Mobile Robot Navigation

This paper presents an obstacle detection and avoidance of mobile robot using stereo camera for indoor environment. Block matching algorithm is solved the correspondence problem occurred in comparing stereo images (left and right sensors of the camera). The algorithm uses Sum of Absolute Differences (SAD). Left image works as a reference block to the right image and the output is disparity mapping or depth maps with the left coordinate system. A pair of camera or stereo vision baseline is based on horizontal configuration. The block matching technique is briefly described with the performance of its output. The curve fitting tool would determine the range of each obstacle detected in disparity mapping. The programming activities are using Matlab software starting from capturing images until navigation of mobile robot.

The problem of mobile robot navigation has received a noticeable attention over last few years. Several different approaches were presented, each having major limitations. In this paper a new, agentbased solution the problem of mobile robots navigation is proposed. It is based on a novel representation of the environment, that divides it into a number of distinct regions, and assigns autonomous software Space Agents to supervise it. Space Agents create a graph, that represents a high-level structure of the entire environment. The graph is used as a virtual space, that robot controlling agents work in. The most important features of the approach are: path planning for multiple robots based on most recent data available in the system, automated collision avoidance, simple localization of a "lost robot" and unrestricted scalability.

This work addresses the real time control of the Khepera mobile robot [1] navigation in a maze with reflector walls. Boolean Neural Networks such as RAM [2] and GSN [3] models are applied to drive the vehicle, following a light source, while avoiding obstacles. Both neural networks are implemented with simple logic and arithmetic functions (NOT, AND, OR, Addition, and Comparison), aiming to improve system speed. The results obtained are compared with two other control strategies: Multi-layer Perceptron (MLP) [4] and Fuzzy Logic [5].

In this study, a path smoothing strategy is proposed for sensor-based coverage problems. Smooth paths are generated for the coverage problems considering mobile robot kinematics constraints. An open agent architecture-based control structure is used to implement the proposed approach on real robots. The algorithm is coded with C++ and implemented on P3-DX mobile robots in MobileSim simulation environments. It is

Ahstract-In this paper, a software simulation model is developed for a two wheels driven mobile robot motion controller that can navigate the robot safely through an unknown environment. The work involves the design of a controller, which has four functions: motion control; obstacle avoidance; self-location; and path planning both global and local. The proposed controller is responsible for the mobile robot navigation after it generates a trajectory between start and goal points. Also it enables the robot to operate successfully in the presence of various obstacles present in any user built maps. The mobile robot is able to locate its position on any given map. The dynamic of the mobile robot is examined and the time constant of the two motors, which affects the direction of the mobile robot motion, is controlled. Obstacle avoidance is implemented with Fuzzy Logic Controller. The numerical experiments demonstrated that the indoor robot navigated successfully in tight corridors, avoided obstacles and dealt with a variety of world maps with various irregular wall shapes that were presented to it.

Mobile robot localization concerns estimating the position and heading of the robot relative to its environment. Basically , the mobile robot moves around without initial knowledge of the environment. Therefore, a scheme to handle it is necessary, such as the Kalman Filters. Rather than the Extended Kalman Filter, we choose to employ the sigma points approach. In this paper, we take into consideration the method proposed by Van Der Merwe to determine the sigma points in Unscented Kalman Filter. The simulation and results verify that the Unscented Kalman Filter works pretty well for locating the mobile robot.

This paper presents a new sensor-based online method for generating collision-free near-optimal paths for mobile robots pursuing a moving target amidst dynamic and static obstacles. At each iteration, first the set of all collision-free directions are calculated using velocity vectors of the robot relative to each obstacle and target, forming the Directive Circle (DC), which is a novel concept. Then, a direction close to the shortest path to the target is selected from feasible directions in DC. The DC prevents the robot from being trapped in deadlocks or local minima. It is assumed that the target's velocity is known, while the speeds of dynamic obstacles, as well as the locations of static obstacles, are to be calculated online.

