Robot Motion Models

2013 ◽  
pp. 140-173
Keyword(s):  
Degrees Of Freedom ◽  
Common Ground ◽  
Probabilistic Models ◽  
Robot Motion ◽  
Robot Localization ◽  
Wheeled Robots ◽  
Motion Models ◽  
Aerial Vehicles ◽  
Mobile Robot Localization ◽  
Real Robot

This is the first chapter of the second section, a section devoted to mobile robot localization. Before presenting the general Bayesian framework for that problem at chapter 7, it is first required to study the different probabilistic models of robot motion. This chapter explores some of the reasons why any real robot cannot move as perfectly as planned, thus demanding a probabilistic model of the robot actions—mainly, its movements. Special emphasis is put on the most common ground wheeled robots, although other configurations (including non-robotic ones) with more degrees of freedom, such as arbitrarily-moving hand-held sensors or aerial vehicles, are also mentioned. The best-known approximate probabilistic models for robot motion are provided and justified.

Probabilistic Approach to Mobile Robot Localization Based on Gaussian Models of Sensors

2014 ◽  
Vol 607 ◽  
pp. 803-810
Author(s):  
František Duchoň ◽  
Andrej Babinec ◽  
Jozef Rodina ◽  
Tomas Fico ◽  
Peter Hubinský
Keyword(s):  
Mobile Robot ◽  
Probabilistic Approach ◽  
Gaussian Model ◽  
Main Body ◽  
Robot Localization ◽  
Article Deal ◽  
Mobile Robot Localization ◽  
Real Robot ◽  
Probabilistic Localization ◽  
Markov Localization

In this paper the probabilistic approach to mobile robot localization is discussed. Generally probabilistic localization uses some type of sensors model. In this paper Gaussian model, which is the most appropriate probabilistic model of the sensors, is used. The main body of the article deal with the proposal of own approach to probabilistic localization, which is inspired by Markov localization. That is why the Markov localization is described in the introduction of the article. At the end of the article several experiments with the real robot are described. Results of the experiments have proven that proposed localization is accurate, fast and reliable.

scholarly journals INTELLECTUALIZED CONTROL SYSTEM FOR UNMANNED AERIAL VEHICLE

2020 ◽  
Vol 1 (1) ◽  
pp. 22-27
Author(s):  
S. LYSENKO ◽  
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Unmanned Aerial Vehicles ◽  
Unmanned Aerial Vehicle ◽  
Degrees Of Freedom ◽  
Large Set ◽  
Motion Models ◽  
External Influences ◽  
Aerial Vehicles ◽  
Aerial Vehicle

The paper presents an intellectualized control system for unmanned aerial vehicles. It is based on the use of the fuzzy logic, implementation of which in the Controller of the unmanned aerial device allowed to track and control the trajectory of its movement. The experimental researches prove the efficiency of the device application of fuzzy logic for control of the drone in conditions of external influences implementation. To construct an intellectualized control system of unmanned aerial vehicles, their structure was considered. The base of the system is used for quadunmanned aerial vehicle, which includes four screws, located symmetrically around the central building. The peculiarity of the drone is that its adjacent screws must spin the opposite one from each other. This requirement is explained by the need to prevent system rotation around its own central axis. Depending on the required trajectory, an important aspect was the ability to set different values of the power of the drone engines. Despite the fact that the simplicity of its structure are characterized, they are able to implement a large set of motion models together with a demonstration of high maneuverability. It is reached the presence of six degrees of freedom, which consist of three progressive and three rotating components to set the trajectory of a movement. In order to solve this problem, it was possible to solve the apparatus of fuzzy logic as the basis of the mathematical model of the system. This allowed to ensure a vague logical control of the fog, and, in turn, intellectualize the behavior of drone in the air in the conditions of external influences on the change of a predetermined trajectory of its movement. At the heart of the Intellectualized unmanned Aerial vehicle control system, two fuzzy controllers were involved in the production of control signals for the command of a UAV flight height and an angle of inclination.

3-D Scan Matching for Mobile Robot Localization over Rough Terrain

2019 ◽  
Vol 139 (9) ◽  
pp. 1041-1050
Author(s):  
Hiroyuki Nakagomi ◽  
Yoshihiro Fuse ◽  
Hidehiko Hosaka ◽  
Hironaga Miyamoto ◽  
Takashi Nakamura ◽  
...  
Keyword(s):  
Mobile Robot ◽  
Rough Terrain ◽  
Robot Localization ◽  
Scan Matching ◽  
Mobile Robot Localization

scholarly journals Development of Non Expensive Technologies for Precise Maneuvering of Completely Autonomous Unmanned Aerial Vehicles

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 391
Author(s):  
Luca Bigazzi ◽  
Stefano Gherardini ◽  
Giacomo Innocenti ◽  
Michele Basso
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Degrees Of Freedom ◽  
Vision System ◽  
Video Stream ◽  
Measurement Unit ◽  
Absolute Position ◽  
Light Load ◽  
Aerial Vehicles ◽  
Angular Velocities ◽  
Custom Hardware

In this paper, solutions for precise maneuvering of an autonomous small (e.g., 350-class) Unmanned Aerial Vehicles (UAVs) are designed and implemented from smart modifications of non expensive mass market technologies. The considered class of vehicles suffers from light load, and, therefore, only a limited amount of sensors and computing devices can be installed on-board. Then, to make the prototype capable of moving autonomously along a fixed trajectory, a “cyber-pilot”, able on demand to replace the human operator, has been implemented on an embedded control board. This cyber-pilot overrides the commands thanks to a custom hardware signal mixer. The drone is able to localize itself in the environment without ground assistance by using a camera possibly mounted on a 3 Degrees Of Freedom (DOF) gimbal suspension. A computer vision system elaborates the video stream pointing out land markers with known absolute position and orientation. This information is fused with accelerations from a 6-DOF Inertial Measurement Unit (IMU) to generate a “virtual sensor” which provides refined estimates of the pose, the absolute position, the speed and the angular velocities of the drone. Due to the importance of this sensor, several fusion strategies have been investigated. The resulting data are, finally, fed to a control algorithm featuring a number of uncoupled digital PID controllers which work to bring to zero the displacement from the desired trajectory.

LMI Methods for Extended ℋ∞ Filters for Landmark-based Mobile Robot Localization

2021 ◽  
Author(s):  
Julio Fajardo ◽  
Victor Ferman ◽  
Jabes Guerra ◽  
Antonio Ribas Neto ◽  
Eric Rohmer
Keyword(s):  
Mobile Robot ◽  
Robot Localization ◽  
Mobile Robot Localization

scholarly journals Calibration of ultrasonic sensors of a mobile robot

2009 ◽  
Vol 6 (3) ◽  
pp. 427-437 ◽  
Author(s):  
Ivan Paunovic ◽  
Darko Todorovic ◽  
Miroslav Bozic ◽  
Goran Djordjevic
Keyword(s):  
Signal Processing ◽  
Mobile Robot ◽  
Calibration Procedure ◽  
Ultrasonic Sensor ◽  
Robot Localization ◽  
Ultrasonic Sensors ◽  
Nonlinear Characteristics ◽  
Mobile Robot Localization

The paper discusses a mobile robot localization. Due to cost and simplicity of signal processing, the ultrasonic sensors are very suitable for this application. However, their nonlinear characteristics requires thorough calibrating procedure in order to achieve reliable readings from the obstacles around the robot. Here we describe SMR400 ultrasonic sensor and its calibration procedure. The suggested calibration procedure was tested through a number of experiments, and the results are presented in this paper. .

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