Dynamic environment modelling and prediction for autonomous systems

A novel modelling paradigm for online diagnostics and prognostics for autonomous systems is presented. A model for the autonomous system being diagnosed is designed using a logic-based formalism. The model supports the run-time ability to verify that the autonomous system is safe and reliable for operation within a dynamic environment. The paradigm is based on the philosophy that there are different types of semantic relationships between the states of different parts of the system. A finite state automaton is devised for each sensed component and some of the non-sensed components. To capture the interdependencies of components within such a complex robotic platform, automatons were related to each other by semantic relationships. Modality was utilised by the formalism to abstract the relationships and to add measures for the possibility and uncertainty of the relationships. The complexity of the model was analysed to evaluate its scalability and applicability to other systems. The results demonstrate that the complexity is not linear and a computational time of 10 ms was required to achieve run-time diagnostics for 2200 KB of knowledge for complex system interdependences. The ability to detect and mitigate hardware related failures was demonstrated within a confined space autonomous operation. Our findings provide evidence of the applicability of our approach for the significant challenge of run-time safety compliance and reliability in autonomous systems.

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Evolutionary Techniques for Mobile Robot Navigation

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.433-440.6646 ◽

2012 ◽

Vol 433-440 ◽

pp. 6646-6651

Author(s):

Soh Chin Yun ◽

S. Parasuraman ◽

Velappa Ganapathy

Keyword(s):

Genetic Algorithm ◽

Mobile Robot ◽

Robot Navigation ◽

Autonomous Systems ◽

Dynamic Environment ◽

Mobile Robot Navigation ◽

Research Trend ◽

Matlab Simulation ◽

Starting Position ◽

To Come

Current research trend in mobile robot is to build intelligent and autonomous systems that enables mobile robot to plan its motion in static and dynamic environment. In this paper, Genetic Algorithm (GA) is utilized to come out with an algorithm that enables the mobile robot to move from the starting position to the desired goal without colliding with any of the obstacles in the environment. The proposed navigation technique is capable of re-planning new optimum collision free path in the event of mobile robot encountering dynamic obstacles. The method is verified using MATLAB simulation and validated by Team AmigoBotTM robot. The results obtained from MATLAB simulation and real time implementation are discussed at the end of the paper.

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Emulation of space motion sickness (SMS) on the dynamic environment simulator centrifuge.

PsycEXTRA Dataset ◽

10.1037/e476502004-001 ◽

1993 ◽

Author(s):

Eric J. Martin ◽

William B. Albery

Keyword(s):

Motion Sickness ◽

Dynamic Environment ◽

Space Motion Sickness ◽

Space Motion

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Systems Engineering for Ethical Autonomous Systems

10.1049/sbra517e ◽

2019 ◽

Cited By ~ 1

Author(s):

Tony Gillespie

Keyword(s):

Systems Engineering ◽

Autonomous Systems

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THE CONCEPT OF AUTONOMOUS SYSTEMS IN INDUSTRY 4.0

Advanced Logistic Systems - Theory and Practice ◽

10.32971/als.2019.006 ◽

2019 ◽

Vol 12 (1) ◽

pp. 77-87

Author(s):

György Kovács ◽

Rabab Benotsmane ◽

László Dudás

Keyword(s):

Industry 4.0 ◽

Intelligent Systems ◽

Autonomous Systems ◽

Essential Element ◽

Industrial Sector ◽

Life Cycles ◽

Multi Agent Systems ◽

New Paradigm ◽

Intelligent Machines ◽

Digital Network

Recent tendencies – such as the life-cycles of products are shorter while consumers require more complex and more unique final products – poses many challenges to the production. The industrial sector is going through a paradigm shift. The traditional centrally controlled production processes will be replaced by decentralized control, which is built on the self-regulating ability of intelligent machines, products and workpieces that communicate with each other continuously. This new paradigm known as Industry 4.0. This conception is the introduction of digital network-linked intelligent systems, in which machines and products will communicate to one another in order to establish smart factories in which self-regulating production will be established. In this article, at first the essence, main goals and basic elements of Industry 4.0 conception is described. After it the autonomous systems are introduced which are based on multi agent systems. These systems include the collaborating robots via artificial intelligence which is an essential element of Industry 4.0.

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