Significant advances in sensor technology, along with economies of scale due to large production volumes have supported the miniaturization of navigation sensors, allowing widespread low-cost integration on Unmanned Aircraft Systems (UAS). In small-size UAS applications, standalone sensors are not a viable option since the reduction in navigation sensor form-factor, weight and cost typically results in lowered accuracy and precision. Fusion of multiple sensor measurements in UAS navigation systems can support greater accuracy, integrity and update rates than is achievable employing individual sensors. This chapter introduces the fundamentals of state-estimation methods employed on UAS and presents different sensor integration architectures, along with an assessment of their advantages and trade-offs. Attention is devoted primarily to recursive optimal estimation algorithms such as the Kalman Filter and its variants owing to its prolific employment in various classes of UAS. The need to support robust navigation performance in Global Navigation Satellite System (GNSS) denied environments, and the proliferation of visual sensors has led to the development of numerous methods for integrating visual sensor measurements (primarily) with inertial sensors. Therefore, the reader is introduced to the most popular system architectures for visual-inertial sensor integration, in order to provide an understanding of the current state-of-the-art and to support the identification of future research pathways.

Autonomous mobile robots have been used to carry out different tasks without continuous human guidance. To achieve the tasks, they must be able to navigate and avoid different kinds of obstacles that faced them. Navigation means that the robot can move through the environment to reach a destination. Obstacles avoidance considers a challenge which robot must overcome. In this work, the authors propose an efficient technique for obstacles avoidance through navigation of swarm mobile robot in an unstructured environment. All robots cooperate with each other to avoid obstacles. The robots detect the obstacles position around them and store their positions in shared memory. By accessing the shared memory, the other robots of the swarm can avoid the detected obstacles when they face them. To implement this idea, the Authors used a MATLAB® and V-REP® (Virtual Robot Experimentation Platform).

In this study a fuzzy logic controller for mobile robot navigation has been designed. The designed controller deals with the uncertainty and ambiguity of the information the system receives. The technique has been used on an experimental mobile robot which uses a set of seven ultrasonic sensors to perceive the environment. The designed fuzzy controller maps the input space (information coming from ultrasonic sensors) to a safe collision-avoidance trajectory (output space) in real time. This is accomplished by an inference process based on rules (a list of IF-THEN statements) taken from a knowledge base. The technique generates satisfactory direction and velocity maneuvers of the autonomous vehicle which are used by the robot to reach its goal safely. Simulation and experimental results show the method can be used satisfactorily by wheeled mobile robots moving on unknown static terrains.

A feature detection system has been developed for real-time identification of lines, circles and people legs from laser range data. A new method suitable for arc/circle detection is proposed: the Inscribed Angle Variance (IAV). Lines are detected using a recursive line fitting method. The people leg detection is based on geometrical relations. The system was implemented as a plugin driver in Player, a mobile robot server. Real results are presented to verify the effectiveness of the proposed algorithms in indoor environment with moving objects.

Real-time autonomous navigation in unpredictable environments is an essential issue in robotics and artificial intelligence. In this chapter, an adaptive neurofuzzy controller is proposed for mobile robot navigation with local information. A combination of multiple sensors is used to sense the obstacles near the robot, the target location, and the current robot speed. A fuzzy logic system with 48 fuzzy rules is designed. Two learning algorithms are developed to tune the parameters of the membership functions in the proposed neuro-fuzzy model and automatically suppress redundant fuzzy rules from the rule base. The "dead cycle" problem is resolved by a state memory strategy. Under the control of the proposed neuro-fuzzy model, the mobile robot can preferably "see" the surrounding environment, avoid static and moving obstacles automatically, and generate reasonable trajectories toward the target. The effectiveness and efficiency of the proposed approach are demonstrated by simulation and experiment studies.

