Mobile Robot In Indoor Environment Research Articles

기능별로 분류된 프레임워크에 기반한 실내용 이동로봇의 주행시스템

This paper proposes a new integrated navigation system for a mobile robot in indoor environments. This system consists of five frameworks which are classified by function. This architecture can make the navigation system scalable and flexible. The robot can recover from exceptional situations, such as environmental changes, failure of entering the narrow path, and path occupation by moving objects, using the exception recovery framework. The environmental change can be dealt with using the probabilistic approach, and the problems with the narrow path and path occupation are solved using the ray casting algorithm and the Bayesian update rule. The proposed navigation system was successfully applied to several robots and operated in various environments. Experimental results showed good performance in that the exception recovery framework significantly increased the success rate of navigation. The system architecture proposed in this paper can reduce the time for developing robot applications through its reusability and changeability.

Remote navigation of a mobile robot in an RFID-augmented environment

In the current article, we address the problem of constructing radiofrequency identification (RFID)-augmented environments for mobile robots and the issues related to creating user interfaces for efficient remote navigation with a mobile robot in such environments. First, we describe an RFID-based positioning and obstacle identification solution for remotely controlled mobile robots in indoor environments. In the robot system, an architecture specifically developed by the authors for remotely controlled robotic systems was tested in practice. Second, using the developed system, three techniques for displaying information about the position and movements of a remote robot to the user were compared. The experimental visualization techniques displayed the position of the robot on an indoor floor plan augmented with (1) a video view from a camera attached to the robot, (2) display of nearby obstacles (identified using RFID technology) on the floor plan, and (3) both features. In the experiment, test subjects controlled the mobile robot through predetermined routes as quickly as possible avoiding collisions. The results suggest that the developed RFID-based environment and the remote control system can be used for efficient control of mobile robots. The results from the comparison of the visualization techniques showed that the technique without a camera view (2) was the fastest, and the number of steering motions made was smallest using this technique, but it also had the highest need for physical human interventions. The technique with both additional features (3) was subjectively preferred by the users. The similarities and differences between the current results and those found in the literature are discussed.

VecSLAM: An Efficient Vector-Based SLAM Algorithm for Indoor Environments

In this paper, we present an efficient SLAM (Simultaneous Localization and Mapping) algorithm named VecSLAM, which localizes and builds a vector map for mobile robots in indoor environments. Compared to grid-mapping approaches, vector-based mapping algorithms require a relatively small amount of memory. Two essential operations for successful vector mapping are vector merging and loop closing. Merging methods used by traditional line segment-based mapping algorithms do not consider the sensor characteristics, which causes additional mapping error and makes it harder to close loops after navigation over a long distance. In addition, few line segment-based SLAM approaches contain loop closing methodology. We present a novel vector merging scheme based on a recursive least square estimation for robust mapping. An efficient loop closing method is also proposed, which effectively distributes the resultant mapping error throughout the loop to guarantee global map consistency. Simulation studies and experimental results show that VecSLAM is an efficient and robust online localization and mapping algorithm.

Direction Sensing RFID Reader for Mobile Robot Navigation

A self-contained direction sensing radio frequency identification (RFID) reader is developed employing a dual-directional antenna for automated target acquisition and docking of a mobile robot in indoor environments. The dual-directional antenna estimates the direction of arrival (DOA) of signals from a transponder by using the ratio of the received signal strengths between two adjacent antennas. This enables the robot to continuously monitor the changes in transponder directions and ensures reliable docking guidance to the target transponder. One of the technical challenges associated with this RFID direction finding is to sustain the accuracy of the estimated DOA that varies according to environmental conditions. It is often the case that the robot loses its way to the target in a cluttered environment. To cope with this problem, the direction correction algorithm is proposed to triangulate the location of the transponder with the most recent three DOA estimates. Theoretical simulation results verify the reliability of the proposed algorithm that quantifies the potential error in the DOA estimation. Using the algorithm, we validate mobile robot docking to an RFID transponder in an office environment occupied by obstacles.

On‐line path planning strategy integrated with collision and dead‐lock avoidance schemes for wheeled mobile robot in indoor environments

PurposeThis paper aims to present a hybrid path planning algorithm which is designed for use of autonomous vehicles in indoor environments. The approach mainly contributes the ability of generating a safe and smooth collision avoidance path for attaining a desired position in an unknown and obstructed environment.Design/methodology/approachThe hybrid planner is based on potential field method and Voronoi diagram approach, and it is represented with the ability of concurrent map building and autonomous navigation.FindingsThe possibility of controlling the look‐ahead distance allows the mobile robot to smartly control the velocity for creating a smooth trajectory autonomously. The dead‐lock problem is solved by defining necessary sub‐goals between targets on the constructed map.Originality/valueThe system controller (look‐ahead control) with the potential field method allows the robot to generate a smooth and safe path for an expected position. Only essential exploration of unknown environment is performed since the approach constrains the mobile robot to explore a safe and sub‐optimal route towards a destination.

