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A Geometric Pulling Force controller for Aerial Robotic Workers

IFAC-PapersOnLine 2017年第1期50卷 10287-10292页

作者： Wuthier D. Kominiak D. Fresk E. Nikolakopoulos G. Robotics Group at the Control Engineering Division of the Department of Computer Electrical and Space Engineering Luleå University of Technology Luleå SE-97187 Sweden

The aim of this article is to establish a geometric, pulling force control scheme in order to enable the concept of Aerial Robotic Workers (ARWs), where the capabilities of the Unmanned Aerial Vehicles (UAVs) are enhanced by aerial manipulators in order to exert known pulling forces on the environment, with characteristic applications such as levers actuation, debris removal and safety assessments. The proposed novel approach consists of interfacing a cascaded position control scheme with a manipulation framework in such a way that the UAV, together with the manipulator are being controlled in a complete system The validity of the proposed scheme as well as the ability of the UAV to track a desired pulling force is validated through a real-world experiment. © 2017

关键词： aerial manipulation field robotics flying robots robots manipulators unmanned aerial vehicles

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CrowdWatcher: an open-source platform to catch the eye of the crowd

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Quality and User Experience 2019年第1期4卷 1-17页

作者： Lebreton, Pierre Hupont, Isabelle Hirth, Matthias Mäki, Toni Skodras, Evangelos Schubert, Anton Raake, Alexander Group of Networked Sensing and Control (NeSC) Zhejiang University Hangzhou China Institute of Intelligent Systems and Robotics Sorbonne University Paris France User-Centric Analysis of Multimedia Data Group Technische Universität Ilmenau Ilmenau Germany Department of Computer Science Aalto University Espoo Finland Department of Electrical and Computer Engineering University of Patras Patras Greece Audio Visual Technology Group Technische Universität Ilmenau Ilmenau Germany

This paper presents the open-source eye tracking platform CrowdWatcher. It enables researchers to measure gaze location and user engagement in a crowdsourcing context through traditional RGB webcams. The proposed platform particularly advances the field of Quality of Experience (QoE) research, as it allows the experimenter to collect remotely and with very limited effort novel information from crowds of participants, such as their commitment towards a task, attention and decision-making processes. Two different experiments are described that were conducted to demonstrate the platform’s potential. The first experiment addresses the measurement of participants’ behavior while performing a movie selection task. Results show that the platform provides complementary information to traditional self-reported data by taking gaze analysis into account. This is of particular relevance, since in a crowdsourcing context decision processes and attention are difficult to assess, and there is often limited control over the engagement of the test user with the task. A second experiment is conducted in the scenario of a multimedia QoE test. Prediction accuracy is compared to a professional infrared eye tracker. While CrowdWatcher performs less well than the professional eye tracker, it is still able to collect valuable gaze information in the far more challenging environment of crowdsourcing. As an outlook to further application domains, the usage of the platform to measure user engagement allows participants who do not pay attention to the task to be identified.

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Deictic gesture enhanced fuzzy spatial relation grounding in natural language

Deictic gesture enhanced fuzzy spatial relation grounding in...

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2017 IEEE International Conference on Fuzzy Systems, FUZZ 2017

作者： Srimal, Arjuna S. Muthugala, Viraj J. Jayasekara, Buddhika P. Robotics and Control Laboratory Department of Electrical Engineering University of Moratuwa Moratuwa10400 Sri Lanka

ISBN: (纸本)9781509060344

In the recent past, domestic service robots have come under close scrutiny among researchers. When collaborating with humans, robots should be able to clearly understand the instructions conveyed by the human users. Voice interfaces are frequently used as a mean of interaction interface between users and robots, as it requires minimum amount of work overhead from the users. However, the information conveyed through the voice instructions are often ambiguous and cumbersome due to the inclusion of imprecise information. The voice instructions are often accompanied with gestures especially when referring objects, locations, directions etc. in the environment. However, the information conveyed solely through these gestures is also imprecise. Therefore, it is more effective to consider a multimodal interface rather than a unimodal interface in order to understand the user instructions. Moreover, the information conveyed through the gestures can be used to improve the understanding of the user instructions related to object placements. This paper proposes a method to enhance the interpretation of user instructions related to the object placements by interpreting the information conveyed through voice and gestures. Furthermore, the proposed system is capable of adapting the understanding, according to the spatial arrangement of the workspace of the robot. Fuzzy logic system is proposed in order to evaluate the information conveyed through these two modalities while considering the arrangement of the workspace. Experiments have been carried out in order to evaluate the performance of the proposed system. The experimental results validate the performance gain of the proposed multimodal system over the unimodal systems. © 2017 IEEE.

关键词： Fuzzy logic

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Identification of friendly and deictic gestures in a sequence using upper body skeletal information

Identification of friendly and deictic gestures in a sequenc...