We would like to invite you to join this exciting new project as a chapter contributor. Since this is a textbook, a great deal of this chapter entails a survey on the topic under the paradigm of cyber-physical systems, what can be done onboard and remotely, the distributed nature of the system and some exercises on futurology (anticipating trends can shed some light on upcoming designs). IET will bring great visibility to your work. You are welcome to suggest another topic/chapter title if you feel it would be more suitable. Each chapter should be around 20-25 pages each and can be submitted as a Word or Latex File. The IET will send you additional information (formatting, permission form, etc.) with the contributor's agreement once you have agreed to contribute to the book. Visit http:// www.theiet.org/resources/author-hub/books/index.cfm to get all information you need as a contributor to an IET research-level book. Each book is expected to have a total number of 500 printed pages (based on approximately 550 words per page with a 20% allowance for figures and tables). We have included a tentative schedule and list of topics below. If this is something you would consider, please send me the title of your chapter, a short description/abstract of the chapter content, and your full contact details. We will expect original content and new results for this book. You can, of course, reuse published material but the percentage of material reuse for the chapter should be less than 40%. The IET will run a piracy software on the full manuscript to control that you are including original material and will reject chapters who contain a large amount of already-published material so please do take this into consideration.

In this study, the method of direct-motion parallel parking is introduced both in theory and in simulations for the vehicle with and without a trailer. The results show that not only a vehicle with a trailer park in parallel using direct motion, but also a vehicle with a trailer park in parallel in direct motion after the final value of the orientation angle is satisfied. Simulation results are given to verify the effectiveness of the proposed method.

Paper videos can be reached from this URL: http://www.youtube.com/playlist?list=PLENSkat0854vszuVfu9mnrEvHsVQGBBgB

In this paper, we present a method to navigate a mobile robot using a webcam. This method determines the shortest path for the robot to transverse to its target location, while avoiding obstacles along the way. The environment is first captured as an image using a webcam. Image processing methods are then performed to identify the existence of obstacles within the environment. Using the Cell Decomposition method, locations with obstacles are identified and the corresponding cells are eliminated. From the remaining cells, the shortest path to the goal is identified. The program is written in MATLAB with the Image Processing toolbox. The proposed method does not make use of any other type of sensor other than the webcam.

An Autonomous Mobile Robot is an artificially intelligent vehicle capable of traveling in unknown and unstructured environments independently. Among the proposed approaches in the literature to handle the navigation problem of a mobile robot is the simple fuzzy reactive approach. This approach, however, occasionally suffers from two major problems, i.e., escaping from trap situations and the combinatorial explosion of the if-then rules in the inference engine. This paper presents a neuro-fuzzy reasoning approach for mobile robot navigation. The proposed approach has the advantage of greatly reducing the number of if-then rules by introducing weighting factors for the sensor inputs, thus inferring the reflexive conclusions from each input to the system rather than putting all the possible states of all the inputs to infer a single conclusion. Four simple neural networks are used to determine the weighting factors. Each neural network is responsible for determining the weighting facto...

Motor schemas serve as the basic unit of behavior specifica tion for the navigation of a mobile robot. They are multiple concurrent processes that operate in conjunction with asso ciated perceptual schemas and contribute independently to the overall concerted action of the vehicle. The motivation be hind the use of schemas for this domain is drawn from neuro- scientific, psychological, and robotic sources. A variant of the potential field method is used to produce the appropriate velocity and steering commands for the robot. Simulation re sults and actual mobile robot experiments demonstrate the feasibility of this approach.

In this study, a Best First Search (BFS) Based Intelligent PID Tuning Method is proposed for a mobile robot path tracking problem. BFS is an informed search strategy in Artificial Intelligence (AI). It has been used in many areas of intelligent systems. Here, it is used to search values of both ultimate period and ultimate gain of Ziegler-Nichols (ZN) PID Tuning Method. So, using these ultimate period and gain, PID parameters can be c from tables of ZN method. The performance measure in this problem is described as minimizing total error during any specified trajectory tracking. Thus the proposed method does not depend on the system model. It only needs error information during the tracking control. So, this method can be used to tune PID parameters of any tracking controller for a system with unknown system dynamics. The proposed method is tested with simulations in MATLAB during trajectory tracking of a mobile robot. In addition, it is compared with classical PID and conventional PID tuning methods including Ziegler-Nichols method, Tyreus-Luyben method, and Shinskey method.

