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3D Joist Perception, Detection, and Climbing for Hexapod Robot

3D Joist Perception, Detection, and Climbing for Hexapod Rob...

IS and T International Symposium on Electronic Imaging: intelligent robotics and industrial applications using computer vision, iriacv 2023

作者： Li, Yibin Zakhor, Avideh Department of Electrical Engineering and Computer Science University of California Berkeley United States

Avoiding obstacles is challenging for autonomous robotic systems. In this work, we examine obstacle avoidance for legged hexapods, as it relates to climbing over randomly placed wooden joists. Our main motivation is to enable robots to navigate inside tight attic spaces of single-family residential homes in the U.S., which typically contain rows of 4, 6, or 8 inch tall joists placed 16 inches apart from each other. We formulate the task as a 3D joist detection problem, and propose a detect-plan-act pipeline using a simultaneous localization and mapping (SLAM) algorithm to generate a pointcloud and a grid map to expose high obstacles such as joists. A line detector is applied on the grid map to extract parameters of the joist, such as height, orientation, and distance;based on this information the hexapod plans a sequence of leg movements to either climb over the joist or move sideways if the joist is too high. We show that our perception and path planning modules work well on the real-world joists with different heights and orientations. © 2023, Society for Imaging Science and Technology.

关键词： Motion planning

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Combining boosting machine learning and swarm intelligence for real time object detection and tracking: towards new meta-heuristics boosting classifiers

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INTERNATIONAL JOURNAL OF intelligent robotics AND applications 2017年第4期1卷 410-428页

作者： Benabderrahmane, Sidahmed Univ Edinburgh Sch Informat 10 Crichton St Edinburgh EH8 9AB Midlothian Scotland

Artificial vision in robotics involves real time detection of objects for fast decision making. Such intelligent systems require efficient algorithms and big learning database of examples for producing robust classifiers. Several methods of objects detection and tracking have been proposed in the literature. However, even though the detection rates have been improved, the processing time and the complexity of the models still representing a key challenge. In this paper, we present a real time object detection and tracking framework based on Adaboost classification, where a strong classifier is generated using an iterative combination of weak learners. This method is based on the use of discriminative features by analyzing different regions of the input image. Instead of performing a full traversal in the entire search space of all possible visual features, we propose to use intelligent heuristics for accelerating time processing and extracting relevant features in the image that lead to a best detection rate. The meta-heuristics involve the use of genetic algorithms, particle swarm optimization, random walk and a novel hybrid combination of these methods. The obtained results, in a case of intelligent transportation system, have shown considerable improvements in term of computation time, efficiency and accuracy.

关键词： Machine learning Boosting Metaheuristics intelligent systems computer vision in robotics Object detection

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Tire Defect Detection with Limited Annotation

Tire Defect Detection with Limited Annotation

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IS and T International Symposium on Electronic Imaging: intelligent robotics and industrial applications using computer vision, iriacv 2023

作者： Ho, Chih-Hui Huang, Ziheng Kim, Na Yeon Bae, You Suk Vasconcelos, Nuno Department of Electrical and Computer Engineering University of California San Diego United States Department of Computer Engineering Tech University of Korea Korea Republic of

Tire defect detection has significant industrial value and has been a research topic in both academia and industry. Despite its importance, prior works does not consider the practical manufacturing circumstances, where there is only limited annotation for the defect. Such limitation hinders the prior works from deploying to the real-world system. To address the problem of Tire Defect Detection with Limited Annotation (TTDLA), we proposed a novel framework, denoted as tire defect detection with Self-Supervision and Synthetic data (or S3). S3 first uses self-supervised learning to train the encoder without using any labeled data in the pretraining stage. The encoder is then adopted as the encoder of the Faster-RCNN detector in the fine-tuning stage. In addition, we proposed an algorithm to generate synthesized image by pasting defects randomly onto the regular image. Experiments demonstrate that both self-supervised learning and synthesized data boost the performance of the detector under TTDLA scenario. © 2023, Society for Imaging Science and Technology.

关键词： Anomaly detection

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Single Shot Appearance Model (SSAM) for multi-target tracking

Single Shot Appearance Model (SSAM) for multi-target trackin...

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2019 intelligent robotics and industrial applications using computer vision Conference, iriacv 2019

作者： Ullah, Mohib Ullah, Habib Cheikh, Faouzi Alaya Norwegian University of Science and Technology Norway College of Computer Science and Engineering University of Ha'il Saudi Arabia

An appearance model plays a crucial rule in multi-target tracking. In traditional approaches, the two steps of appearance modeling i.e visual representation and statistically similarity measure are modeled separately. Visual representation is achieved either through hand-crafted features or deep features and statically similarity is measure through a cross entropy loss function. A loss function based on cross-entropy (KL-divergence, mutual information) find closely related probability distribution for the targets. However, if the targets have similar visual representation, it ends up mixing the targets. To tackle this problem, we come up with a synergetic appearance model named Single Shot Appearance Model (SSAM) based on Siamese neural network. The network is trained with a contrastive loss function for finding the similarity between different targets in a single shot. The input to the network is two target patches and based on their similarity, a contrastive score is output by the network. The proposed model is evaluated on accumulative dissimilarity metric on three datasets. Quantitatively, promising results are achieved against three baseline methods. © 2019, Society for Imaging Science and Technology.

