Navigation System For Autonomous Mobile Robot Research Articles

Design a Nonlinear MPC Controller for Autonomous Mobile Robot Navigation System Based on ROS

Design a Nonlinear MPC Controller for Autonomous Mobile Robot Navigation System Based on ROS

Development of an Autonomous Robotics Platform for Road Marks Painting Using Laser Simulator and Sensor Fusion Technique

SUMMARYThe design and experimental works of an autonomous robotic platform for road marks painting are presented in this paper as the first autonomous system of its kind. The whole system involves two main sub-systems, namely: an autonomous mobile robot navigation system which is used for recognizing the roads and estimating the position of road marks, and automatic road marks painting system that is attached to the mobile robot platform to control the spray of the paint on the road’s surface. The experimental results show the capability of the proposed system to perform the task of autonomous road marks painting with accuracy of ±10 cm.

IMPLEMENTASI TEKNOLOGI COMPUTER VISION SEBAGAI PENGENDALI MOBILE ROBOT BERBASIS KAMERA WEB

In this research will be developed autonomous Mobile Robot navigation system, using vision sensor in the form of web camera. The ability of the robot to find the path, avoiding obstacles in an indoor environment becomes the key to the success of navigation. One of the things that underlies the robot navigation system is the process of image information processing from a web camera. So it takes a method that can process image information from the web camera into image data more easily read by the computer. The method that can be used to solve this problem is Canny Edge Detection. Canny Edge Detection has some of the most optimum edge detection criteria that localize the image well, detect objects well and clear response. With these advantages, the Canny Edge Detection method can produce a more representative image approaching the real object. After the edge detection process is completed then the next step is to identify and identify paths or obstacles that exist. Paths and obstructions that have been identified will be used as models to determine which direction the robot will run. The whole process of computing and control will be done using Raspberry pi, while for image processing using OpenCV application.

Optical 3D laser measurement system for navigation of autonomous mobile robot

In our current research, we are developing a practical autonomous mobile robot navigation system which is capable of performing obstacle avoiding task on an unknown environment. Therefore, in this paper, we propose a robot navigation system which works using a high accuracy localization scheme by dynamic triangulation. Our two main ideas are (1) integration of two principal systems, 3D laser scanning technical vision system (TVS) and mobile robot (MR) navigation system. (2) Novel MR navigation scheme, which allows benefiting from all advantages of precise triangulation localization of the obstacles, mostly over known camera oriented vision systems. For practical use, mobile robots are required to continue their tasks with safety and high accuracy on temporary occlusion condition. Presented in this work, prototype II of TVS is significantly improved over prototype I of our previous publications in the aspects of laser rays alignment, parasitic torque decrease and friction reduction of moving parts. The kinematic model of the MR used in this work is designed considering the optimal data acquisition from the TVS with the main goal of obtaining in real time, the necessary values for the kinematic model of the MR immediately during the calculation of obstacles based on the TVS data.

Autonomous mobile robot navigation system designed in dynamic environment based on transferable belief model

This paper investigates the possibility of using transferable belief model (TBM) as a promising alternative for the problem of path planning of nonholonomic mobile robot equipped with ultrasonic sensors in an unknown dynamic environment, where the workspace is cluttered with static obstacles and moving obstacles. The concept of the transferable belief model is introduced and used to design a fusion of ultrasonic sensor data. A new strategy for path planning of mobile robot is proposed based on transferable belief model. The robot’s path is controlled using proposed navigation strategy that depends on navigation parameters which is modified by TBM pignistic belief value. These parameters are tuned in real time to adjust the path of the robot. A major advantage of the proposed method is that, with detection of the robot’s trapped state by ultrasonic sensor, the navigation law can determine which obstacle is dynamic or static without any previous knowledge, and then select the relevant obstacles for corresponding robot avoidance motion. Simulation is used to illustrate collision detection and path planning.

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.

Symbolic Trajectory Description in Mobile Robotics

Autonomous mobile robot navigation systems are based on three principal kinds of techniques: map-based navigation, map-building-based navigation and mapless navigation. We propose a method for symbolic trajectory description in unknown indoor environments. The chosen form uses a panoramic description called fresco. The method uses distance measurements from a 2D laser range finder, digitises the robot's visibility area, eliminates superfluous data and reorients their presentation. The landmarks are then extracted and organised into the fresco which is validated by means of neighbourhood rules. As the robot moves in the environment, the frescoes are created and both the amount of new information a fresco carries out and its position in relation to the preceding ones are evaluated by means of two criteria. Only frescoes selected as enough informative are stored to describe the robot's route.

Symbolic environment representation by means of frescoes in mobile robotics

Autonomous mobile robot navigation systems are based on three principal kinds of techniques: map-based navigation, map-building-based navigation and mapless navigation. We propose a mapless method for trajectory description in unknown indoor environments. The method uses distance measurements from a 2D laser range finder, digitises the robot's visibility area, eliminates superfluous data and reorients their presentation with laws similar to those used in cellular automata. The landmarks are extracted and organised in a panoramic description called fresco. The frescoes which are validated by means of neighbourhood rules. The most informative frescoes are detected by means of two criteria and stored. The stored frescoes are considered as a human-like descritption of the robot's route and could be used by the robot to retrieve its route to its starting point.

Mobile robot navigation and scene modeling using stereo fish-eye lens system

We present an autonomous mobile robot navigation system using stereo fish-eye lenses for navigation in an indoor structured environment and for generating a model of the imaged scene. The system estimates the three-dimensional (3D) position of significant features in the scene, and by estimating its relative position to the features, navigates through narrow passages and makes turns at corridor ends. Fish-eye lenses are used to provide a large field of view, which images objects close to the robot and helps in making smooth transitions in the direction of motion. Calibration is performed for the lens-camera setup and the distortion is corrected to obtain accurate quantitative measurements. A vision-based algorithm that uses the vanishing points of extracted segments from a scene in a few 3D orientations provides an accurate estimate of the robot orientation. This is used, in addition to 3D recovery via stereo correspondence, to maintain the robot motion in a purely translational path, as well as to remove the effects of any drifts from this path from each acquired image. Horizontal segments are used as a qualitative estimate of change in the motion direction and correspondence of vertical segment provides precise 3D information about objects close to the robot. Assuming detected linear edges in the scene as boundaries of planar surfaces, the 3D model of the scene is generated. The robot system is implemented and tested in a structured environment at our research center. Results from the robot navigation in real environments are presented and discussed.

Sonar-based real-world mapping and navigation

A sonar-based mapping and navigation system developed for an autonomous mobile robot operating in unknown and unstructured environments is described. The system uses sonar range data to build a multileveled description of the robot's surroundings. Sonar readings are interpreted using probability profiles to determine empty and occupied areas. Range measurements from multiple points of view are integrated into a sensor-level sonar map, using a robust method that combines the sensor information in such a way as to cope with uncertainties and errors in the data. The resulting two-dimensional maps are used for path planning and navigation. From these sonar maps, multiple representations are developed for various kinds of problem-solving activities. Several dimensions of representation are defined: the abstraction axis, the geographical axis, and the resolution axis. The sonar mapping procedures have been implemented as part of an autonomous mobile robot navigation system called Dolphin. The major modules of this system are described and related to the various mapping representations used. Results from actual runs are presented, and further research is mentioned. The system is also situated within the wider context of developing an advanced software architecture for autonomous mobile robots.