Mobile Robot Navigation System Research Articles

Comparison of Embedded System Design for Industrial Applications

This paper presents a survey on embedded systems design and applications. Several platforms for embedded systems, including microcontrollers, microprocessors, field-programmable gate arrays, digital signal processors, and application-specific integrated circuits are discussed and compared. A survey of embedded system-based industrial applications is presented. Examples of real-life design decisions specific to development of such systems are also presented. The carefully selected three design case study examples include industrial control of wind tunnel with emphasis on actuator control, a mobile robot navigation system with emphasis on integration and synchronization of several subsystems, and optimized implementation of computationally intensive control system on a small microcontroller system.

System interdependence analysis for autonomous robots

With the increasing complexity of robotic systems, system robustness and efficiency are harder to achieve, since they are determined by the interplay of all of a system’s components. In order to improve the robustness of such systems, it is essential to identify the system components that are crucial for each task and the extent to which they are affected by other components and the environment. Such knowledge will help developers to improve their systems, and can also be directly utilized by the systems themselves, for example, to detect failures and thereby correctly adjust the system’s behavior. In this article a method of system interdependence analysis is presented. The basic idea is to learn and quantitatively evaluate the coherence between performance indicators of different system components, as well as the influence of environmental parameters on the system. To validate the proposed approach, system interdependence analysis is applied to the navigation system of an autonomous mobile robot. Its navigational methods are presented and suitable indicators are derived. The results of using the method, based on experimental data from an extended field experiment, are given.

Development of a navigation system for heterogeneous mobile robots

Navigation systems often play an important role in mobile robot control. Many existing mobile robot navigation systems have been implemented and tested for particular types of robots. However, in some implementations, such as heterogeneous multi-robot systems, a generic navigation system can offer potential advantages. In such applications, a generic navigation system should be able account for robots with varying size, shape, drive type and sensor quantities. Additionally, it should be capable of offering a high degree of flexibility for navigation in known and unknown environments. Hence, a single generic navigation system that combines the benefits of reactive and deliberative control has been developed for heterogeneous mobile robots. The design of the hierarchical hybrid navigation system is based on the A* algorithm, a polar histogram and a modified dynamic window approach. Simulation experiments with three heterogeneous robots in a range of environments have been conducted. Performance of reactive navigation and hybrid reactive-deliberative navigation in known and unknown environments is evaluated. Favourable results are achieved with the developed reactive system. Hybrid reactive-deliberative navigation offers improved performance over reactive navigation in known environments. Deliberative control does not affect performance significantly in unknown environments. Initial hardware experiments demonstrate that the navigation system can work on real robots.

Localization and tracking algorithm for mobile robot based on wireless sensor network

PurposeThe purpose of this paper is to describe a low‐cost mobile robot localization and navigation system based on wireless sensor network (WSN). This can be used in extreme environments such as mines, hostile areas, and other places where static nodes cannot be deployed directly.Design/methodology/approachThe distance between robot and beacon nodes is estimated by received signal strength indicator (RSSI). The adaptive Kalman filter algorithm is used to eliminate noise from the RSSI signal. Then, the paper gets the mobile robot's status information which includes position, velocity, and acceleration. In order to improve the real‐time performance of systems and reduce the computation overhead of robot's CPU, the paper chooses a beacon node by some certain rules to implement the localization program. The robot and beacon nodes use the CC2430 as the communication and data‐processing unit.FindingsThe experiment shows it has the characteristics of good position precision, is easily realized, and inexpensive.Research limitations/implicationsThat the system needs so many beacon nodes, is the main limitation.Practical implicationsA very useful and low‐cost method for mobile robot localization and navigation.Originality/valueThe innovative aspect of the paper is that it attempts an analysis of the localization system based on WSN, by attempting to analyze and explain the model of localization system which includes robot and beacon nodes.

Autonomous Navigation System for Mobile Robot Using Randomly Distributed Passive RFID Tags

This paper presents an autonomous navigation system for a mobile robot using randomly distributed passive RFID tags. In the case of randomly distributed RFID tags, it is difficult to provide the precise location of the robot especially in the area of sparse RFID tag distribution. This, combined with the wide turning radius of the robot, can cause the robot to enter a zigzag exploration path and miss the goal. In RFID-based navigation, the key is to reduce both the number of RFID tags and the localization error for practical use in a large space. To cope with these, we utilized the Read time, which measures the reading time of each RFID tag. With this, we could estimate accurately the localization and orientation without using any external sensors or increasing the RFID tags. The average estimation errors of 7.8cm in localization and 11 degrees in orientation were achieved with 102 RFID tags in the area of 4.2m by 6.2m. Our proposed method is verified with the path trajectories produced during navigation compared with conventional approaches.

Design and implementation of a navigation system for autonomous mobile robots

In this paper, a navigation system for autonomous mobile robots is proposed. Our navigation system is a hybrid of behaviour-based and model-based navigation systems. In our system, a behaviour-based subsystem is in charge of low-level reactive actions, and a model-based subsystem is responsible for high-level planned actions. If there are obstacles in the way, the navigation system will use our obstacle avoidance algorithm to navigate around these obstacles and keep the robot moving towards the destination. On the basis of our experimental results, our navigation system can navigate the robot to the destination effectively.