关键词： Probability distributions

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intelligent robotics and applications 1

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丛书名： Lecture Notes in computer Science

1000年

作者： YongAn Huang Hao Wu Zhouping Yin Honghai Liu

The three volume set LNAI 10462, LNAI 10463, and LNAI 10464 constitutes the refereed proceedings of the 10th International Conference on intelligent robotics and applications, ICIRA 2017, held in Wuhan, China, in Augu... 详细信息

ISBN: (数字)9783319652986

ISBN: (纸本)9783319652979

关键词： Artificial Intelligence computer Imaging, vision, Pattern Recognition and Graphics Control, robotics, Mechatronics computers and Society computer applications User Interfaces and Human computer Interaction

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intelligent robotics and applications 1

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丛书名： Lecture Notes in computer Science

1000年

作者： YongAn Huang Hao Wu Zhouping Yin Honghai Liu

ISBN: (数字)9783319652924

ISBN: (纸本)9783319652917

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Creation of a fusion image obtained in different electromagnetic ranges used in industrial robotic systems

Creation of a fusion image obtained in different electromagn...

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2020 intelligent robotics and industrial applications using computer vision Conference, iriacv 2020

作者： Semenishchev, Evgenii Voronin, Viacheslav Zelensky, Aleksandr Tolstova, Irina Moscow Russia Rostov-on-Don Russia

Modern robotic systems allow you to automate processes and increase employee productivity. To create such systems, finite state machines (sensor systems) and machine vision systems are used. The scope of their application may be the development of a robotic system within the framework of the INDUSTRY 4.0 project. The article proposes an approach based on combining data obtained by fusion from cameras working in various electromagnetic ranges. An approach is proposed that is based on a fusion of data on the shape, boundaries, and parameters of objects. The search for the boundaries and shape of objects is based on the layer-bylayer simplification of images with the allocation of local features at each level. The search for local features is based on the allocation of local stationary sections, the allocation of the boundaries of objects, the determination of their parameters and the combination of information in a single information space. The search for boundaries is based on the use of the method of combined image analysis with a joint analysis of the L2 norm criterion. As a measure of a discrepancy, the square of the difference of the input value and the resulting estimate is used. As an example, the results of fusing images based on the combination of infrared data, RGB data, and IR cameras are presented. © 2020 Society for Imaging Science and Technology.

关键词： Cameras

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Automatic inspection of aeronautic components

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MACHINE vision AND applications 2017年第5-6期28卷 591-605页

作者： San Biagio, Marco Beltran-Gonzalez, Carlos Giunta, Salvatore Del Bue, Alessio Murino, Vittorio Ist Italiano Tecnol Pattern Anal & Comp Vis Dept PAVIS Genoa Italy AVIOAero Rivalta Di Torino Italy

industrial processes are costly in terms of time, money and customer satisfaction. The global economic pressures have gradually led businesses to improve these processes to become more competitive. As a result, the demand of intelligent visual inspection systems aimed at ensuring the high quality in production lines is increasing. In this paper, we present a computer vision system that, using only images, is able to address two main problems: (i) model checking: automatically check whether a component meets given specifications or rules, (ii) visual inspection: defect inspection on irregular surfaces, in particular, decolourization and scratches detection. In the experimental results, we show the effectiveness of our system and the readiness of such technologies for their integration in industrial processes.

关键词： Automatic visual inspection Model checking Machine learning Defects inspection Image processing Machine vision Registration Multi-view analysis

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vision-based machine learning worker assistance system for people with disabilities on low-cost hardware

Vision-based machine learning worker assistance system for p...

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2021 intelligent robotics and industrial applications using computer vision Conference, iriacv 2021

作者： Christ, Micha Jauch, Christian Denecke, Julia Wiedenroth, Saskia J. Department Image and Signal Processing Fraunhofer IPA Stuttgart Germany

Working in protected workshops places supervisor workers in a work field with concurrent targets. On the one side, the workers with disabilities require a safe space to meet special requirements and on the other side, customers expect comparable time and quality standards than in the normal industry while maintaining cost pressure. We propose a technical solution to support the supervisors with the quality control. We developed a flexible assistance system for people with disabilities working in protected workshops that is based on a Raspberry Pi4 and uses cameras for perception. It is appliable for packaging and picking processes and is supported by additional step by step guidance to reach as many protected workshops as possible. The system tries to support supervisors in quality control and provide information if any action is required to free time for interpersonal matters. An automatic pick-by-light system is included which uses hand recognition. To ensure good speed we used image processing and verified the detections with a machine learning approach for robustness against lighting conditions. In this paper we present the system, which is available open source, itself with its features and the development of the machine learning algorithm. © 2021 Society for Imaging Science and Technology. All rights reserved.

关键词： Supervisory personnel

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Terrain Segmentation for Commercial Vehicles and Working Machines

Terrain Segmentation for Commercial Vehicles and Working Mac...

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IS and T International Symposium on Electronic Imaging: intelligent robotics and industrial applications using computer vision, iriacv 2023

作者： Edlinger, Raimund Mitterhuber, Ulrich Nüchter, Andreas University of Applied Sciences Upper Austria Wels Austria Informatics XVII - Robotics Julius-Maximilians University Würzburg Germany

In the field of automated working machines, not only is the general trend towards automation in industry, transport and logistics reflected, but new areas of application and markets are also constantly emerging. In this paper we present a pipeline for terrain classification in off-road environments and in the field of "automated maintenance of slopes", which offers potential for solving numerous socioeconomic needs. Working tasks can be made more efficient, more ergonomic and, in particular, much safer, because mature, automated vehicles are used. At present, however, such tasks can only be carried out remotely or semi-automatically, under the supervision of a trained specialist. This only partially facilitates the work. The real benefit only comes when the supervising person is released from this task and is able to pursue other work. In addition to the development of a safe integrated system and sensor concept for use in public spaces as a basic prerequisite for vehicles licensed in the future, increased situational awareness of mobile systems through machine learning in order to increase their efficiency and flexibility, is also of great importance. © 2023, Society for Imaging Science and Technology.

关键词： Commercial vehicles

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