A variant of navigation of a mobile robot with the help of a camera

The navigation problem for a mobile robot in indoor environment is considered. A camera, rigidly attached to the object and observing markers placed on the ceiling, is the main source of navigation data. Additional data are provided by odometry devices and is processed by an algorithm based on approximate integration of the equations of nonholonomic constraints.

Guidance of a mobile robot using an array of static cameras located in the environment

This paper presents a new proposal for positioning and guiding mobile robots in indoor environments. The proposal is based on the information provided by static cameras located in the movement environment. This proposal falls within the scope of what are known as intelligent environments; in this case, the environment is provided with cameras that, once calibrated, allow the position of the robots to be obtained. Based on this information, control orders for the robots can be generated using a radio frequency link. In order to facilitate identification of the robots, even under extremely adverse ambient lighting conditions, a beacon consisting of four circular elements constructed from infrared diodes is mounted on board the robots. In order to identify the beacon, an edge detection process is carried out. This is followed by a process that, based on the algebraic distance, obtains the estimated ellipses associated with each element of the beacon. Once the beacon has been identified, the coordinates of the centroids for the elements that make up the beacon are obtained on the various image planes. Based on these coordinates, an algorithm is proposed that takes into account the standard deviation of the error produced in the various cameras in ascertaining the coordinates of the beacon's elements. An odometric system is also used in guidance that, in conjunction with a Kalman Filter, allows the position of the robot to be estimated during the time intervals required to process the visual information provided by the cameras.

Model-Free Execution Monitoring in Behavior-Based Robotics

In the near future, autonomous mobile robots are expected to help humans by performing service tasks in many different areas, including personal assistance, transportation, cleaning, mining, or agriculture. In order to manage these tasks in a changing and partially unpredictable environment without the aid of humans, the robot must have the ability to plan its actions and to execute them robustly and safely. The robot must also have the ability to detect when the execution does not proceed as planned and to correctly identify the causes of the failure. An execution monitoring system allows the robot to detect and classify these failures. Most current approaches to execution monitoring in robotics are based on the idea of predicting the outcomes of the robot's actions by using some sort of predictive model and comparing the predicted outcomes with the observed ones. In contrary, this paper explores the use of model-free approaches to execution monitoring, that is, approaches that do not use predictive models. In this paper, we show that pattern recognition techniques can be applied to realize model-free execution monitoring by classifying observed behavioral patterns into normal or faulty execution. We investigate the use of several such techniques and verify their utility in a number of experiments involving the navigation of a mobile robot in indoor environments.

실내 환경에서의 이동로봇 제어를 위한 유비쿼터스 인터페이스 시스템

Recently, there are lots of concerning on ubiquitous environment of robots and URC (Ubiquitous Robotic Companion). In this paper, a practical ubiquitous interface system far controlling mobile robots in indoor environments was proposed. The interface system was designed as a manager-agent model including a PC manager, a mobile manager, and robot agents for being able to be accessed by any network. In the system, the PC manager has a 3D virtual environment and shows real images for a human-friendly interface, and share the computation load of the robot such as path planning and managing geographical information. It also contains Hybrid Format Manager(HFM) working for transforming the image, position, and control data and interchanging them between the robots and the managers. Mobile manager working in the minimized computing condition of handsets has a mobile interface environment displaying the real images and the position of the robot and being able to control the robots by pressing keys. Experimental results showed the proposed system was able to control robots rising wired and wireless LAN and mobile Internet.