Keywords: Intelligent PID tuning, best first search, autonomous mobile robot, mobile robot path tracking problem, computed torque PID control, dynamic model, Ziegler-Nichols, Tyreus-Luyben, Shinskey.

An efficient path planner for autonomous car-like vehicles should handle the strong kinematic constraints, particularly in confined spaces commonly encountered while maneuvering in city traffic, and should enable rapid planning, as the city traffic scenarios are highly dynamic. State-of-the-art planning algorithms handle such difficult cases at high computational cost, often yielding non-deterministic results. However, feasible local paths can be quickly generated leveraging the past planning experience gained in the same or similar environment. While learning through supervised training is problematic for real traffic scenarios, we introduce in this paper a novel neural network-based method for path planning, which employs a gradient-based self-supervised learning algorithm to predict feasible paths. This approach strongly exploits the experience gained in the past and rapidly yields feasible maneuver plans for car-like vehicles with limited steering-angle. The effectiveness of suc...

This paper describes a system to improve indoor navigation through use of radio frequency identification (RFID) technology. The terminal unit is an embedded system equipped with an RFID reader for localization, a mobile robot for navigation, and a combination of ultrasonic and IR sensors for obstacle detection and avoidance during navigation. To increase accuracy of an indoor guidance system, a triangulation method is proposed to accurately detect the location. While the proposed method can be verified by many methods, the accuracy is demonstrated through use of a mobile robot. It navigates to a designated location through continuously monitoring all RFID tags in the vicinity, localizing itself, and calculating the path to the destination.

In this project we are brougth to construct a unicycle robot to control it. using the cinematic modeling of the unicycle robot under MATLAB / SIMULINK we succeeded to characterize and enslave our system while controlling it in speed and CAP. Then, using the graphical interface V-REP we developed the prototype of the robot. Then we succeeded in communicating the environment MatlAb / Simulink with the graphical interface V-REP, this allowed us to control our robo
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An efficient path planner for autonomous car-like vehicles should handle the strong kinematic constraints, particularly in confined spaces commonly encountered while maneuvering in city traffic, and should enable rapid planning, as the city traffic scenarios are highly dynamic. State-of-the-art planning algorithms handle such difficult cases at high computational cost, often yielding non-deterministic results. However, feasible local paths can be quickly generated leveraging the past planning experience gained in the same or similar environment. While learning through supervised training is problematic for real traffic scenarios, we introduce in this paper a novel neural network-based method for path planning, which employs a gradient-based self-supervised learning algorithm to predict feasible paths. This approach strongly exploits the experience gained in the past and rapidly yields feasible maneuver plans for car-like vehicles with limited steering-angle. The effectiveness of suc...

In this work we present a novel approach to the analysis of current grid-based path planning algorithms. Traditional path planners limit an agent’s possible headings to increments of π/4 which results in unnatural and suboptimal paths that are difficult to traverse in practice. Our work compares this traditional solution to a number of alternative planners that use varying methods for heading calculation. We prove that planners which are not limited to this subset of headings are significantly more efficient even when accounting for computational times. Based on these findings we believe a significant amount of time and physical energy can be saved simply by adopting free-form natural planners in robotic agents.

Localisation and mapping are the key requirements in mobile robotics to accomplish navigation. Frequently laser scanners are used, but they are expensive and only provide 2D mapping capabilities. In this paper we investigate the suitability of the Xbox Kinect optical sensor for navigation and simultaneous localisation and mapping. We present a prototype which uses the Kinect to capture 3D point cloud data of the external environment. The data is used in a 3D SLAM to create 3D models of the environment and localise the robot in the environment. By projecting the 3D point cloud into a 2D plane, we then use the Kinect sensor data for a 2D SLAM algorithm. We compare the performance of Kinectbased 2D and 3D SLAM algorithm with traditional solutions and show that the use of the Kinect sensor is viable. However, its smaller field of view and depth range and the higher processing requirements for the resulting sensor data limit its range of applications in practice.

