An improved fuzzy logic based navigation system for mobile robots

Generally, the mobile robots are researched and used in many applications. The fundamental method of navigation is localization, which includes aspects such as sensing, mapping, localization, planning and control. In order to we build the navigation system for mobile robot operating in unmodified environments, we must know the mobile robot’s position in reference frame. Typically reference frame is a Cartesian or we can specify the landmark as reference position. The dead reckoning based on odometry is widely to identify and classify landmarks. Dead reckoning systems estimate the position of the mobile robot with respect to an initial point by measuring linear and angular velocities or accelerations. There are also hybrid models, which have sensor systems of both dead reckoning and landmark recognition. Dead reckoning is the estimate of current position based on a previously determined position or estimated speeds from a known past position over time. This can also determine the future position by projecting an ordered course and speed of advance from a known present position. A disadvantage of dead reckoning is that new positions are calculated solely from previous positions. The errors of the process are cumulative, so the error in the position fix grows with time (Hans-Joachim von der Hardt & Didier Wolf Rene Husson, 1996; Hye Ri Park et al., 2009). Odometry is widely used in mobile robots to estimate their position relative to reference frame. This method estimates the position over time with errors accumulation due to the integration of velocity measurements (Cristina Tar in Sauer and Hermann Brugger, 2001; Houshangi, N. and Azizi, F., 2006; Agostino Martinelli, 2002). The navigation system for mobile robot based on odometry has typically problem with inaccuracy. Because of the odometry problems navigation systems for mobile robot application cannot work solely on dead reckoning. This is the reason that the navigation system needs the addition of sensory information. Additional sensors can be defined as information provided to assist the accomplishment of a task by a system. Practically, sensor fusion refers to the integration process where the different sources of sensory information are combined into one representational format. Typically in navigation systems sensory information can be fused from complementary sensors, redundant sensors or even from a single sensor over a period of time. The advantage of sensory fusion provides uncertainty and noise reduction, failure toleration and extended flexibility of sensor ability.

Mobile robot, especially wheeled mobile robot, with its simple mechanical structure and inherent agility, has attracted significant attentions for dynamic environment applications in the last two decades (Pin et al. 1994 and Purwin 2006). A general mobile robot Guidance, Navigation and Control (GNC) system is illustrated in the following figure. The main function of guidance system is to generate a feasible trajectory command, usually in multiple dimension of freedom (DOF) to achieve a robot task. The objective of control system is to drive the mobile robot following the commanded trajectory with acceptable tracking errors and stability margin. The function of robot navigation system is to provide accurate position, velocity and/or orientation information for the control system and guidance system. A stable and accurate navigation system is the bridge between the guidance system and control system of mobile robots, which ultimately determines the robot performance.

One of the ultimate goals of mobile robotics research is to build robots that can safely carry out missions in hazardous and populated environments. Most of today's commercial mobile devices scale poorly along this dimension. Their motion planning relies on accurate, static models of the environments, and therefore they often fail their mission if humans or other unpredictable obstacles block their path. To build autonomous mobile robots one has to build systems that can perceive their environments, react to unforeseen circumstances, and plan dynamically in order to achieve their mission. Thus, the objective of the motion planning and control problem is to find collision-free trajectories, in static or dynamic environments containing some obstacles, between a start and a goal configuration. It has attracted much research in recent years. In this context the term control has a broad meaning that includes many different controls, such as low-level motor control, and behaviour control, where behaviour represents many complicated tasks, like obstacle avoidance and goal seeking. This article describes an intelligent motion planning and navigation system for omnidirectional mobile robots based on fuzzy logic. Owing to its simplicity and hence its short response time, the fuzzy navigator is especially suitable for on-line applications with strong real-time requirements. On-line planning is an on-going activity. The planner receives a continuous flow of information about occurring events and generates new commands in response to the incoming events, while previously planned motions are being executed. The fuzzy-rule-base of the proposed system combines the repelling influence, which is related to the distance and the angle between the robot and nearby obstacles, with the attracting influence produced by the distance and the angular difference between the actual direction and position of the robot and the final configuration, to generate actuating commands for the mobile platform. It can be considered as an on-line local navigation method for omnidirectional mobile robots for the generation of instantaneous collision-free motions. This reactive system is especially suitable for real-time applications. The use of fuzzy logic leads to a transparent system which can be tuned by hand or by a set of learning rules. Furthermore, this approach allows obstacle avoidance and navigation in dynamic environments. The functioning of the fuzzy motion planner with respect to omnidirectional mobile robots and results of simulated experiments are presented.

