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vision Based Automatic Landing with Runway Identification and Tracking

Vision Based Automatic Landing with Runway Identification an...

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international conference on control, automation, robotics and vision

作者： Amit Kumar Tripathi Vijay V Patel Radhakant Padhi Aeronautical Development Agency Bangalore India Faculty of Aerospace Engineering Indian Institute of Science Bangalore India

vision based autolanding of a fixed wing unmanned aerial vehicle (UAV) is presented in this paper. the UAV initially searches for a nearby unknown road or runway from the terrain image frames captured by onboard stereovision camera. the captured unknown road or runway are evaluated to be a possible candidate for autolanding of a UAV. Six degree of freedom (6DOF) simulink model of a UAV with landing and navigation autopilot is integrated with flightgear flight simulation software. Flightgear FG camera is used to capture image frames of the nearyby terrain while UAV is in auto navigation mode and performing a level flight with various heading angles to look out for all possible terrain scenario. Image processing techniques are applied on the captured video to identify a smooth and straight road or runway for landing and calculates the relative distance and orientation of the runway or road with respect to UAV's own location. Image processing techniques such as hough transform and random Sample consensus (RANSAC) are used to detect the road strip lines. Once the desired touchdown point on the road is computed as well as road heading is also computed, UAV begins the autolanding maneuver. the image processing techniques are used to identify the candidate road or runway. the heading of the runway and cross track of the runway is computed and autopilot is designed to bring the aircraft at desired touch down point on the road or runway.

关键词： Roads Cameras Unmanned aerial vehicles Image edge detection Silicon Aircraft

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Mature Tomato Fruit Detection Algorithm Based on improved HSV and Watershed Algorithm

Mature Tomato Fruit Detection Algorithm Based on improved HS...

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6th international-Federation-of-Automatic-control (IFAC) conference on Bio-robotics (BIOrobotics)

作者： Malik, Muhammad Hammad Zhang, Ting Li, Han Zhang, Man Shabbir, Sana Saeed, Ahmed China Agr Univ Key Lab Modern Precis Agr Syst Integrat Res Minist Educ Beijing Peoples R China Beijing Beida Software Engn Co Ltd Beijing Peoples R China China Agr Univ Coll Informat & Elect Engn Beijing Peoples R China

Tomato is well known fruit since it has many essential and beneficial nutrients like antioxidant, vitamin C and A for human daily diet. Tomato picking by hand is both labor and time consuming. therefore, to overcome these issues, tomato needs to be picked up automatically with the help of harvesting robot. Recently automation of fruit harvesting gains great popularity. To guide the harvesting robot to pick up the fruit correctly, it is important to correctly detect and find the location of the red mature fruit. In this study, a new mature tomato detection algorithm based on the improved HSV (Hue, Saturation, Value) color space and the improved watershed segmentation was proposed, by using a cheap and easily accessible RGB camera for guiding a robot to pick up red tomatoes automatically. For that purpose, the color images of tomato plants were captured under natural light. At the first stage, improved HSV transform was used to remove background and to detect only red tomatoes. then the morphological operations were applied to modify the detected fruit. the second step was the separation of connected fruits, so that the harvesting robot can pick up fruit correctly. To achieve this task, the improved watershed algorithm was applied. As a result, the detection of separated red mature tomato was done by using the improved HSV and watershed, with uneven illumination and complex background. the overall red fruit detection accuracy was up to 81.6%, and it shows good potential to be used on harvesting robot. (C) 2018, IFAC (international Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Harvesting robot Computer vision Detection Localization HSV Watershed

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Multi-robot motion-formation distributed control with sensor self-calibration: experimental validation

Multi-robot motion-formation distributed control with sensor...