DYNAMIC LOCALIZATION OF A MOBILE ROBOT WITH ACTIVE BEACON SENSORS

With the development of service robots and with the emerging concept of ubiquitous world, localization of a mobile robot has become a popular issue. Even though several localization schemes are introduced previously, none of them is free from the economical constraints to be used for commercial robots. To implement a real time commercial localization system, a new absolute position estimation method for a mobile robot in indoor environment is proposed in this paper. Design and implementation of the localization system comes from the usage of active beacon systems each of which is composed of an RFID receiver and an ultra-sonic transmitter. The RFID receiver gets the synchronization signal from the mobile robot and the ultrasonic transmitter sends out a short-term signal to be used for measuring the distance from the beacon to the mobile robot. Position of a mobile robot in a three dimensional space can be calculated basically from the distance information from three beacons which have their own absolute position information. One of the interesting problems comes from the static localization scheme. That is, the collision avoidance against surrounding objects can be planned and controlled efficiently if the velocity of the moving object can be estimated. Main contribution of this paper is developing a dynamic localization scheme to be used for the moving objects, which is a new application field of active beacon sensors. In the dynamic localization scheme, sensor fusion techniques and extended Kalman filter have been used to improve the estimation accuracy. Experimental results demonstrate the effectiveness and feasibility of this scheme.

RFID 응용 기술을 이용한 이동 로봇의 실내 위치 추정

Recently, with the development of service robots and with the new concept of ubiquitous world, the position estimation of mobile objects has been raised to an important problem. As pre-liminary research results, some of the localization schemes are introduced, which provide the relative location of the moving objects subjected to accumulated errors. To implement a real time localization system, a new absolute position estimation method for a mobile robot in indoor environment is proposed in this paper. Design and implementation of the localization system comes from the usage of active beacon systems (based upon RFID technology). The active beacon system is composed of an RFID receiver and an ultra-sonic transmitter: 1. The RFID receiver gets the synchronization signal from the mobile robot and 2. The ultra-sonic transmitter sends out the traveling signal to be used for measuring the distance. Position of a mobile robot in a three dimensional space can be calculated basically from the distance information from. Three beacons and the absolute position information of the beacons themselves. In some case, the mobile robot can get the ultrasonic signals from only one or two beacons, because of the obstacles located along the moving path. Therefore, in this paper, as one of our dedicated contribution, the position estimation scheme with less than three sensors has been developed. Also, the extended Kalman filter algorithm is applied for the improvement of position estimation accuracy of the mobile robot.

Building Environment Maps using Neural Networks

In this paper, we present an efficient and cheap approach to build a sonar based mapping for autonomous mobile robot in indoor environments. The system uses one ultrasonic sensor (Emitter and receiver are separated) mounted on a motorization constituted by two motors allowing horizontal and vertical scanning. The reading is modelled as probability profiles projected on a two-dimensional map. These readings provide information’s concerning empty and occupied areas in the sensor cone. The latter is the geometric representation of the sensor beam, it is composed of data allowing recognition of the environment structure. The world is modelled as a grid of cells. Two classifying neural nets are used to build the environment map, the first one for robot navigation in order to scan the workspace to obtain a data base which is used by a second one to build the map. The result is a faithful representation of the real environment when the inputs are cones of data. The map can be used to plan paths and for navigation. Results from actual runs are presented at the end.

Construction de cartes d'environnements par des réseaux de neurones

In this paper, we present an efficient approach to build a sonar based mapping for autonomous mobile robot in indoor environments. The system uses one ultrasonic sensor mounted on a motorization constituted by one stepper motor allowing an horizontal scanning. The reading is modelled as probability profiles projected on a two-dimensional map. These readings provide information's concerning empty and occupied areas in the sensor cone. The robot is placed in different positions of the environment where sets of readings are taken. In this paper we propose a neural approach to determine the status of a given area in a global then in a local manner. The goal being to obtain an environment map with a high accuracy. The map can be used in path planning.

Efficient integration of metric and topological maps for directed exploration of unknown environments

Recent research in mobile robot navigation is focused on integrating the metric and topological paradigms to unsupervisedly construct representations of indoor environments. While metric methods produce accurate environment representations, these representations present a huge data volume and they are consequently difficult to process in real time. On the other hand, topological maps can be processed in a more efficient way, but they are typically difficult to disambiguate and update. This paper describes an exploration algorithm for totally or partially unexplored environments. The algorithm is based on a representation that integrates the metric and topological paradigms. Exploration planning is performed at two levels: global planning is performed at topological level and local planning is performed at metric level. The main advantage of the proposed algorithm is that exploration can be performed in a fast and efficient way by using the presented representation. The method has been successfully tested for a Pioneer P2AT mobile robot in indoor environments.

Mobile Robot Playback Navigation Based on Robot Pose Calculation Using Memorized Omnidirectional Images

We propose a method of autonomous navigation for mobile robots in indoor environments by a teaching and playback scheme. During teaching, an operator guides a robot to move by manual control. While moving, the robot memorizes its motion measured by odometry and an environmental image taken by an omnidirectional camera at each time interval, and regards places where images were taken as target positions. When navigating autonomously, the robot plays back memorized motion to track each target position and corrects its position by calculating its relative pose using current and memorized images, to follow the taught route. In this method, vertical edges existing in the environment are used as landmarks to calculate robot position, and an evaluation function defined by us is used to find corresponding vertical edges between two images. The robot thus can navigate robustly in real building environments. The system can avoid the problem of the operator covering a part of the environment in images during the teaching stage.