Localization of a vehicle is a key component for driving assistance or autonomous navigation. In this work, we propose a visual positioning system (VPS) for vehicle or mobile robot navigation. Different from general landmark-based or model-based approaches, which rely on some predefined known landmarks or a priori information about the environment, no assumptions on the prior knowledge of the scene are made. A stereo-based vision system is built for both extracting feature correspondences and recovering 3-D information of the scene from image sequences. Relative positions of the camera motion are then estimated by registering the 3-D feature points from two consecutive image frames. Localization of the vehicle is finally given by the reference to its initial position. I. INTRODUCTION Localization and tracking of a vehicle are major compo- nents for providing positions, directions and travel informa- tion to the driver. They also serve as key technologies for building autonomous navigation systems. Many approaches have been proposed for locating a mobile robot or a ve- hicle based on various techniques. The most commonly used methods include dead-reckoning techniques, navigation using active beacons, landmark-based navigation, map-based navigation, global positioning system (GPS), and vision- based positioning. Dead-reckoning is a procedure for determining the present location of a vehicle by advancing some previous position through known path and velocity information over a given period of time. Since an odometer and optical sensors for wheel direction detection are easily installed on a vehicle, dead-reckoning is usually a less expensive method for vehicle localization. However, the integration of incremental motion information over time will lead to accumulation of errors. In the case of long distance travel, accumulation of orientation error will make position errors diverge as the increase of driving time. Active beacons such as laser, sonar or radio can be used as media for vehicle or mobile robot navigation. These approaches use triangulation to measure the distance between a number of beacons and the mobile platform and then deter- mine the current location. The problems associated with this technique are the inaccuracy of the distance measurement caused by time-delay of the signals, and the installation and maintenance cost of a large number of beacons required for an area. Satellite based differential global positioning system (DGPS) is probably the most advanced and accurate method for identifying the position and orientation of an object. However, DGPS does not work well if the satellite signals are blocked. This situation commonly happens in the indoor environments, or urban areas with tall buildings, etc. In this work, we propose a visual positioning system (VPS) for vehicle or mobile robot navigation. Different from general landmark-based or model-based approaches which rely on some predefined known landmarks or ap rioriinfor- mation about the environment (e.g., 3-D models of buildings, objects, etc.), no assumptions on the prior knowledge of the scene are made. A stereo-based vision system is built for both extracting feature correspondences and recovering 3- D information of the scene from image sequences. A robust feature tracking method based on simultaneously considering the inter-frame images from the same camera and the stereo image pair from different cameras at a fixed time instant is developed. The relative position of the camera motion is then estimated by registering the 3-D feature points from two consecutive image frames. Our system includes the following basic modules:

The advancements of the research on Mobile Robots with high degree of autonomy is possible, on one hand, due to its broad perspective of applications and, on other hand, due to the development and reduction of costs on computer, electronic and mechanic systems. Together with the research in Artificial Intelligence and Cognitive Science, this scenario currently enables the proposition of ambitious and complex robotic projects. Most of the applications were developed outside the structured environment of industry assembly lines and have complex goals, such as planets exploration, transportation of parts in factories, manufacturing, cleaning and monitoring of households, handling of radioactive materials in nuclear power plants, inspection of volcanoes, and many other activities. This chapter presents and discusses the main topics involved on the design or adoption of a mobile robot system and focus on the control and navigation systems for autonomous mobile robots. Thus, this chapter is organized as follows: • The Section 2 introduces the main aspects of the Robot design, such as: the conceptualization of the mobile robot physical structure and its relation to the world; the state of art of navigation methods and systems; and the control architectures which enables high degree of autonomy. • The Section 3 presents the dynamic and control analysis for navigation robots with kinematic and dynamic model of the differential and omnidirectional robots. • And finally, Section 4 presents applications for a robotic platform of Automation, Simulation, Control and Supervision of Navegation Robots, with studies of dynamic and kinematic modeling, control algorithms, mechanisms for mapping and localization, trajectory planning and the platform simulator.

