Authors:
Piotr Artiemjew
1
;
Karolina Krzykowska-Piotrowska
2
and
Marek Piotrowski
3
Affiliations:
1
Faculty of Mathematics and Computer Science, University of Warmia and Mazury in Olsztyn, Poland
;
2
Faculty of Transport, Warsaw University of Technology, Poland
;
3
Faculty of Economic Sciences, University of Warmia and Mazury in Olsztyn, Poland
Keyword(s):
Mobile Robotics, Behaviour Tracking, Convolutional Neural Networks (CNN), Spot Boston Dynamics, Unitree Go2 Pro, Human–Robot Interaction.
Abstract:
In a world where mobile robotics is increasingly entering various areas of people’s lives, creating systems that track the behavior of mobile robots is a natural step toward ensuring their proper functioning. This is particularly important in cases where improper use or unpredictable behavior may pose a threat to the environment and, above all, to humans. It should be emphasized that this is especially relevant in the context of using robotic solutions to improve the quality of life for people with special needs, as well as in human–robot interaction. Our primary aim was to verify the experimental effectiveness of classification based on convo-lutional neural networks for detecting behaviours of four-legged robots. The study focused on evaluating the performance in recognising typical robot poses. The research was conducted in our robotics laboratory, using Spot and Unitree Go2 Pro quadruped robots as experimental platforms. We addressed the challenging task of pose recognition witho
ut relying on motion tracking — a difficulty particularly pronounced when dealing with rotations.
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