Using Passive RFID Technology for Mobile Robot Navigation and Environment Mapping

The increasing need for automated surveillance systems in indoor environments such as airports, warehouses, production plants, etc. has stimulated the development of intelligent systems based on mobile sensors. Differently from traditional non-mobile surveillance systems, those based on mobile robots are still in their initial stage of development, and many issues are currently open for investigation (Everett, 2003; DehuaI et al., 2007). The use of robots significantly expands the potential of surveillance systems, which can evolve from the traditional passive role in which the system can only detect events and trigger alarms, to active surveillance in which a robot can be used to interact with the environment, with humans or with other robots for more complex cooperative actions (Burgard et al., 2000; Vig & Adams, 2007). A major challenge in surveillance tasks using mobile robots is that of providing the robot with a suitable knowledge of the environment to both navigate safely and perform inspection tasks. Furthermore, in order to effectively exploit mobility and multifunctionality, it is important to develop integrated control systems, capable of addressing simultaneously a number of problems, such as task planning, dynamic task sequencing, resolution of conflicts for shared resources, event-based feedback control. These issues are part of our current research concerning the development of a multi-sensor Surveillance Mobile Robot (SMR). The SMR consists of a commercial mobile robot (see Fig. 1), which is equipped with various sensors including a laser rangefinder and a Radio Frequency IDentification (RFID) device, and takes advantage of a hybrid control architecture to implement both high-level functions, like mission execution monitoring, and low-level reactive control. So far, several behaviours and tasks have been implemented and experimented. In this chapter, we focus on environment mapping and exploration using RFID technology. An RFID device typically consists of radio frequency (RF) tags, a reader with one or more antennas, and software to process the tag readings. The reader interrogates the tags, receiving their ID code and other information stored in their memory. Tags can be either passive or active. Passive tags are activated by the electromagnetic field generated by the RFID antenna. Active tags, instead, are powered by an on-board battery (Finkenzeller, 2003). Applications of RFID include inventory management, industry automation, ID badges and 2

Three-dimensional (3D) mapping deals with the problem of building a map of the unknown environments explored by a mobile robot. In contrast to 2D maps, 3D maps contain richer information of the visited places. Besides enabling robot navigation in 3D, a 3D map of the robot surroundings could be of great importance for higher-level robotic tasks, like scene interpretation and object interaction or manipulation, as well as for visualization purposes in general, which are required in surveillance, urban search and rescue, surveying, and others. Hence, the goal of this thesis is to develop a system which is capable of reconstructing the surrounding environment of a mobile robot as a three-dimensional map. Microsoft Kinect camera is a novel sensing sensor that captures dense depth images along with RGB images at high frame rate. Recently, it has dominated the stage of 3D robotic sensing, as it is low-cost, low-power. For this work, it is used as the exteroceptive sensor and obtains 3D point clouds of the surrounding environment. Meanwhile, the wheel odometry of the robot is used to initialize the search for correspondences between different observations. As a single 3D point cloud generated by the Microsoft Kinect sensor is composed of many tens of thousands of data points, it is necessary to compress the raw data to process them efficiently. The method chosen in this work is to use a feature-based representation which simplifies the 3D mapping procedure. The chosen features are planar surfaces and orthogonal corners, which is based on the fact that indoor environments are designed such that walls, ground floors, pillars, and other major parts of the building structures can be modeled as planar surface patches, which are parallel or perpendicular to each other. While orthogonal corners are presented as higher features which are more distinguishable in indoor environment. In this thesis, the main idea is to obtain spatial constraints between pairwise frames by building correspondences between the extracted vertical plane features and corner features. A plane matching algorithm is presented that maximizes the similarity metric between a pair of planes within a search space to determine correspondences between planes. The corner matching result is based on the plane matching results. The estimated spatial constraints form the edges of a pose graph, referred to as graph-based SLAM front-end. In order to build a map, however, a robot must be able to recognize places that it has previously visited. Limitations in sensor processing problem, coupled with environmental ambiguity, make this difficult. In this thesis, we describe a loop closure detection algorithm by compressing point clouds into viewpoint feature histograms, inspired by their strong recognition ability. The estimated roto-translation between detected loop frames is added to the graph representing this newly discovered constraint. Due to the estimation errors, the estimated edges form a non-globally consistent trajectory. With the aid of a linear pose graph optimizing algorithm, the most likely configuration of the robot poses can be estimated given the edges of the graph, referred to as SLAM back-end. Finally, the 3D map is retrieved by attaching each acquired point cloud to the corresponding pose estimate. The approach is validated through different experiments with a mobile robot in an indoor environment