Sensor-based navigation of a mobile robot in an indoor environment

The work presented in this paper deals with the problem of the navigation of a mobile robot either in unknown indoor environment or in a partially known one. A navigation method in an unknown environment based on the combination of elementary behaviors has been developed. Most of these behaviors are achieved by means of fuzzy inference systems. The proposed navigator combines two types of obstacle avoidance behaviors, one for the convex obstacles and one for the concave ones. The use of zero-order Takagi–Sugeno fuzzy inference systems to generate the elementary behaviors such as “reaching the middle of the collision-free space” and “wall-following” is quite simple and natural. However, one can always fear that the rules deduced from a simple human expertise are more or less sub-optimal. This is why we have tried to obtain these rules automatically. A technique based on a back-propagation-like algorithm is used which permits the on-line optimization of the parameters of a fuzzy inference system, through the minimization of a cost function. This last point is particularly important in order to extract a set of rules from the experimental data without having recourse to any empirical approach. In the case of a partially known environment, a hybrid method is used in order to exploit the advantages of global and local navigation strategies. The coordination of these strategies is based on a fuzzy inference system by an on-line comparison between the real scene and a memorized one. The planning of the itinerary is done by visibility graph and A ∗ algorithm. Fuzzy controllers are achieved, on the one hand, for the following of the planned path by the virtual robot in the theoretical environment and, on the other hand, for the navigation of the real robot when the real environment is locally identical to the memorized one. Both the methods have been implemented on the miniature mobile robot Khepera ® that is equipped with rough sensors. The good results obtained illustrate the robustness of a fuzzy logic approach with regard to sensor imperfections.

Qualitative navigation for mobile robots in indoor environments

This article describes a novel qualitative navigation method for mobile robots in indoor environments. The approach is based on qualitative representations of variations in sensor behavior between adjacent regions in space. These representations are used to localize and guide planning and reaction. Off-line, the system accepts as input a line-based diagram of the environment and generates a map based on a simple qualitative model of sensor behavior. During execution, the robot controller integrates this map into a reaction module. This architecture has been tested both in simulation and on a real mobile robot. Results from both trials are provided.

Sensor-based fuzzy navigation of an autonomous mobile robot in an indoor environment

The work presented in this paper deals with the problem of navigating a mobile robot either in an unknown indoor environment or in a partially known one. A navigation method based on the combination of elementary behaviors has been developed for an unknown environment. Most of these behaviors are achieved by means of fuzzy inference systems. The proposed navigator combines two types of obstacle avoidance behaviors, one for the convex obstacles and one for the concave ones. In the case of a partially known environment, a hybrid method is used to exploit the advantages of global and local navigation strategies. The coordination of these strategies is based on a fuzzy inference system that involves an on-line comparison between the real scene and a memorized one. Both methods have been implemented on the miniature mobile robot Khepera ® which is equipped with rough sensors. The good results obtained illustrate the robustness of a fuzzy logic approach with regard to sensor imperfections.

Vision-based navigation and environmental representations with an omnidirectional camera

Proposes a method for the visual-based navigation of a mobile robot in indoor environments, using a single omnidirectional (catadioptric) camera. The geometry of the catadioptric sensor and the method used to obtain a bird's eye (orthographic) view of the ground plane are presented. This representation significantly simplifies the solution to navigation problems, by eliminating any perspective effects. The nature of each navigation task is taken into account when designing the required navigation skills and environmental representations. We propose two main navigation modalities: topological navigation and visual path following. Topological navigation is used for traveling long distances and does not require knowledge of the exact position of the robot but rather, a qualitative position on the topological map. The navigation process combines appearance based methods and visual servoing upon some environmental features. Visual path following is required for local, very precise navigation, e.g., door traversal, docking. The robot is controlled to follow a prespecified path accurately, by tracking visual landmarks in bird's eye views of the ground plane. By clearly separating the nature of these navigation tasks, a simple and yet powerful navigation system is obtained.

Localization System for Mobile Robots in Indoor Environments

This article describes a system for locating a mobile robot moving in a known indoor environment. The simplest method for position estimation of a mobile robot is odometry; but, as the robot moves errors of estimation are accumulated and the accuracy