A new localization system for autonomous robots

2004 ◽  
Author(s):  
S. Hernandez ◽  
C.A. Morales ◽  
J.M. Torres ◽  
L. Acosta
Keyword(s):  
Autonomous Robots ◽  
Localization System

An adaptive localization system for outdoor/indoor navigation for autonomous robots

2006 ◽  
Author(s):  
E. B. Pacis ◽  
B. Sights ◽  
G. Ahuja ◽  
G. Kogut ◽  
H. R. Everett
Keyword(s):  
Autonomous Robots ◽  
Indoor Navigation ◽  
Localization System

Empirical analysis of cyber-attacks to an indoor real time localization system for autonomous robots

2017 ◽  
Vol 70 ◽  
pp. 422-435 ◽  
Author(s):  
Ángel Manuel Guerrero-Higueras ◽  
Noemí DeCastro-García ◽  
Francisco Javier Rodríguez-Lera ◽  
Vicente Matellán
Keyword(s):  
Real Time ◽  
Empirical Analysis ◽  
Autonomous Robots ◽  
Cyber Attacks ◽  
Localization System

Design of a Bell-Shaped Ultra Wideband Antenna for Indoor Localization System

2015 ◽  
Vol 5 (6) ◽  
pp. 323 ◽  
Author(s):  
Nadia Ghariani ◽  
Mohamed Salah Karoui ◽  
Mondher Chaoui ◽  
Mongi Lahiani ◽  
Hamadi Ghariani
Keyword(s):  
Indoor Localization ◽  
Ultra Wideband ◽  
Localization System ◽  
Wideband Antenna

VLSI Implementation of an Adaptable Localization System for All Unicycle Robot Platforms

2017 ◽  
Vol 10 (1) ◽  
pp. 8
Author(s):  
Badr Elkari ◽  
Hassan Ayad ◽  
Abdeljalil El Kari ◽  
Mostafa Mjahed
Keyword(s):  
Vlsi Implementation ◽  
Localization System

LEARNING NAIVE PHYSICS BY VISUAL OBSERVATION: USING QUALITATIVE SPATIAL REPRESENTATIONS AND PROBABILISTIC REASONING

2001 ◽  
Vol 01 (03) ◽  
pp. 273-285 ◽  
Author(s):  
PAUL A. BOXER
Keyword(s):  
Bayesian Network ◽  
Probabilistic Reasoning ◽  
Autonomous Robots ◽  
Visual Observation ◽  
Spatial Representations ◽  
Physical Behavior ◽  
Naive Physics ◽  
Action At A Distance ◽  
General Physical ◽  
Test Scenarios

Autonomous robots are unsuccessful at operating in complex, unconstrained environments. They lack the ability to learn about the physical behavior of different objects through the use of vision. We combine Bayesian networks and qualitative spatial representation to learn general physical behavior by visual observation. We input training scenarios that allow the system to observe and learn normal physical behavior. The position and velocity of the visible objects are represented as qualitative states. Transitions between these states over time are entered as evidence into a Bayesian network. The network provides probabilities of future transitions to produce predictions of future physical behavior. We use test scenarios to determine how well the approach discriminates between normal and abnormal physical behavior and actively predicts future behavior. We examine the ability of the system to learn three naive physical concepts, "no action at a distance", "solidity" and "movement on continuous paths". We conclude that the combination of qualitative spatial representations and Bayesian network techniques is capable of learning these three rules of naive physics.

scholarly journals Estimation bounds of beat signal in the R-Mode localization system

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Lars Grundhofer ◽  
Stefan Gewies ◽  
Giovanni Del Galdo
Keyword(s):  
Beat Signal ◽  
Mode Localization ◽  
Localization System
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