In this study, the method of direct-motion diagonal and perpendicular parking is introduced both in theo-ry and in simulations for the vehicle with and without a trailer. The results show that not only a vehicle with a trailer park in diagonal or perpendicular using direct motion, but also a vehicle with a trailer park in diagonal and perpendicular in direct motion after the final value of the orientation angle is satisfied. Simulation results are given to verify the effec-tiveness of the proposed method. Keywords: diagonal, perpendicular, direct motion, parking, trailer, car-like vehicle Paper videos can be reached from this URL: http://www.youtube.com/playlist?list=PLENSkat0854vGceT1fX4Yr6Yw0UqqW_vl

Modern Unmanned Aircraft Systems (UAS) employ a variety of sensors and multi-sensor data fusion techniques to provide advanced operational capabilities and trusted autonomy (i.e., required levels of safety, integrity, security and interoperability), when sharing the airspace with other manned and unmanned platforms. Low-cost and high-performance Navigation and Guidance Systems (NGS) for UAS have been developed at RMIT University by employing a combination of compact and lightweight sensors to satisfy the Required Navigation Performance (RNP) in all flight phases. Additionally, recent research at RMIT has focused on the development of a unified approach to separation assurance and collision avoidance suitable for UAS cooperative/non-cooperative sensor architectures and allowing for an extended range of operations both in mission-essential and safety-critical tasks. The Sense-and-Avoid Unified Method (SUM) developed at RMIT provides an innovative analytical framework to combine real-time measurements (and associated uncertainties) of navigation states, platform dynamics and tracking observables to produce high-fidelity dynamic geo-fences suitable for integration in future avionics, Air Traffic Management (ATM) and defense decision support tools.

This paper surveys the developments of the last 20 years in the area of vision for mobile robot navigation. Two major components of the paper deal with indoor navigation and outdoor navigation. For each component, we have further subdivided our treatment of the subject on the basis of structured and unstructured environments. For indoor robots in structured environments, we have dealt separately with the cases of geometrical and topological models of space. For unstructured environments, we have discussed the cases of navigation using optical flows, using methods from the appearance-based paradigm, and by recognition of specific objects in the environment.

A more natural interaction between humans and mobile robots can be achieved by bridging the gap between the format of spatial knowledge used by robots and the format of languages used by humans. This enables both sides to communicate by using shared knowledge. Spatial knowledge can be (re)presented in various ways to increase the interaction between humans and mobile robots. One effective way is to describe the route verbally to the robot. This method can permit computer language-naive users to instruct mobile robots, which understand spatial descriptions, to naturally perform complex tasks using succinct and intuitive commands. We present a spatial language to describe route-based navigation tasks for a mobile robot. The instructions of this spatial language are implemented to provide an intuitive interface with which novice users can easily and naturally describe a navigation task to a mobile robot in a miniature city or in any other indoor environment. In our system, the instructions of the processed route are analyzed to generate a symbolic representation via the instruction interpreter. The resulting symbolic representation is supplied to the robot motion planning stage as an initial path estimation of route description and it is also used to generate a topological map of the route’s environment.

Being able to build a map of the environment and to simultaneously localize within this map is an essential skill for mobile robots navigating in unknown environments in absence of external referencing systems such as GPS. This so-called simultaneous localization and mapping (SLAM) problem has been one of the most popular research topics in mobile robotics for the last two decades and efficient approaches for solving this task have been proposed. One intuitive way of formulating SLAM is to use a graph whose nodes correspond to the poses of the robot at different points in time and whose edges represent constraints between the poses. The latter are obtained from observations of the environment or from movement actions carried out by the robot. Once such a graph is constructed, the map can be computed by finding the spatial configuration of the nodes that is mostly consistent with the measurements modeled by the edges. In this paper, we provide an introductory description to the graph-based SLAM problem. Furthermore, we discuss a state-of-the-art solution that is based on least-squares error minimization and exploits the structure of the SLAM problems during optimization. The goal of this tutorial is to enable the reader to implement the proposed methods from scratch.

