János Nacsa

6603669168

Publications - 4

AR Solution for Indoor Drone Motion Forecasting

Imre Paniti József Tóth Erik Tóth János Nacsa

Publication Name: Springer Proceedings in Advanced Robotics

Publication Date: 2024-01-01

Volume: 33 SPAR

Issue: Unknown

Page Range: 60-64

Description:

In case of indoor drone applications three different areas are classified: inventory management, indoor intra-logistics and inspection & surveillance. For each drone movement there is an optimal trajectory where the time for reaching point B from point A can be minimized. This optimal trajectory cannot be executed in case a human is blocking the route. In order to avoid collisions with the drone, either the drone trajectory has to be modified in real-time (which might cost additional time and energy in case the drone delivers a heavy object) or the human operator has to be warned with a pre-defined understandable signal so he/she can modify his/her movement in time. In this paper, the implementation of an Augmented Reality solution (previously tested in an industrial relevant environment on a collaborative robot) using a micro drone is presented.

Open Access: Yes

DOI: 10.1007/978-3-031-76428-8_12

Human–robot collision predictor for flexible assembly

Imre Paniti Péter Kovács Dávid Szűr János Nacsa

Publication Name: Acta Imeko

Publication Date: 2021-01-01

Volume: 10

Issue: 3

Page Range: 72-80

Description:

The performance of human–robot collaboration can be improved in some assembly tasks when a robot emulates the effective coordination behaviours observed in human teams. However, this close collaboration could cause collisions, resulting in delays in the initial scheduling. Besides the commonly used acoustic or visual signals, vibrations from a mobile device can be used to communicate the intention of a collaborative robot (cobot). In this paper, the communication time of a virtual reality and depth camera-based system is presented in which vibration signals are used to alert the user of a probable collision with a UR5 cobot. Preliminary tests are carried out on human reaction time and network communication time measurements to achieve an initial picture of the collision predictor system’s performance. Experimental tests are also presented in an assembly task with a three-finger gripper that functions as a flexible assembly device.

Open Access: Yes

DOI: 10.21014/ACTA_IMEKO.V10I3.1072

Recent advances in learning content and infrastructure development for layout and process planning courses at the SZTAKI learning factories

Zsolt Kemény Richárd Beregi Bence Tipary Kristóf Abai János Nacsa

Publication Name: Procedia Manufacturing

Publication Date: 2020-01-01

Volume: 45

Issue: Unknown

Page Range: 319-324

Description:

Two locations maintained by SZTAKI-in Budapest and Gyor, respectively-provide infrastructure for learning factory programs, primarily in layout and process planning, and process execution. In addition to project-oriented work successfully hosted by the facilities for several years, the development of repeatable and evolvable learning content began in 2019. A preceding publication presented a roadmap for the development of re-usable courses based on outcomes of one-shot projects which built up an initial infrastructure. This paper gives an in-progress view at selected dimensions of learning content and course development. In view of recent additions to available infrastructure, an extended portfolio of design and scenario choices is presented with suggested sets of options which can be opened up for elaboration by course participants. Complementing these, typical course types are also summarized, with special emphasis on options likely to be deployed in the current operating context of the facilities.

Open Access: Yes

DOI: 10.1016/j.promfg.2020.04.024

A Learning Factory Environment for Human–Robot Collaboration-Based Remanufacturing Supported by Artificial Intelligence Solutions

Imre Paniti Zsolt Kemény Mátyás Hajós János Nacsa

Publication Name: Lecture Notes in Networks and Systems

Publication Date: 2025-01-01

Volume: 1546 LNNS

Issue: Unknown

Page Range: 296-303

Description:

In contrast to one-way assembly of products, simple disassembly and more complex remanufacturing present additional challenges and unknowns on several levels, often requiring human capabilities to be combined with machines—thereby becoming a rewarding deployment field for human–robot collaboration, supported by artificial intelligence, advanced planning and extended reality for improved human–machine interrelations. While the industry has realized little benefit of these—still evolving—areas, learning factories can contribute to closing gaps in skills and mindset of future engineers likely to actively shape the aforementioned fields at the time they begin to notably penetrate industrial production. The paper proposes an approach for building up a portfolio of learning factory resources supporting students in acquiring and independently refining knowledge and practice related to collaborative remanufacturing. The paper presents an incremental approach extending manufacturing knowledge to diagnostics and disassembly in collaborative environments, with an outlook on more comprehensive remanufacturing.

Open Access: Yes

DOI: 10.1007/978-3-031-98883-7_36