Sirvi Autor "Avalos Conchas, Paola" järgi

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Payload transportation system of a learning factory
(Tartu Ülikool, 2023) Avalos Conchas, Paola; Kruusamäe, Karl, juhendaja; Vunder, Veiko, juhendaja
The transition to Industry 4.0 has highlighted the urgent need for a skilled workforce proficient in operating and maintaining advanced robotic and automation systems. To address this critical issue and prepare the next generation of industry professionals, this thesis focuses on the design and implementation of a payload transportation system for a Learning Factory. The proposed system combines SLAM, AR Tracking, and ROS Navigation technologies to enable efficient navigation and obstacle avoidance within the Learning Factory environment. Additionally, the system facilitates seamless cooperation with a manipulator robot, enabling collaborative tasks and enhancing the overall efficiency of the system. The outcome of this work is demonstrated in a book transportation scenario incorporating a multi-robot system that consists of two manipulator robots, and a mobile base. This work contributes to bridging the skills gap and equipping the next generation of industry professionals with practical robotics knowledge.
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Socially Aware Planning for Indoor Navigation
(Tartu Ülikool, 2025) Avalos Conchas, Paola; Kruusamäe, Karl, juhendaja; Zhang, Bin, juhendaja; Tartu Ülikool. Loodus- ja täppisteaduste valdkond; Tartu Ülikool. Tehnoloogiainstituut
As robots increasingly present in human-populated spaces, they must be able to navigate among humans safely and without disrupting. People try to preserve their own personal space when moving in real-world social spaces. However, the current navigation methods do not consider this aspect and treat humans as any other obstacle.This thesis proposes a method that considers personal space for humans as well as social navigation norms. For this purpose, the robot converts camera-based human detections to a costmap form and define the personal space as a Gaussian asymmetric function. The proposed solution is validated through real-world experiments, demonstrating that the robot can improve the quality of navigation. The proposed solution is available on GitHub as a costmap layer that can be easily integrated into existing frameworks.