RoboPET: a semi-autonomous robot for hazardous inspections (original) (raw)
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On the use of Bayesian Networks to develop behaviours for mobile robots
Robotics and Autonomous Systems, 2007
Bayesian Networks are models which capture uncertainties in terms of probabilities that can be used to perform reasoning under uncertainty. This paper presents an attempt to use Bayesian Networks as a learning technique to manage task execution in mobile robotics. To learn the Bayesian Network structure from data, the K2 structural learning algorithm is used, combined with three different net evaluation metrics. The experiment led to a new hybrid multiclassifying system resulting from the combination of 1-NN with the Bayesian Network, that allows one to use the power of the Bayesian Network while avoiding the computational burden of the reasoning mechanism -the so-called evidence propagation process. As an application example we present an approach of the presented paradigm to implement a door-crossing behaviour in a mobile robot using only sonar readings, in an environment with smooth walls and doors. Both the performance of the learning mechanism and the experiments run in the real robot-environment system show that Bayesian Networks are valuable learning mechanisms, able to deal with the uncertainty and variability inherent to such systems.
An Autonomous Mobile Robot for Refinery Inspection
International Journal of Innovative Technology and Research, 2016
Industrial safety is one of the main aspects of industry specially refining industry. To avoid any types of unwanted phenomena all refining industry follows some basic precaution and phenomena. Communication is the main key factor for any industry today to monitor different parameters and take necessary actions accordingly to avoid any types of hazards.To implement a robotic system to autonomously navigate in an oil and gas refinery and it must be able to communicate with the control room and also localize it and alert workers in hazardous leakages and other accidents. Oil and gas refineries can be a dangerous environment for numerous reasons, including heat, gasses and humidity at the refinary. In order to augment how human operators interact with this environment, a mobile robotic platform is developed. This paper focuses on the use of WiFi for communicating with and localizing the robot. All the algorithms implemented are tested in real world scenarios with the robot developed and results are promising.
Lecture on Bayesian Perception & Decision-making for Autonomous Vehicles and Mobile Robots
2017
New technologies for Autonomous Vehicles and Mobile Robots will be presented, with an emphasis on multi-sensors Embedded Perception, Situation Awareness, Collision Risk Assessment, and Decision-making for safe navigation in Dynamic Human Environments. It will be shown that Bayesian approaches are mandatory for developing these technologies and for obtaining the required robustness in presence of uncertainty and complex dynamic situations. The talk will be illustrated by some interesting results obtained in the scope of several collaborative projects involving either national research and development institutes such as CEA-LETI and IRT (Technological Research Institute) Nanoelec, or international industrial companies such as Toyota or Renault-Nissan.
KI - Künstliche Intelligenz, 2016
With increasing capabilities and reliability of autonomous mobile robots, inspection of remote industrial plants in challenging environments becomes feasible. With the ARGOS challenge, oil and gas company TOTAL S.A. initiated an international competition aimed at the development of the first autonomous mobile robot which can safely operate in complete or supervised autonomy over the entire onshore or offshore production site, potentially in hazardous explosive atmospheres and harsh conditions. In this work, the approach of joint Austrian-German Team ARGONAUTS towards solving this challenge is introduced, focussing on autonomous capabilities. These build on functional components developed during prior participation in the RoboCup Rescue Robot League.
Obstacle Avoidance and Proscriptive Bayesian Programming
2003
Unexpected events and not modeled properties of the robot environment are some of the challenges presented by situated robotics research field. Collision avoidance is a basic security requirement and this paper proposes a probabilistic approach called Bayesian Programming, which aims to deal with the uncertainty, imprecision and incompleteness of the information handled to solve the obstacle avoidance problem. Some examples illustrate the process of embodying the programmer preliminary knowledge into a Bayesian program and experimental results of these examples implementation in an electrical vehicle are described and commented. A video illustration of the developed experiments can be found at Abstract Unexpected events and not modeled properties of the robot environment are some of the challenges presented by situated robotics research field. Collision avoidance is a basic security requirement and this paper proposes a probabilistic approach called Bayesian Programming, which aims to deal with the uncertainty, imprecision and incompleteness of the information handled to solve the obstacle avoidance problem. Some examples illustrate the process of embodying the programmer preliminary knowledge into a Bayesian program and experimental results of these examples implementation in an electrical vehicle are described and commented. A video illustration of the developed experiments can be found at
VIKINGS: An Autonomous Inspection Robot for the ARGOS Challenge
HAL (Le Centre pour la Communication Scientifique Directe), 2019
This paper presents the overall architecture of the VIKINGS robot, one of the five contenders in the ARGOS challenge and winner of two competitions. The VIKINGS robot is an autonomous or remote-operated robot for the inspection of oil and gas sites and is able to assess various petrochemical risks based on embedded sensors and processing. As described in this article, our robot is able to autonomously monitor all the elements of a petrochemical process on a multi-storey oil platform (reading gauges, state of the valves, proper functioning of the pumps) while facing many hazards (leaks, obstacles or holes in its path). The aim of this article is to present the major components of our robot's architecture and the algorithms we developed for certain functions (localization, gauge reading, etc). We also present the methodology that we adopted and that allowed us to succeed in this challenge.