This paper investigates the use of refocused optical mouse sensors for odometry in the field of outdoor robotic navigation. Optical mouse sensors like the ADNS-2610 are small, inexpensive, non-contact devices, which integrate a CMOS camera and DSP hardware to provide two-dimensional optical displacement measurements. Current research indicates that vertical height variance contributes as a dominant cause of systematic error to horizontal displacement measurements, which raises significant problems for irregular environments encountered in outdoor robotic navigation. In this paper we propose two approaches to mitigate this systematic error induced by height variance. The efficacy and robustness of the proposed approaches are tested by experimentation on an asphalt concrete road surface and by simulation.

In this manuscript, we propose an on-line trajectorytracking algorithm for nonholonomic Differential-Drive Mobile Robots (DDMRs) in the presence of possibly large parametric and measurement uncertainties. Most mobile robot tracking techniques that depend on reference RF beacons rely on approximating line-of-sight (LOS) distances between these beacons and the robot. The approximation of LOS is mostly performed using Received Signal Strength (RSS) measurements of signals propagating between the robot and RF beacons. However, accurate mapping between RSS measurements and LOS distance remains a significant challenge and is almost impossible to achieve in an indoor reverberant environment. This paper contributes to the development of a neighboring optimal control strategy where the two major control tasks, trajectory tracking and point stabilization, are solved and treated as a unified manner using RSS measurements emitted from Radio Frequency IDentification (RFID) tags. The proposed control scheme is divided into two cascaded phases. The first phase provides the robot's nominal control inputs (speeds) and its trajectory using full-state feedback. In the second phase, we design the neighboring optimal controller, where RSS measurements are used to better estimate the robot's pose by employing an optimal filter. Simulation and experimental results are presented to demonstrate the performance of the proposed optimal feedback controller for solving the stabilization and trajectory tracking problems using a DDMR.

In this study, the method of direct-motion diagonal and perpendicular parking is introduced both in theo-ry and in simulations for the vehicle with and without a trailer. The results show that not only a vehicle with a trailer park in diagonal or perpendicular using direct motion, but also a vehicle with a trailer park in diagonal and perpendicular in direct motion after the final value of the orientation angle is satisfied. Simulation results are given to verify the effec-tiveness of the proposed method. Keywords: diagonal, perpendicular, direct motion, parking, trailer, car-like vehicle Paper videos can be reached from this URL: http://www.youtube.com/playlist?list=PLENSkat0854vGceT1fX4Yr6Yw0UqqW_vl

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Computer Vision (CV) and Sensors play a decisive role in the operation of Unmanned Aerial Vehicle (UAV), but there exists a void when it comes to analysing the extent of their impact on the entire UAV system. In general, the fact that a UAV is a Cyber-Physical System (CPS) is not taken into account. In this proposal, we propose to expand on earlier books covering the use of CV and sensing in UAVs. Among other things, an entirely autonomous UAV can help to (i) obtain information about the environment, (ii) work for an extended period of time without human interference, (iii) move either all or part of itself all over its operating location devoid of human help and (iv) stay away from dangerous situations for people and their possessions. A Vision System (VS) entails the way CV data will be utilized, the appropriate architecture for total avionics integration, the control interfaces, and the UAV operation. Since the VS core is its sensors and cameras, multi-sensor fusion, navigation, hazard detection, and ground correlation in real time are important operational aspects that can benefit from CV knowledge and technology. This book will aim to collect and shed some light on the existing information on CV software and hardware for UAVs as well as pinpoint aspects that need additional thinking. It will list standards and a set of prerequisites (or lack of them thereof) when it comes to CV deployment in UAVs. The issue of data fusion takes a centre place when the book explores ways to deal with sensor data and images as well as their integration and display. The best practices to fuse image and sensor information to enhance UAV performance by means of CV can greatly improve all aspects of the corresponding CPS. The CPS viewpoint can improve the way UAVs interact with the Internet of Things (IoT), use cloud computing, meet communications requirements, implement hardware/software paradigms necessary to handle video streaming, incorporate satellite data, and combine CV with Virtual/Augmented Realities.