Motion planning in order to optimize the length and clearance applying a Hopfield neural network

This paper deals with motion planning in plane for a mobile robot with two freedom degrees through some polygonal unmoved obstacles. Applying Minkowski sum, we can represent the robot as a point. Then, by using traditional approaches such as visibility graphs, simple and generalized Voronoi diagrams, decomposition methods, etc, it is possible to provide a graph covering obstacles, say roadmap. In order to find a real-time collision-free robot motion planning between two arbitrary source and target configurations through the roadmap, an adoptive Hopfield neural network is considered. Maximizing the clearance of path together with minimizing the length of path are pursued in a bi-objective framework. For treating with multiple objectives TOPSIS method, as a kind of goal programming techniques, is provided to find the efficient solutions. Because of capability of parallel computation through hardware implementation of neural networks, the presented approach is a reasonable technique in mobile robot navigation and traveler guidance systems. The advantages of the proposed system are confirmed by simulation experiments. This approach can be directly extended in unknown environment including time-varying conditions.

Object learning and detection using evolutionary deformable models for mobile robot navigation

SUMMARYDeformable models have been studied in image analysis over the last decade and used for recognition of flexible or rigid templates under diverse viewing conditions. This article addresses the question of how to define a deformable model for a real-time color vision system for mobile robot navigation. Instead of receiving the detailed model definition from the user, the algorithm extracts and learns the information from each object automatically. How well a model represents the template that exists in the image is measured by an energy function. Its minimum corresponds to the model that best fits with the image and it is found by a genetic algorithm that handles the model deformation. At a later stage, if there is symbolic information inside the object, it is extracted and interpreted using a neural network. The resulting perception module has been integrated successfully in a complex navigation system. Various experimental results in real environments are presented in this article, showing the effectiveness and capacity of the system.

歩行経路観測に基づく重要点抽出による移動ロボットナビゲーション

In this paper, we investigate a mobile robot navigation system which can localize the mobile robot correctly and navigate based on observation of human walking in order to operate in a space populated by humans with minimal disturbance to them. To realize this system, we utilize multiple intelligent devices embedded in the environment. The human walking paths are obtained from a distributed vision system. We extract important points from observation and build a topological map of nodes which are important points and of edges which are added based on the presence of paths between important points. In addition, the human walking paths between two important points are averaged to get frequently used paths. The averaged paths are utilized as paths of the mobile robots. The mobile robot navigation based on observation of human is also performed with the support of the system. The position and orientation of the mobile robot are estimated from wheel encoder and distributed vision system measurement data using an extended Kalman filter. The system navigates the mobile robot along the frequently used paths by tracking control.

2P2-D10 レーザ光を用いた移動ロボットのための経路指示システム(車輪移動ロボット)

We research the mobile robot navigation system by using one laser source. We confirmed that we could navigate the mobile robot by using laser navigation system and described the basic features of this system. However, when the navigation speed was fast, it was difficult for the robot to follow the laser spot. We found that we needed to change the scanning speed, synchronizing it with the acceleration and deceleration of the mobile robot to realize the smooth navigation. In this paper, we examine the device configuration and the simulation result to scan the laser smoothly.

Robot Navigation in Cluttered 3-D Environments Using Preference-Based Fuzzy Behaviors

Autonomous navigation systems for mobile robots have been successfully deployed for a wide range of planar ground-based tasks. However, very few counterparts of previous planar navigation systems were developed for 3-D motion, which is needed for both unmanned aerial and underwater vehicles. A novel fuzzy behavioral scheme for navigating an unmanned helicopter in cluttered 3-D spaces is developed. The 3-D navigation problem is decomposed into several identical 2-D navigation subproblems, each of which is solved by using preference-based fuzzy behaviors. Due to the shortcomings of vector summation during the fusion of the 2-D subproblems, instead of directly outputting steering subdirections by their own defuzzification processes, the intermediate preferences of the subproblems are fused to create a 3-D solution region, representing degrees of preference for the robot movement. A new defuzzification algorithm that steers the robot by finding the centroid of a 3-D convex region of maximum volume in the 3-D solution region is developed. A fuzzy speed-control system is also developed to ensure efficient and safe navigation. Substantial simulations have been carried out to demonstrate that the proposed algorithm can smoothly and effectively guide an unmanned helicopter through unknown and cluttered urban and forest environments.

Real Time Mapping and Dynamic Navigation for Mobile Robots

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.

An optical flow-based sensing system for reactive mobile robot navigation

This work discusses the use of optical flow to generate the sensorial information a mobile robot needs to react to the presence of obstacles when navigating in a non-structured environment. A sensing system based on optical flow and time-to-collision calculation is here proposed and experimented, which accomplishes two important paradigms. The first one is that all computations are performed onboard the robot, in spite of the limited computational capability available. The second one is that the algorithms for optical flow and time-to-collision calculations are fast enough to give the mobile robot the capability of reacting to any environmental change in real-time. Results of real experiments in which the sensing system here proposed is used as the only source of sensorial data to guide a mobile robot to avoid obstacles while wandering around are presented, and the analysis of such results allows validating the proposed sensing system.

