Mobile robot localization in an unknown environment using sonar sensors and an incidence angle based sensors switching policy — Experimental results (original) (raw)

Global Integration of Ultrasonic Sensors Information in Mobile Robot Localization

1999

This paper describes an ultrasonic sensors localization system for autonomous mobile robot navigation in a semi-structured indoor environment. A peripheral ring of 24 ultrasonic sensors is used to obtain the information required for the localization process. The proposed a lgorithm is based upon an extended Kalman filter, which u tilizes matches between ob served g eometric beacons projections and an a priori map o f beacon locations, to correct t he position and orientation o f t he vehicle. The resulting self-localization module has been integrated successfully in a more complicated navigation system. Various experimental results show the effectiveness of the presented algorithm.

A new Extended Kalman Filter based on actual local information for mobile robots

9th European Workshop on Advanced Control and Diagnosis (ACD 2011), 2011

In this work we propose a new version of Extended Kalman Filter for robot localization. Such algorithm is based on local data and does not require any assumption on robot's working environment. The filter is driven by measurements, taken by ultrasonic sensors located onboard the robot, and a switching sensors activation policy is devised, which allows power saving still achieving accurate tracking. We obtain reliable estimation results, as shown in simulations. The proposed algorithm works well both in "simple" and in "complex" environments.

Adaptive extended Kalman filter (AEKF)-based mobile robot localization using sonar

Robotica, 2000

In this paper, an AEKF algorithm is used to localize a mobile robot equipped with eight Polaroid sonars in an indoor structured environment. The system state equation and sonar measurement models used for locating the mobile robot are set up. The localization process based on the AEKF algorithm is given. Four criteria used to judge the validity of predictive measurements of sonars are presented, which can increase the probability of the matching between predictive measurements and actual measurements. Experiments show that the localization precision based on our methods is greater than that using the conventional EKF algorithm.

Filters for mobile robots: EKF, UKF and sensor switching - experimental results

2011 9th IEEE International Conference on Control and Automation (ICCA), 2011

ABSTRACT In this work we compare the performance of two well known filters for nonlinear models, the Extended Kalman Filter and the Unscented Kalman Filter, in estimating the position and orientation of a mobile robot. The two filters are driven by measurements taken by ultrasonic sensors that are located onboard the robot, and a switching sensors activation policy is devised, which allows power saving and accurate tracking. The experimental results show a substantial equivalence of the two filters, although in principle the approximating properties of the UKF are much better.

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 ...

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.

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...

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.