The Methods of Radar Detection of Landmarks by Mobile Autonomous Robots

2019 ◽  
pp. 171-196
Author(s):  
Oleksandr Poliarus ◽  
Yevhen Poliakov
Keyword(s):  
Autonomous Robots ◽  
Radar Detection

The relation between radar detection and the observer"s concept of a target. (Subtask Vigil II.).

1962 ◽  
Author(s):  
Robert D. Baldwin ◽  
A. Dean Wright ◽  
Donald J. Lehr
Keyword(s):  
Radar Detection

Effects of Training With Added Relevant Responses on RADAR Detection

2008 ◽  
Author(s):  
Michael D. Young ◽  
Alice F. Healy ◽  
Cleotilde Gonzalez ◽  
Varun Dutt ◽  
Lyle E. Bourne
Keyword(s):  
Radar Detection

Radar Detection of Swimmers

2009 ◽  
Vol 68 (2) ◽  
pp. 127-135
Author(s):  
V. A. Zuikov ◽  
V. I. Lutsenko ◽  
Yu. A. Pedenko ◽  
V. B. Razskazovsky ◽  
V. G. Sugak ◽  
...  
Keyword(s):  
Radar Detection

Wideband Radar Detection for Maneuvering Multi-Target

2010 ◽  
Vol 32 (3) ◽  
pp. 564-569 ◽  
Author(s):  
Jun-hai Su ◽  
Long Zhang ◽  
Meng-dao Xing
Keyword(s):  
Radar Detection ◽  
Wideband Radar

Security Considerations for Autonomous Robots

1988 ◽  
Author(s):  
Douglas W. Gage
Keyword(s):  
Autonomous Robots

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 Symbiosis with artificial intelligence via the prism of law, robots, and society

2021 ◽  
Author(s):  
Stamatis Karnouskos
Keyword(s):  
Artificial Intelligence ◽  
Social Economic ◽  
Autonomous Robots ◽  
Law And Society ◽  
Citizenship Rights ◽  
Legal Entity ◽  
Profound Impact ◽  
The People ◽  
Physical Presence ◽  
And Robotics

AbstractThe rapid advances in Artificial Intelligence and Robotics will have a profound impact on society as they will interfere with the people and their interactions. Intelligent autonomous robots, independent if they are humanoid/anthropomorphic or not, will have a physical presence, make autonomous decisions, and interact with all stakeholders in the society, in yet unforeseen manners. The symbiosis with such sophisticated robots may lead to a fundamental civilizational shift, with far-reaching effects as philosophical, legal, and societal questions on consciousness, citizenship, rights, and legal entity of robots are raised. The aim of this work is to understand the broad scope of potential issues pertaining to law and society through the investigation of the interplay of law, robots, and society via different angles such as law, social, economic, gender, and ethical perspectives. The results make it evident that in an era of symbiosis with intelligent autonomous robots, the law systems, as well as society, are not prepared for their prevalence. Therefore, it is now the time to start a multi-disciplinary stakeholder discussion and derive the necessary policies, frameworks, and roadmaps for the most eminent issues.

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