Construction of a Smart Vision-Guided Robot System for Manipulation in a Dynamic Environment

Ārents, Jānis; Greitāns, Modris; Lesser, Bernd

doi:10.1201/9781003337232-18

Cited by 5 publications

(3 citation statements)

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“…One of the most popular ways to make robots smarter is by attaching cameras [4] that can acquire information on a specific region of interest or perceive the environment around the robot and adjust their movements respectively [19]. Therefore, by giving the robots an ability to see, comprehend and act accordingly, we can automate tasks that traditionally require human intelligence or complex and very spacious machines [5].…”

Section: Introductionmentioning

confidence: 99%

Synthetic Data of Randomly Piled, Similar Objects for Deep Learning-Based Object Detection

Arents,

Lesser,

Bizuns

et al. 2022

Lecture Notes in Computer Science

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Currently, the best object detection results are achieved by supervised deep learning methods, however, these methods depend on annotated training data. With the synthetic data generation approach, we intend to mimic the real data characteristics and diversify the dataset by a systematic rendering of highly realistic synthetic pictures. We systematically explore how different combinations and portions of real and synthetic datasets affect object detectors performance. The developed synthetic data generation framework shows promising results in deep learning-based object detection tasks and can supplement real data when the variety of real training data is insufficient. However, when synthetic data ratio increases over real data ratio, a decrease in average precision can be observed, which has the most affect on 0.75-0.95 IoU threshold range.

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Section: Introductionmentioning

confidence: 99%

Synthetic Data of Randomly Piled, Similar Objects for Deep Learning-Based Object Detection

Arents,

Lesser,

Bizuns

et al. 2022

Lecture Notes in Computer Science

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“…Figure 3. Some of the possible combinations of different conditions: (a) single object[35]; (b) multiple, similar and overlapping objects[36]; (c) complex, multiple, different and divided objects[37], (d) multiple, different and overlapping objects[38].…”

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Smart Industrial Robot Control Trends, Challenges and Opportunities within Manufacturing

2022

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Industrial robots and associated control methods are continuously developing. With the recent progress in the field of artificial intelligence, new perspectives in industrial robot control strategies have emerged, and prospects towards cognitive robots have arisen. AI-based robotic systems are strongly becoming one of the main areas of focus, as flexibility and deep understanding of complex manufacturing processes are becoming the key advantage to raise competitiveness. This review first expresses the significance of smart industrial robot control in manufacturing towards future factories by listing the needs, requirements and introducing the envisioned concept of smart industrial robots. Secondly, the current trends that are based on different learning strategies and methods are explored. Current computer-vision, deep reinforcement learning and imitation learning based robot control approaches and possible applications in manufacturing are investigated. Gaps, challenges, limitations and open issues are identified along the way.

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“…• "Vision, Identification, with Z-sensing Technology and key Applications" (VIZTA, GA:826600), • "Intelligent Motion Control under Industry 4.E" (IMOCO4.E, GA:101007311). Horizon 2020 project: [4][5][6][7][8][9][10], scientific conferences [11][12][13], and book chapters [14,15]. Part of the research activities of the Thesis are included in the "Smart robot system with advanced vision, sensing, and human gesture understanding capabilities" which was evaluated by the Latvian Academy of Sciences in the annual Science Achievements' Competition as one of the most significant scientific achievements in Latvia in 2022¹.…”

Section: Practical Value and Approbationmentioning

confidence: 99%

Research and Development of Smart Control Methods for Industrial Robots

Ārents

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This thesis addresses the smart control methods for industrial robots, their applications, and learning strategies, in particular focusing on efficient data preparation, computer vision-based robot control, and knowledge acquisition from humans through demonstrations. The primary aim of the Thesis is to research and develop novel techniques in these fields to improve the potential deployment of industrial robots for working in dynamic environments and advance the operability of such systems. Based on the identified challenges and open issues during the literature review, the Thesis develops and evaluates novel synthetic data generation techniques for usage in computer vision-based robot control and an approach for learning from human demonstrations.

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Construction of a Smart Vision-Guided Robot System for Manipulation in a Dynamic Environment

Cited by 5 publications

References 0 publications

Synthetic Data of Randomly Piled, Similar Objects for Deep Learning-Based Object Detection

Synthetic Data of Randomly Piled, Similar Objects for Deep Learning-Based Object Detection

Smart Industrial Robot Control Trends, Challenges and Opportunities within Manufacturing

Research and Development of Smart Control Methods for Industrial Robots

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