Mobile robot localization and object description

Cernohorsky, Josef; Jandura, Pavel; Mach, Ondrej

doi:10.1109/carpathiancc.2017.7970452

Cited by 7 publications

(2 citation statements)

References 1 publication

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“…The localization of the mobile platform can be handled by sensor fusion of several different sensor principles [8,9]. Typically, odometry, nine DOF sensor, combines accelerometers, gyroscopes and magnetometers, GPS or ultra-wideband (UWB) absolute localization [10], and even advanced visual systems.…”

Section: Quick Overview Of Possible Solutionmentioning

confidence: 99%

Robotic Plug-in Combined Charging System with Improved Robustness

Cernohorsky

Jandura

2019

The 6th International Electronic Conference on Sensors and Applications

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This paper describes the development of an algorithm robotic plug-in of a charging system for mobile platform. In the first chapter, there is a short overview of possibilities of automatic plugin system, including proprietary industrial solution. In the main part, there is a description of the system based on UR robot with build-in force torque sensors and Intel RealSense Camera. This camera combines IR depth lens with regular RGB camera and six DOF inertial sensor, which is used in our application too. The conventional solution of this problem is usually based on RGB image processing in various state of the art, from simple pattern matching, neural network, or genetic algorithm to complex AI solution. The quality of the solution mostly depends on robustness of image processing. In our cases, we use simple sensor fusion. Thanks to multiple information and constrain of values, we can assume, if the algorithm is proceeding successfully or not. The system uses the internal parameters of the robotic arm, e.g., end-effector position and orientation and forcetorque information in tool center point. The next information is RGB camera image and camera depth image, and the inertial unit build in camera. The other important information is the location of the vehicle inlet on the mobile platform, where the shape of mobile platform is considered as a constrain for image processing. The system is validated only on a physical model with CCS type 2 plug and vehicle inlet, because the mobile platform is under construction by another team.

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Section: Quick Overview Of Possible Solutionmentioning

confidence: 99%

Robotic Plug-in Combined Charging System with Improved Robustness

Cernohorsky

Jandura

2019

The 6th International Electronic Conference on Sensors and Applications

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“…One of the key tools developed is OpenCV, which is an open-source computer vision library [1]. Scenarios utilising computer vision use a digital camera attached to the endeffector [2], a fixed [3] or a mobile robot [4]. The fixed approach has proved more accurate with its detection compared to mounting the camera on the end-effector and to the mobile robot due to error caused by vibration through their movement.…”

Section: Introductionmentioning

confidence: 99%

Visual control of a robotic arm

Carter¹,

Tokhi²,

Oumar³

2018

Robotics Transforming the Future

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Integration of robot control with computer vision is an emerging technology. The ability to capture a real world scenario, filter out a specific object, and then localise it with respect to a specific point opens up many avenues for future technologies. In this paper, computer vision is integrated with a 6-DOF robotic arm. The aim is to develop a methodology for controlling a robotic arm through visual interaction. The investigations carried out include mathematical and geometrical representation of the physical manipulator, object detection utilising a colour segmentation approach, establishing communications from the command line of the visual recognition program with an Arduino and development of a user orientated software package that integrates the two key objectives.

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