In this paper, we focus on configuring a low cost RFID (Radio Frequency IDentification) system with less RFID readers and low density RFID tag textiles for stable and accurate self-localization for an omni-directional mobile robot. An RFID system using multiple RFID readers and high density RFID tags has already been applied to an indoor mobile robot self-localization. However, the system production cost is relatively high. To reduce the production cost of the system while maintaining the self-localization performance, we redesign two new RFID systems configured with 20 RFID readers and 5 RFID readers enlarging the size of an RFID reader antenna. Particularly, a new likelihood function of the tag detection with the redesigned large size RFID reader antenna is proposed for Monte Carlo Localization (MCL). Furthermore, we come up with a novel arrangement of RFID tags installed in a hexagon pattern. The new arrangement of ID tags enables both low cost ID tag installation and high performance of self-localization. We examine the performances of robot self-localization with less RFID readers and lower density RFID tags installed on the floor based on MCL(Monte Carlo localization). The simulation demonstrates the validity of our proposed configuration of the RFID based self-localization.

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.

Recently, various autonomous mobile robots have been developed for practical use. To support the coexistence of robots and humans in real environments, we propose a concept named ‘Region with Velocity Constraints (RVC),’ which is set around hazardous areas. RVCs are regions where the velocities of the robot are constrained to predefined values. Inside the RVCs, the robot has to reduce its translational velocity to avoid predicted hazards such as collisions with obstacles, and to reduce its rotational velocity to prevent undesirable motions such as sharp turns. We also propose a motion planning method for navigating the mobile robot in an environment with RVCs based on the Navigation Function and Global Dynamic Window Approach. Our method generates a trajectory satisfying both translational and rotational velocity constraints to be compatible with the surroundings. Moreover, to demonstrate the validity of our method, we performed numerical simulations and experiments.

Mobile Robot is an extremely essential technology in the industrial world. Optimal path planning is essential for the navigation of mobile robots. The firefly algorithm is a very promising tool of Swarm Intelligence, which is used in various optimization areas. This study used the firefly algorithm to solve the mobile robot path-planning problem and achieve optimal trajectory planning. The objective of the proposed method is to find the free-collision-free points in the mobile robot environment and then generate the optimal path based on the firefly algorithm. It uses the A∗ algorithm to find the shortest path. The essential function of use the firefly algorithm is applied to specify the optimal control points for the corresponding shortest smooth trajectory of the mobile robot. Cubic Polynomial equation is applied to generate a smooth path from the initial point to the goal point during a specified period. The results of computer simulation demonstrate the efficiency of the firefly algorithm in generating optimal trajectory of mobile robot in a variable degree of mobile robot environment complexity.

Scene perception of mobile robot is that the robot realizes the perception and understanding of the surrounding environment through a series of sensors configured by itself, and the perception technology based on vision has always been a hot and difficult point in research. Therefore, in terms of visual environment perception algorithms, there have been numerous valuable research achievements in recent years. In particular, the object detection and segmentation algorithm based on convolutional neural network has shown good performance in simple scene, but there are still some limitations when these algorithms are directly applied to actual scenes. In this paper, we study the practical application of vision-based environmental perception of mobile robots in complex scenes, presents a unified algorithm architecture of object detection and road segmentation, and build a vision-based mobile robot's environment perception system. Firstly, the image acquisition of the surrounding environment is completed by the computer camera mounted on the robot, and then the obstacle detection and the segmentation of the drivable area are achieved by using the target detection and segmentation algorithm. In order to meet the real-time requirements, the detection and segmentation algorithms share the same feature extraction network, and are jointly trained as one framework. Finally, according to the detection and segmentation results, the robot can automatically avoid obstacles and move in the drivable area.