In recent years, due to the declining birthrate, there are more and more situations where elderly people need to be cared for. As a result, the burden per caregiver has been increasing. As a technology to solve this problem, indoor robot navigation systems that support indoor mobility of those who need care are attracting attention. A navigation system using a mobile robot has components such as position estimation and movement control. As a conventional technique for location estimation, there is a location estimation method using QR codes. In addition, there is a conventional method of mobile control using RSSI. The proposed method presented in this paper is a robot navigation system that replaces QR codes with RFID tags and uses the RSSI-based mobile control method. In addition, we propose a path recovery system when the mobile robot deviates from the path during navigation. In the performance evaluation of navigation between arbitrary points, the maximum error from the ideal trajectory was found to be 13 cm. We also found that the average error between the destination tag and the stop position was 20 cm, while the maximum error was 51 cm. In the performance evaluation of the path recovery system, the maximum error between the ideal path and the stop position was found to be 30 cm.

In this paper, we present a recognition-based autonomous navigation system for mobile robots. The system is based on our previously proposed robust invariant feature (RIF) detector. This detector extracts highly robust and repeatable features based on the key idea of tracking multi-scale interest points and selecting unique representative local structures with the strongest response in both spatial and scale domains. Weighted Zernike moments are used as the feature descriptor and applied to the place recognition. The navigation system is composed of on-line and off-line two stages. In the off-line learning stage, we train the robot in its workspace by just taking several images of representative places as landmarks. Then, in the on-line navigation stage, the robot recognizes scenes, obtains robust feature correspondences, and navigates the environment autonomously using the iterative pose converging (IPC) algorithm which is based on the idea of the visual servoing technique. The experimental results and the performance evaluation show that the proposed navigation system can achieve excellent performance in complex indoor environments.

Aiming at the positioning and navigation system of indoor mobile robot, a fuzzy control method of indoor location navigation based on UWB communication technology is proposed. Firstly, based on the UWB radio signal, the mobile robot localization information is obtained by the twice triangular centroid localization algorithm, and the reactive fuzzy controller is used to realize the local path planning. An improved contour following method is proposed to make the mobile robot get out of the trap area quickly and reach the target. This method can effectively drive the mobile robot to reach the target in a complex and unknown environment with reasonable path. Finally, a fourwheel indoor mobile robot is taken as an example in the indoor environment of 10m × 10m. The experiment proves that the method can effectively drive the mobile robot to reach the target point in the unknown environment with the better path.

Aiming at the problem of navigation in unknown environment of autonomous mobile robot, the self-made mobile robot was taken as research object. The integrated navigation technology was adapted to integrate the SLAM navigation system, DR navigation system and MEMS micro inertial navigation system possessed by the studied robot to construct the SLAM/DR/MEMS integrated navigation system of mobile robot. The experiments were carried to test the method, and the results show that the precision of navigation and location of mobile robot was developed by the integrated navigation scheme.

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.

This article describes the tests and results of the evaluation of an inertial navigation system for a mobile robot, as a mechanism for supplying additional information to the self-localization process. The mobile robot under study has differential steering, a built-in odometric system based on optical encoders for each wheel, and an on-board processor that calculates the position relative to any start point. The inertial system consists of an X-Y dual axis accelerometer, to calculate the position through double integration. The errors of the models are examined and included into the sensors and mobile robot models to validate the estimated position. Many movement conditions like straight and circular paths, including collisions, are identified with both odometric and inertial systems to obtain a more accurate value of the relative position. Results are presented for these conditions, showing that information given by a low cost navigation system based on encoders and accelerometers, with the proper signal processing, can reduce long-term errors and drifts

