO) algorithm. The proposed ACO algorithm has a unique characteristic that conventional ACO algorithms do not have. The proposed ACO algorithm (Three Pheromones ACO, TPACO) uses three types of pheromones to find the solution efficiently, whereas conventional ACO algorithms use only one type of pheromone. One of the three pheromones is the local pheromone, which helps an ant organize its coverage set with fewer sensors. The other two pheromones are global pheromones, one of which is used to optimize the number of required active sensors per Point of Interest (PoI), and the other is used to form a sensor set that has as many sensors as an ant has selected the number of active sensors by using the former pheromone. The TPACO algorithm has another advantage in that the two user parameters of ACO algorithms are not used. We also introduce some techniques that lead to a more realistic approach to solving the EEC problem. The first technique is to utilize the probabilistic sensor detection model. The second method is to use different kinds of sensors, i.e., heterogeneous sensors in continuous space, not a grid-based discrete space. Simulation results show the effectiveness of our algorithm over other algorithms, in terms of the whole network lifetime.

An improved localization system with RFID technology for a mobile robot

This paper proposes an improved localization scheme for self-localization of an mobile robot by fusing RFID localization system and ultrasonic measurements. The novel localization system for an indoor mobile robot is proposed to improve the efficiency of mobile robot system. The proposed system is based on previous RFID localization system, which removes the uncertainty of robot location using the distance measurements by ultra-sonic sensors. We address more efficient localization algorithm than the previous system for the mobile robot in the given environment. First, RFID and wheel encoder localization systempsilas uncertainty, which may result in inaccurate location data, is modeled. And then, the algorithm for estimating each uncertainty is proposed for localization. Finally, a proposed algorithm successfully demonstrated through simulation experiments conducted under certain assumption.

Position and Compliance Control of a Pneumatic Muscle Actuated Manipulator for Enhanced Safety

The safety of humans who work with robots is an important issue. Many studies have addressed related methods, but fundamental limits to meet safety requirements have been encountered owing to the absence of compliance in robot actuators. Pneumatic muscle is considered to be a basic actuator and offers the advantage of intrinsic elasticity to achieve joint compliance. In this study, joint compliance actuated by pneumatic muscle is actively utilized to enhance human safety during collisions. To this end, the authors present a novel approach to control compliance and associated positions independently with no cross-performance effects using pneumatic muscles. The proposed method is verified by simulation and experiments using a physical robot.

Sensor network based localization algorithm using fusion sensor-agent for indoor service robot

We propose a system architecture and algorithm for improved localization of indoor service robots. Many studies on sensor network based localization systems have been reported for indoor service robots. Previous sensor network based localization systems have used a sensor-agent which is composed of only one kind of sensor, but such an arrangement may have difficulty adapting to changing environmental conditions. Therefore, we configure the sensor-agent with different kinds of sensors using a sensor fusion concept. To do so, we propose a novel architecture for a sensor network based localization system. We further propose a localization algorithm which processes the position data from each sensor-agent. Finally, we apply the proposed localization algorithm to real indoor service robots and demonstrate the improved localization ability of the service robots.

Localization and map-building of mobile robot based on RFID sensor fusion system

In this paper, we propose an improved localization system for an indoor mobile robot using RFID (Radio Frequency IDentification) system and wheel encoders. Nowadays, RFID technology is widely used in the robot field. We investigate recent RFID localization system based on (passive) tag-floor for mobile robot, and analyze the problems and limitation of previous researches. First, RFID and wheel encoder localization systempsilas uncertainty, which may result in inaccurate location data, is modeled. And then, the algorithm for estimating each uncertainty is proposed for localization. Finally, a proposed algorithm successfully demonstrated through simulation experiments conducted under certain assumption.

Mobile robot localization scheme based on RFID and sonar fusion system

We propose a novel algorithm for improved localization of the mobile robots by fusing RFID localization system and ultra-sonic sensors. Because there are uncertainties in previous RFID localization system, we are focusing on the sensor fusion system, which removes the uncertainty of the robot location using the distance measurements by ultra-sonic sensors. We divide into 2 parts, which are GPE (Global Position Estimation) by RFID localization system and LEC (Local Environment Cognition) by ultra-sonic sensor. The scheme to remove the uncertainty is proposed, which are the modeling about the measurement noise for RFID localization system and to estimate the straight obstacle and circular obstacle in well-structural indoor environment by the ultra-sonic sensors. The hierarchical localization algorithm is proposed to estimate the position of the mobile robot from GPE and LEC and for robust localization under the unknown indoor environment.

Mobile robot localization in indoor environment using RFID and sonar fusion system

Localization of indoor environment is a fundamental issue for mobile robot. In this paper, we proposed the localization scheme to fusion the RFID localization system, ultrasonic sensor and wheel encoder. The uncertainty is factor caused by each localization system, and the estimation error is affected by this uncertainty. The sensor system for mobile robot localization is technical limitation such as sensing range, operation feature. Therefore we focus on sensor fusion scheme. When the mobile robot moves, certain data combination set to fuse is selected according to the environmental factor. The performance and simplicity of the approach is demonstrated with the result produced by experiments using mobile robot.

Improved Ant Colony Optimization algorithm by potential field concept for optimal path planning

In this paper, an improved ant colony optimization (ACO) algorithm is proposed to solve path planning problems. These problems are to find a collision-free and optimal path from a start point to a goal point in environment of known obstacles. There are many ACO algorithm for path planning. However, it take a lot of time to get the solution and it is not to easy to obtain the optimal path every time. It is also difficult to apply to the complex and big size maps. Therefore, we study to solve these problems using the ACO algorithm improved by potential field scheme. We also propose that control parameters of the ACO algorithm are changed to converge into the optimal solution rapidly when a certain number of iterations have been reached. To improve the performance of ACO algorithm, we use a ranking selection method for pheromone update. In the simulation, we apply the proposed ACO algorithm to general path planning problems. At the last, we compare the performance with the conventional ACO algorithm.

Localization of a mobile robot based on an ultrasonic sensor using dynamic obstacles

In this article, we propose a localization scheme for a mobile robot based on the distance between the robot and moving objects. This method combines the distance data obtained from ultrasonic sensors in a mobile robot, and estimates the location of the mobile robot and the moving object. The movement of the object is detected by a combination of data and the object’s estimated position. Then, the mobile robot’s location is derived from the a priori known initial state. We use kinematic modeling that represents the movement of a robot and an object. A Kalman-filtering algorithm is used for addressing estimation error and measurement noise. Throughout the computer simulation experiments, the performance is verified. Finally, the results of experiments are presented and discussed. The proposed approach allows a mobile robot to seek its own position in a weakly structured environment.