Novel indoor mobile robot navigation using monocular vision

Novel autonomous navigation system for an indoor mobile robot based on monocular vision is presented. The navigation system is composed of on-line and off-line stages. During the off-line learning stage, the robot records an image frame sequence. From this sequence a hybrid environment map is built with Rao–Blackwellized particle filters (RBPF), where the number of resampling steps is determined adaptively for significantly reducing the particle depletion problem, and the evolution strategies (ES) are introduced for avoiding particle impoverishment. The map is partitioned into topological locations characterized by a set of geometrical scale invariant key-points. These key-points, represented with multi-dimension descriptors, can be robustly matched despite changes in contrast, scale and viewpoint, demonstrated with nearest neighbor search based on KD-tree. In the on-line navigation stage, the robot recognizes the most likely location through robust location recognition algorithm, estimates the relative pose between the locations, and then navigates the environment autonomously. Experiment results carried out with a real robot in an indoor environment show the superior performance of the proposed method.

Omnidirectional Vision Based Topological Navigation

In this work we present a novel system for autonomous mobile robot navigation. With only an omnidirectional camera as sensor, this system is able to build automatically and robustly accurate topologically organised environment maps of a complex, natural environment. It can localise itself using such a map at each moment, including both at startup (kidnapped robot) or using knowledge of former localisations. The topological nature of the map is similar to the intuitive maps humans use, is memory-efficient and enables fast and simple path planning towards a specified goal. We developed a real-time visual servoing technique to steer the system along the computed path. A key technology making this all possible is the novel fast wide baseline feature matching, which yields an efficient description of the scene, with a focus on man-made environments.

SPEED-RANGE DILEMMAS FOR VISION-BASED NAVIGATION IN UNSTRUCTURED TERRAIN

The performance of vision-based navigation systems for off-road mobile robots depends crucially on the resolution of the camera, the sophistication of the visual processing, the latency between image and sensor capture to actuator control, and the period of the control loop. One particularly important design question is whether one should increase the resolution of the camera images, and the range of the obstacle detection algorithms, at the expense of latency and control loop period. We first report experimental results on the resolution-period trade-off with a stereo vision-based navigation system implemented on the LAGR mobile robot platform. We propose a multi-agent perception and control architecture that combines a sophisticated long-range path detection method operating at high resolution and low frame rate, with a simple stereo-based obstacle detection method operating at low resolution, high frame rate, and low latency. The system combines the advantages of the long-range module for strategic path planning, with the advantages of the short-range module for tactical driving.

2A1-D03 レーザレンジファインダを用いた非数量的地図に基づく自律移動ロボットのナビゲーション(車輪移動ロボット・メカトロニクス)

In this research, we propose a method of mobile robot navigation system with a simple environmental data expression, graph-map. In general, autonomous mobile robot has to estimate its position. Usually it is necessary for a robot to correct cumulative error. For this purpose, the technique using the RFID tag in the environment as a local landmark and the technique matching distance data to environmental data have been proposed. However, they require complex preparation before the robot being navigated. In this paper, we verified the effectiveness of the system by the experiment, using a robot.

A 3-level autonomous mobile robot navigation system designed by using reasoning/search approaches

This paper describes how soft computing methodologies such as fuzzy logic, genetic algorithms and the Dempster–Shafer theory of evidence can be applied in a mobile robot navigation system. The navigation system that is considered has three navigation subsystems. The lower-level subsystem deals with the control of linear and angular volocities using a multivariable PI controller described with a full matrix. The position control of the mobile robot is at a medium level and is nonlinear. The nonlinear control design is implemented by a backstepping algorithm whose parameters are adjusted by a genetic algorithm. We propose a new extension of the controller mentioned, in order to rapidly decrease the control torques needed to achieve the desired position and orientation of the mobile robot. The high-level subsystem uses fuzzy logic and the Dempster–Shafer evidence theory to design a fusion of sensor data, map building, and path planning tasks. The fuzzy/evidence navigation based on the building of a local map, represented as an occupancy grid, with the time update is proven to be suitable for real-time applications. The path planning algorithm is based on a modified potential field method. In this algorithm, the fuzzy rules for selecting the relevant obstacles for robot motion are introduced. Also, suitable steps are taken to pull the robot out of the local minima. Particular attention is paid to detection of the robot’s trapped state and its avoidance. One of the main issues in this paper is to reduce the complexity of planning algorithms and minimize the cost of the search. The performance of the proposed system is investigated using a dynamic model of a mobile robot. Simulation results show a good quality of position tracking capabilities and obstacle avoidance behavior of the mobile robot.

A Visual Positioning System for Vehicle or Mobile Robot Navigation

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 mobile platform is finally given by the reference to its initial position.

A guide to vision-based map building

This paper describes a simple vision-based mapping system for mobile robot navigation in an unknown outdoor environment, consistent with the efforts of Georgia Tech's team for the DARPA-sponsored Learning Applied to Ground Robots (LAGR) project. This approach, which has been robust to different environments and responsive to time-critical constraints, is a good example of robotics in practice