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2017 IEEE Region 10 Conference, TENCON 2017

作者： Sirithunge, H. P. Chapa Arjuna, P.H.D. Srimal, S. Buddhika, A.G. Jayasekara, P. Robotics and Control Laboratory Department of Electrical Engineering University of Moratuwa Moratuwa10400 Sri Lanka

ISBN: (纸本)9781509011339

Human-robot interaction has become a popular topic in the domestic environment owing to the increase in elderly population. In order to achieve a more natural interaction process, robots are expected have human-like interaction capabilities. Human-like behavior should extend beyond the interaction process to perceive human intention. The robots are required to have intelligence to interpret and distinguish a human user's instructions as well as friendly behavior before making decisions regarding interaction. This paper presents a model that can identify a sequence of gestures without the interference of voice commands. The system extracts and analyzes upper body skeletal information for a period of time in order to identify friendly and deictic gestures separately, before initiating an interaction on user demand. Ability of the robot to perceive friendly or calling gestures alongside deictic gestures make the task of commanding the robot easier and nonverbal. This behavior enables interaction process even in noisy environments as well as at relatively longer distances. To achieve this behavior, the system is equipped with separate units to extract and analyze information, estimate 3 types of gestures in series and finally to take decisions regarding whether to interact with the user. The system is implemented and tested in an artificially created domestic environment. Results of the conducted experiment are used to verify the behavior of the proposed system. © 2017 IEEE.

关键词： Human robot interaction

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Interpreting fuzzy directional information in navigational commands based on arrangement of the surrounding environment

Interpreting fuzzy directional information in navigational c...

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2017 IEEE International Conference on Fuzzy Systems, FUZZ 2017

作者： Viraj, M.A. Muthugala, J. Buddhika, A.G. Jayasekara, P. Robotics and Control Laboratory Department of Electrical Engineering University of Moratuwa Moratuwa10400 Sri Lanka

ISBN: (纸本)9781509060344

Human friendly service robots should possess human like interaction and reasoning capabilities. Humans prefer to use voice instructions in order to communicate with peers. Those voice instructions often include linguistic notions and descriptors that are fuzzy in nature. Therefore, the human friendly robots should be capable of understanding the fuzzy information in user instructions. This paper proposes a method in order to interpret directional information in navigational user commands by considering the environment dependent fuzziness associated with the directional linguistic notions. A module called Direction Interpreter has been introduced for handling the fuzzy nature of directional linguistic notions. The module has been implemented with a fuzzy logic system that is capable of modifying the perception of the robot about the directional information according to the surrounding environment of the robot. This modification is done by weighting the output membership function with the distribution of the free space around the robot. According to the experimental results, the proposed system is capable of replicating the natural directional perception of humans that depends on the environment to a greater extent than the existing approaches. © 2017 IEEE.

关键词： Linguistics

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Accurate Object Recognition for Unmanned Aerial Vehicle Electric Power Inspection using an Improved YOLOv2 Algorithm

Accurate Object Recognition for Unmanned Aerial Vehicle Elec...

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IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Qiang Chen Yunyuan Gao Yun Peng Jianhai Zhang Ke Sun Intelligent Control & Robotics Institute Hangzhou Dianzi University Hangzhou China Hangzhou Dianzi University Hangzhou China Department of Biomedical Engineering University of Houston Houston United States School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China Hangzhou Chuangjiang Information Technology Co. Ltd. Hangzhou China

With the growing application of Unmanned Aerial Vehicle (UAV) in the electric power inspection, there is an increasing demand for accurate recognition of power tower components from a large number of pictures and videos. Traditional machine learning methods require feature extraction of, such as edge, color, texture, etc., are not suitable for the complex scene of the UAV electric power inspection. The end-to-end object detection algorithm YOLOv2, offers overall high recognition accuracy and speed, but is less so in recognizing small targets . In this study, the YOLOv2 was improved by: 1)adding an additional passthrough layer to the original network to fuse more fine-grained features to improve the recognition capability for small targets, and 2) removing the redundant convolutional layers and reducing the number of the filters in the middle convolutional layers to simplify the network structure. The datasets are from Multi-rotor UAV inspection of high voltage transmission lines. Three types of power tower components including the tower, tower base, and signs are selected as recognition targets. Results showed that the mean average precision (mAP) of the improved model for three types of target recognition was increased to 98.19%, higher than that of the original one, and the recognition speed was increased by 35%.