Developing an autonomous indoor mobile robot navigation system from scratch is very difficult and it takes a long time. It requires a series of complex tasks such as handling given sensor inputs, calculating all the information needed for autonomous navigation, and controlling actuators required for movement. In this paper, an autonomous navigation system for indoor mobile robots is introduced mainly based on open source provided by the robot operating system. The presented system is capable of autonomously navigating an unstructured indoor environment avoiding collision with static or dynamic objects. To this end, the system consists of three main modules: mapping, localization, and planning. The mapping module builds a global map for an unknown environment by means of a simultaneous localization and mapping algorithm based on laser scanner data. The localization module estimates the mobile robot’s pose within the prebuilt map by way of an adaptive Monte Carlo localization approach. The planning module builds a local cost map for collision avoidance, generates collision-free trajectories to reach a goal pose based on the cost map, and produces control commands to follow the trajectories. The presented system has been tested not only in simulation environments built in the Gazebo simulator but also in real environments utilizing the Jackal mobile robot, to validate its performance for autonomous navigation including collision avoidance.

New mutual agreement protocol to secure mobile RFID-enabled devices

In this paper, a novel method of obstacle recognition in indoor environment for mobile robot using RFID (Radio Frequency Identification) technology and stereo vision is proposed as it is inexpensive, flexible and easy to use in practical environment. Because information of the obstacles or environment can be written in ID tags, the proposed method can detect the obstacles easily and quickly compared with the other methods. The Radio Frequency is not so stable, so the Bayes rule was introduced to calculate probability where the ID tag exists in order to improve the accuracy of localizing obstacle with the ID tags. Then the stereo camera is started to process the image of ROI where the obstacle exists. The proposed method does not need to process all image and easily gets some information of obstacle such as size, color, thus decreases the processing computation. This paper introduces the architecture of the proposed method and presents some experimental results.

The rapid expansion in the industrial revolution has been aroused to utilize of the robot in various high accuracy and productivity applications. Optimal path planning is an important concern in navigation of mobile robot interesting with finding an optimal path. The firefly algorithm is considered as an increasingly promising tool of Swarm Intelligence, which is used in various optimization areas. The objective of the proposed method is to find the free-collision points in mobile robot environment and generate the optimal path based on firefly algorithm and D* algorithm. D* algorithm has been applied to find the shortest path. The essential function of applying the firefly algorithm and Quadratic parametric equations is to iterative globally search and determine the accurate positions of a set of intermediate via points within the free space and generating the corresponding smooth trajectory at a specified time respectively. The collision with obstacles can be efficiently avoided by constructing the free Cartesian space, which guarantee a free collision path planning. The simulation results illustrate the efficiency of proposed methods performance in finding and determining the optimal path and trajectory even in various degrees of environmental complexity.

Wireless Indoor Localization Using Passive RFID Tags

In this paper, a novel genetic algorithm based approach to path planning of a mobile robot is proposed. The major characteristic of the proposed algorithm is that the chromosome has a variable length. The location target and obstacles are included to find a path for a mobile robot in an environment that is a 2D workplace discretized into a grid net. Each cell in the net is a gene. The number of genes in one chromosome depends on the environment. The locations of the robot, the target and the obstacle are marked in the workplace. The proposed algorithm is capable of generating collision-free paths for a mobile robot in both static and dynamic environments. In a static environment, the generated robot path is optimal in the sense of the shortest distance. The effectiveness of the proposed model is demonstrated by simulation studies.

Sensor Cooperation and World Perception of the Autonomous Mobile Robot Romane

Recently, the radio frequency identification (RFID) system is paid attention as an identification source that can realize a ubiquitous environment. One of the important technologies that apply the RFID system is the indoor position estimation of RFID tags. For example, it can be applied to an indoor navigation system for handicapped person and recognition of ambient environment by a mobile robot, etc. In this paper, we propose a new positional estimation method to estimate the RFID tag position by using the tune of the antenna directivity of the RFID reader (D-CRR). This method controls the directivity of the antenna by dynamically changing the distance between the antenna elements of the RFID reader. We show the effectiveness of the proposed method by computer simulations.