Off-line localisation of a mobile robot using ultrasonic measurements (original) (raw)

Real-Time Mobile Robot Localisation With Poor Ultrasonic Data

The research in autonomous mobile robot is enlarging upon low cost mobile robotics. This low cost constraint implies the use of a poor perception system and a low computing power. In such a context, algorithms have to be simple in order to be executed in real time and proof against the weaknesses of the sensing system. The solutions proposed for the localisation are based on the fact that the higher the localisation algorithm speed is the lower the error on the position and on the orientation due to the odometry is.

Probabilistic localization methods of a mobile robot using ultrasonic perception system

2009 International Conference on Information and Automation, 2009

Effective localization is a fundamental prerequisite for achieving autonomous mobile robot. In this paper, we propose three probabilistic approaches to solve the global localization problem and the kidnapped robot problem. The first approach named the hybrid Grid-MCL algorithm merges Monte Carlo Localization (MCL) and grid localization. It can solve the global localization problem with very low on-line computational costs. The second approach, sampling in Similar Energy Regions (SER), is used to conquer the kidnapped robot problem. The third approach is a combination of previous two approaches with adaptive samples, which solves the global localization problem and the kidnapped robot problem together. The validity of our approaches is verified through extensive simulations employing ultrasonic perception system.

Analysis of Indoor Robot Localization Using Ultrasonic Sensors

This paper analyzes the Monte Carlo localization (MCL) method, which estimates the pose of an indoor mobile robot. A mobile robot must know where it is to navigate in an indoor environment. The MCL technique is one of the most influential and popular techniques for estimation of robot position and orientation using a particle filter. For the analysis, we perform experiments in an indoor environment with a differential drive robot and ultrasonic range sensor system. The analysis uses MATLAB for implementation of the MCL and investigates the effects of the control parameters on the MCL performance. The control parameters are the uncertainty of the motion model of the mobile robot and the noise level of the measurement model of the range sensor.

MOBILE ROBOT POSITIONING USING ODOMETRY AND ULTRASONIC SENSORS

Mobile robotics is an area that deals with the control of autonomous and semiautonomous vehicles. The use of mobile robots is growing in a large number of applications especially in manufacturing, hazardous materials handling …etc In this paper, the navigation system built on a mobile robot operating in a warehouse is presented. As the mobile must be able to navigate from a known position to a desired new location and orientation avoiding any contact with fixed or moving objects while in Route, the sensory system is very important. Hybrid navigation system that combines the perception and dead reckoning is used and gives satisfactory operation .The encoder and the ultrasonic sensors used are presented in details and the navigation system designed based on their operation is illustrated. 1 INTRODUCTION AND PRELIMINARIES Navigation means the ability to wonder in the environment without colliding with obstacles, the ability to determine one's own position, and the ability to reach ...

Mobile Robot Localization Using Sonar

IEEE Transactions on Pattern Analysis and Machine Intelligence, 1987

This correspondence describes a method by which range data from a sonar rangefinder can be used to determine the two-dimensional position and orientation of a mobile robot inside a room. The plan of the room is modeled as a list of segments indicating the positions of walls. The algorithm works by correlating straight segments in the range data against the room model, then eliminating implausible configurations using the sonar barrier test, which exploits physical constraints on sonar data. The approach is extremely tolerant of noise and clutter. Transient objects such as furniture and people need not be included in the room model, and very noisy, low-resolution sensors can be used. The algorithm's performance is demonstrated using a Polaroid Ultrasonic Rangefinder.

Ultrasonic Sensors used to Detect the Position of the Mobile Service Robot

Acta Mechanica Slovaca, 2021

The article talks about our design of ultrasound-based localization system. The designed system is primarily intended for mobile robotics. The second part of this article is focused on the initial verification of the basic characteristics and accuracy of the position measurement. This location system is currently still in the process of improving the functions required for usage in mobile robotics. We are currently working on removing some types of interferences.

Modeling of Ultrasonic Range Sensors for Localization of Autonomous Mobile Robots

2016

— This paper presents a probabilistic model of ul-trasonic range sensors using backpropagation neural networks trained on experimental data. The sensor model provides the probability of detecting mapped obstacles in the environment, given their position and orientation relative to the transducer. The detection probability can be used to compute the location of an autonomous vehicle from those obstacles that are more likely to be detected. The neural network model is more accurate than other existing approaches, since it captures the typical multilobal detection pattern of ultrasonic transducers. Since the network size is kept small, implementation of the model on a mobile robot can be efficient for real-time navigation. An example that demonstrates how the credence could be incorporated into the extended Kalman filter (EKF) and the numerical values of the final neural network weights are provided in the Appendixes. Index Terms—Kalman filtering, mobile robots, motion plan-ning, neura...

Localization and Differentiation Using Ultrasonic Sensors

2018

The most crucial problem for the mobile robot navigation is obstacles detection and their localization. The determination of obstacle position should be as accurate as possible in order to support robot self-localization procedures. In order to increase its efficiency the recognition of some feature of obstacles shapes should be done. The most advanced systems use laser range-finder and vision to solve this task. They allow to obtain a lot of data of a robot environment and the delivered information is quite precise. Unfortunately these devices have some important drawbacks. Laser scanning range-finders are still expensive. Their another drawback is that they scan only in a single plain. It causes that some obstacle cannot be detected. There are also available 3D laser range-finders. But measurements performed by them are time consuming. Therefore it is rather difficult to use them for on-line mobile robot navigation when a robot moves during a measurement execution. Considering vis...

Hough Localization for mobile robots in polygonal environments

Robotics and Autonomous Systems, 2002

Knowing the position and orientation of a mobile robot situated in an environment is a critical element for effectively accomplishing complex tasks requiring autonomous navigation, and many techniques for robot self-localization have been extensively studied in the past.