关键词： Conferences Data science Cyberspace

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Dismantling strategy for capacitors placed on printed circuits boards: challenges and preliminary results

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IOP Conference Series: Materials Science and engineering 2020年第1期997卷

作者： S Cazan D Chirita C Stamate D Irimia A Burlacu I Doroftei Department of Mechanical Engineering Mechatronics and Robotics 'Gheorghe Asachi' Technical University of Iasi Iasi Romania Department of Energy Utilisation Electrical Drives and Industrial Automation 'Gheorghe Asachi' Technical University of Iasi Iasi Romania Department of Automation Control and Applied Informatics 'Gheorghe Asachi' Technical University of Iasi Iasi Romania

The present paper describes the challenges and preliminary results obtained during the design and implementation of a disassembly system for capacitors placed on printed circuits boards. This research is included in an attempt to reduce the impact of the electronic waste. The proposed architecture includes a six axis multipurpose industrial robot, dedicated sensors and a custom built tool. An originally designed rotary tool, with a specific type of drill bit, was used to dismantle each capacitor on the board. A sensor triggers the tool for the drill rotation when the perforation is accomplished accordingly, so that the friction force is big enough to initiate the dismantling procedure. Using the rotation movement of the drill, at a calculated number of rotations, the capacitors are successfully dismantled from the printed circuits board. An artificial vision system composed from a stereo camera and an image-processing algorithm, provides data with regard to the position of each capacitor on the printed circuit board. A vacuum cleaner was integrated in the scheme to recover the capacitors after dismantling. The scheme design was perpetually optimized using dedicated simulation software. Also, preliminary results obtained on the physical system underline the potential advantages of the proposed approach.

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Retrieval-based localization based on domain-invariant feature learning under changing environments

arXiv

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arXiv 2019年

作者： Hu, Hanjiang Wang, Hesheng Liu, Zhe Yang, Chenguang Chen, Weidong Xie, Le Autonomous Robot Lab Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education Shanghai Jiao Tong University Shanghai200240 China T Stone Robotics Institute Chinese University of Hong Kong Hong Kong Bristol Robotics Laboratory University of the West of England BristolBS16 1QY United Kingdom School of Material Science and Engineering Shanghai Jiao Tong University Shanghai200240 China

Visual localization is a crucial problem in mobile robotics and autonomous driving. One solution is to retrieve images with known pose from a database for the localization of query images. However, in environments with drastically varying conditions (e.g. illumination changes, seasons, occlusion, dynamic objects), retrieval-based localization is severely hampered and becomes a challenging problem. In this paper, a novel domain-invariant feature learning method (DIFL) is proposed based on ComboGAN, a multi-domain image translation network architecture. By introducing a feature consistency loss (FCL) between the encoded features of the original image and translated image in another domain, we are able to train the encoders to generate domain-invariant features in a self-supervised manner. To retrieve a target image from the database, the query image is first encoded using the encoder belonging to the query domain to obtain a domain-invariant feature vector. We then preform retrieval by selecting the database image with the most similar domain-invariant feature vector. We validate the proposed approach on the CMU-Seasons dataset, where we outperform state-of-the-art learning-based descriptors in retrieval-based localization for high and medium precision scenarios. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

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Global Multivariable HOSM Differentiator for Output-Feedback Unit Vector control of Nonuniform Relative Degree Systems

Global Multivariable HOSM Differentiator for Output-Feedback...

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International Workshop on Variable Structure Systems, VSS

作者： Tiago Roux Oliveira Victor Hugo Pereira Rodrigues Andrei Battistel Leonid M. Fridman Department of Electronics and Telecommunication Engineering (DETEL) State University of Rio de Janeiro (UERJ) Brazil Department of Control Engineering and Robotics National Autonomous University of Mexico (UNAM) Federal District Mexico

In this paper, we propose an output feedback unit vector controller to solve the longstanding problem of global exact output tracking for a class of multivariable uncertain plants with nonlinear disturbances. In order to deal with the nonuniform arbitrary relative degree, we extend our previous estimation scheme based on global differentiators using dynamic gains to a multivariable framework. A diagonally stable assumption over the system high-frequency gain (HFG) matrix must be invoked. Preserving the same guidelines and simplicity of its monovariable version, the variable gain super-twisting algorithm (STA) is employed to obtain the robust and exact multivariable differentiator. In addition, norm observers for the unmeasured state are employed to dominate the disturbances as well as to update the differentiator gains since both may be state dependent. Thus, uniform global exponential stability and ultimate exact tracking are guaranteed. A numerical example is presented to illustrate the application of the proposed multivariable approach.

关键词： Observers MIMO communication Stability analysis Modulation Output feedback Gain Uncertainty

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Harmonic potential based communication-aware navigation and beamforming in cluttered spaces with full channel-state information

Harmonic potential based communication-aware navigation and ...

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2017 IEEE International Conference on robotics and Automation, ICRA 2017

作者： Afzal, Waqas Masoud, Ahmad A. Department of Electrical Engineering Control and Robotics King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia

ISBN: (纸本)9781509046331

In this paper, we introduce a communication-aware algorithm to navigate mobile agents with non-trivial dynamics in cluttered environments. The navigation technique is based on the Harmonic Potential Field (HPF) approach to motion-planning. The proposed approach employs beamforming at the Base-Station (BS) simultaneously with the robot's motion to increase the Channel Spectral Efficiency (CSE). The approach is developed and basic proofs of performance are provided. Realistic simulations using the WINNER-II wireless channel model are used to demonstrate the navigation algorithm. © 2017 IEEE.

关键词： Beamforming

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