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international conference on control, automation, robotics and vision (ICARCV)

作者： Hector Garcia de Marina Johan Siemonsma Bayu Jayawardhana Ming Cao Unmanned Aerial Systems Center Southern University of Denmark Denmark Faculty of Science and Engineering University of Groningen the Netherlands

In this paper, we present the design and implementation of a robust motion formation distributed control algorithm for a team of mobile robots. the primary task for the team is to form a geometric shape, which can be freely translated and rotated at the same time. this approach makes the robots to behave as a cohesive whole, which can be useful in tasks such as collaborative transportation. the robustness of the algorithm relies on the fact that each robot employs only local measurements from a laser sensor which does not need to be off-line calibrated. Furthermore, robots do not need to exchange any information with each other. Being free of sensor calibration and not requiring a communication channel helps the scaling of the overall system to a large number of robots. In addition, since the robots do not need any off-board localization system, but require only relative positions with respect to their neighbors, it can be aimed to have a full autonomous team that operates in environments where such localization systems are not available. the computational cost of the algorithm is inexpensive and the resources from a standard microcontroller will suffice. this fact makes the usage of our approach appealing as a support for other more demanding algorithms, e.g., processing images from onboard cameras. We validate the performance of the algorithm with a team of four mobile robots equipped with low-cost commercially available laser scanners.

关键词： Robot kinematics Robot sensing systems Mobile robots Shape Task analysis Robustness

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Enhanced Character Segmentation for Multi-Language Data Plate in Substation Transformer Based on Connected Component Analysis

Enhanced Character Segmentation for Multi-Language Data Plat...

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international conference on control, automation, robotics and vision (ICARCV)

作者： Jieling Zheng Xiren Miao Shih-Hau Fang Jing Chen Hao Jiang College of Electrical Engineering and Automation Fuzhou University Fuzhou China Department of Electrical Engineering MOST Joint Research Center for AI Technology and All Vista Healthcare Taiwan

Intelligent inspection in the substation transformer using optical character recognizer has been developing rapidly. Character segmentation from the text line of data plate is an important step for localization and recognition of electrical equipment. However, on-site character segmentation is challenging if the data plate contains multiple languages, especially when the width between Chinese and non-Chinese character differs significantly and the complex environments cause the light reflection and fading. this paper proposes a new method, based on analyzing the connected component and Chinese character's structure, to segment characters from multi-language data plate of substations. the proposed method uses the combination of the HSV color space and multi-scale MSRCP to reduce the effect of illumination and complex background. the proposed method utilized the width of each kind character, the interval between characters and the relationship within the left-right structure Chinese character to improve the segmentation accuracy. Experimental results show that the text lines from the data plate in substation transformer, including Chinese, English, Roman numerals, Arabic numerals and symbols, can be segmented correctly. Results show that the proposed method outperforms two existing character segmentation methods and achieves 99.4% precision in the multi-language data plate dataset.

关键词： Image color analysis Substations Image segmentation Lighting Inspection Character recognition Feature extraction

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A Hybrid control Architecture For Autonomous Driving In Urban Environment

A Hybrid Control Architecture For Autonomous Driving In Urba...

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international conference on control, automation, robotics and vision (ICARCV)

作者： Chanyoung Jung Seokwoo Jung David Hyunchul Shim Department of Computer and Information Sciences Northumbria University UK School of Engineering Science and Design Teesside University UK

Autonomous driving in an urban environment is one of the most actively studied topics. To date, many studies on autonomous driving can be classified into two main approaches: *** perception-based approach *** path tracking-based approach. However, each approach has its own limitations for fully autonomous driving. In the case of perception-based approach, it is impossible to autonomously drive to the global destination because it only runs locally within the sensor range. On the other hand, the path tracking-based approach relies heavily on accurate navigation. For accurate navigation, there are many studies using expensive equipment or extremely precise and detailed maps, but they have not been resolved yet, and they are also not practical. In this paper, we address the problem of autonomous driving in an urban environment through the proposed hybrid control architecture. Proposed control architecture is designed to be complementary of local perception-based and global path tracking-based approaches. Especially, end-to-end deep learning based autonomous driving, which mimics human driving, is applied as a local perceptionbased approach. In addition, path tracking-based autonomous driving is performed in an environment where directional information to the destination is required, such as intersections. At the same time, our hybrid control architecture effectively compensates for the navigation error using ICP matching between the perception-based driven trajectory and the global path to the destination without any highly detailed prior map or expensive equipment. the performance of the proposed architecture on a full-scale autonomous vehicle is verified through experiments in the urban environment.