Recently, stereovision has appeared in robotics as a source of information for real-time mapping and path planning. In this paper, an intelligent motion system for mobile robots is designed and implemented using stereovision. The proposed system uses stereovision as a primary method for sensing the environment, and the system is able to navigate intelligently in an indoor environment with varying degrees of obstacle complexity. It creates noiseless and high-confidence 3D point clouds and uses these point clouds as an input for the mapping and path-planning modules. The proposed system was built by developing, enhancing, and integrating various techniques, modules and algorithms. The Stereovision-based Path-planning module is the integration of three main enhanced techniques: (1) the multi-baseline multi-view stereovision filter (MMSVF), (2) accurate floor detection and segmentation (AFDS), and (3) the intelligent gazing module (IGM). This Stereovision-based Path planning (MMSVF, IGM, and AFDS) was integrated with the Fuzzy Logic Motion Controller (FLMC). All techniques, modules and algorithms are implemented using a multi-threaded and client---server-based architecture. To prove the viability and robustness of our proposed system, we have integrated all components of the system into a fully functional mobile robot navigation system. We compared the performance of the main modules with that of similar modules in the literatures, and showed that our modules had better performance. Testing the whole system is more important than just testing each module individually. To the best of our knowledge, the literatures lack such testing. Hence, in this paper we present the performance of our complete integrated system in different environments using different parameters and different architectures.

Research on mobile robots began in the late sixties with the Stanford Research Institute’s pioneering work. Two versions of SHAKEY, an autonomous mobile robot, were built in 1968 and 1971. The main purpose of this project was “to study processes for the realtime control of a robot system that interacts with a complex environment” 〈NIL 69〉. Indeed, mobile robots were and still are a very convenient and powerful support for research on artificial intelligence oriented robotics. They possess the capacity to provide a variety of problems at different levels of generality and difficulty in a large domain including perception, decision making, communication, etc., which all have to be considered within the scope of the specific constraints of robotics: on-line computing, cost considerations, operating ability, and reliability.

In the past, there has been a tremendous amount of progress in the area of autonomous robot navigation, and a large variety of robots have been developed that demonstrated robust navigation capabilities indoors, in nonurban outdoor environments, or on roads; relatively few approaches have focused on navigation in urban environments such as city centers. Urban areas, however, introduce numerous challenges for autonomous robots as they are rather unstructured and dynamic. In this paper, we present a navigation system for mobile robots designed to operate in crowded city environments and pedestrian zones. We describe the different components of this system, including a simultaneous localization and mapping module for dealing with huge maps of city centers, a planning component for inferring feasible paths, taking into account the traversability and type of terrain, a module for accurate localization in dynamic environments, and the means for calibrating and monitoring the platform. Our navigation system has been implemented and tested in several large‐scale field tests, in which a real robot autonomously navigated over several kilometers in a complex urban environment. This also included a public demonstration, during which the robot autonomously traveled along a more than 3‐km‐long route through the city center of Freiburg, Germany.

This paper discusses the importance, the complexity and the challenges of mapping mobile robot's unknown and dynamic environment, besides the role of sensors and the problems inherited in map building. These issues remain largely an open research problems in developing dynamic navigation systems for mobile robots. The paper presenst the state of the art in map building and localization for mobile robots navigating within unknown environment, and then introduces a solution for the complex problem of autonomous map building and maintenance method with focus on developing an incremental grid based mapping technique that is suitable for real-time obstacle detection and avoidance. In this case, the navigation of mobile robots can be treated as a problem of tracking geometric features that occur naturally in the environment of the robot. The robot maps its environment incrementally using the concept of occupancy grids and the fusion of multiple ultrasonic sensory information while wandering in it and stay away from all obstacles. To ensure real-time operation with limited resources, as well as to promote extensibility, the mapping and obstacle avoidance modules are deployed in parallel and distributed framework. Simulation based experiments has been conducted and illustrated to show the validity of the developed mapping and obstacle avoidance approach.