VOLUME 1-CONTROL AND PERFORMANCE: This tome explores how sensors and computer vision technologies are used in unmanned aerial vehicles for the navigation, control, stability, reliability, guidance, fault detection, self-maintenance, strategic replanning and reconfiguration of the entire system. It helps analyse the manner UAVs interact with the Internet of Things (IoT), use cloud computing, meet communications requirements, implement hardware/software paradigms necessary to handle still imagery, video streaming, incorporate satellite data, and combine computer vision with virtual/augmented realities (VR/AR).NB: This is planned to be the companion volume of Estrela, Hemanth, Saotome (Eds) / Imaging and Sensing for Unmanned Aerial Vehicles: Volume 2-Deployment and Applications

This article presents a summary of applications of chaos and fractals in robotics. Firstly, basic concepts of determin‐ istic chaos and fractals are discussed. Then, fundamental tools of chaos theory used for identifying and quantifying chaotic dynamics will be shared. Principal applications of chaos and fractal structures in robotics research, such as chaotic mobile robots, chaotic behaviour exhibited by mobile robots interacting with the environment, chaotic optimization algorithms, chaotic dynamics in bipedal locomotion and fractal mechanisms in modular robots will be presented. A brief survey is reported and an analysis of the reviewed publications is also presented.

A location and tracking system becomes very important to our future world of pervasive computing, where information is all around us. Location is one of the most needed information for emerging and future applications. Since the public use of GPS satellite is allowed, several state-of-the-art devices become part of our life, e.g. a car navigator and a mobile phone with a built-in GPS receiver. However, location information for indoor environments is still very limited. Several techniques are proposed to get location information in buildings such as using a radio signal triangulation, a radio signal (beacon) emitter, or signal fingerprinting. Using Radio Frequency Identification (RFID) tags is a new way of giving location information to users. Due to its passive communication circuit, RFID tags can be embedded almost anywhere without an energy source. The tag stores location information and gives it to any reader that is within a proximity range which can be up to 10-15 meters for UHF RFID systems. We propose an RFID-based system for navigation in a building for blind people or visually impaired. The system relies on the location information on the tag, a user's destination, and a routing server where the shortest route from the user's current location to the destination. The navigation device communicates with the routing server using GPRS networks. We build a prototype based on our design and show some results. We found that there are some delay problems in the devices which are the communication delay due to the cold start cycle of a GPRS modem and the voice delay due to the file transfer delay from MMC module.

We present an innovative path following system based upon multi-camera visual odometry and visual landmark matching. This technology enables reliable mobile robot navigation in real world scenarios including GPS-denied environments both indoors and outdoors. We recover paths in full 3D, making it applicable to both on and off-road ground vehicles. Our controller relies on pose updates from visual odometry, allowing us to achieve path following even when only a joystick drive interface to the base robot platform is available. We experimentally investigate two specific applications of our technology to autonomous navigation on ground vehicles -non line-ofsight leader-following (between heterogeneous platforms) and retro-traverse to home base. For safety and reliability we add dynamic short range obstacle detection and reactive avoidance capabilities to our controller. We show the results for end-toend real time implementation of this technology using current off-the-shelf computing and network resources in challenging environments.

The main purpose of this article is to present the legal problems of autonomous shipping. Therefore, the author proposes the classification of autonomous merchant ships; underling the difference between autonomous and unmanned vessels. This allows for extensive analysis of the law of the sea and international maritime law provisions applicable to autonomous and unmanned vessels. As a result, the regulatory barriers and difficulties in terms of compliance with the international maritime standards by the analyzed categories of vessels are presented.