关键词： Autonomous vehicles Navigation Urban areas Iterative closest point algorithm Training Real-time systems

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Level of Detail in Motion Science Associated with Older Adults

Level of Detail in Motion Science Associated with Older Adul...

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international conference on control, automation, robotics and vision (ICARCV)

作者： Shahram Payandeh Networked Robotics and Sensing Laboratory (NR&S-L) Simon Fraser University Burnaby British Columbia CANADA

It is a well-know statistics that the percentage of our elderly population will globally surpass the other age groups. Majority of the elderly would still prefer to keep an active life style. In support of this life style, various monitoring systems are being designed and deployed in order to have a seamless integration with the daily living activities of the elderly while preserving various levels of their privacy. Motion tracking is one of these health monitoring systems. When properly designed, deployed, integrated and analyzed, they can be used to assist in determining some on-sets of anomalies in elderly health at various levels of their movements and activities. In this paper, we propose to divide the overall motion tracking of the elderly into three levels were at each hierarchical level, information regarding movement patterns can be analyzed ranging from global motion patterns to more detailed gait patterns. Depending on the state of frailty of elderly, this hierarchical break-downs can allow the medical personnel and physicians options for deploying sensing system which offers different degree of granularity in motion sensing. these levels are defined as: a) coarse global path of mobility between various key nodal points in the monitoring area; b) the trajectory of motion between each of the key nodal points and c) the gait patterns along the motion trajectories between the key nodal points. In this paper we presents an overview of these levels and associated tools which can be utilized in order to further explore and define motion patterns that can be utilized by data scientists and medical personnel as a part of the prevention analysis.

关键词： Senior citizens Monitoring Markov processes Probability distribution Robot sensing systems Tracking

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Research on Command Confirmation Unit Based on Motor Imagery EEG Signal Decoding Feedback in Brain-Computer Interface

Research on Command Confirmation Unit Based on Motor Imagery...

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international conference on control, automation, robotics and vision

作者： Yue Zhang Weihai Chen Chun-Liang Lin Junsheng Chu Fangang Meng School of Automation and Electrical Engineering National Chung Hsing University of Taiwan School of Automation and Electrical Engineering Beihang University Beijing China Department of Electrical Engineering National Chung Hsing University Taichuang Taiwan Department of Neurosurgery Capital Medical University Beijing China

the brain-computer interface (BCI) technology is a new human-machine interaction technology that realizes people to control external devices directly by thinking (i.e. electroencephalogram, EEG). However, because of the weakness and randomness of EEG signal, it is very complicated and difficult to process and identify the EEG signal recorded by the non-invasive BCI, and the decoding error often occurs. In view of the brain electrical signal decoding error, an experimental paradigm for simultaneous acquisition of spontaneous EEG and evoked EEG was designed, where the subjects generated the error-related potentials (ErrP) based on the decoded feedback of motor imagery EEG. We analyzed the EEG signal two times. the motor imagery EEG, which was the component of the EEG signal, was analyzed at the first time analysis. We classified the motor imagery EEG signal of left and right hand, then analyzed the classification method quantitatively using the Receiver Operating Characteristic (ROC) curves and the area under the curve (AVC). Although the EEG signal were influenced greatly by the individual difference, the AVC values can still reach more than 0.7. Meanwhile, the frequency domain characteristics were analyzed. the activation brain regions of the left-right hand motor imagery are mainly concentrated in the area of the perceptual motor cortex where is responsible for hand motion, but they will also be influenced by the artifacts of the surrounding channels. In the second time analysis, the ErrP was extracted and discussed. Its latency, waveform and amplitude characteristics were studied in the time domain and then a suitable classifier is selected by comparing a variety of classifiers, which classification accuracy is up to 90%. therefore, the research based on the ErrP signal played a theoretical foundation for applying to the lower limb exoskeleton rehabilitation robot in the future, and ensured the feasibility of applying the command confirmation unit based on ErrP si

关键词： conferences automation Robots

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A Novel control Strategy of Energy Storage System Considering Prediction Errors of Photovoltaic Power

A Novel Control Strategy of Energy Storage System Considerin...

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international conference on control, automation, robotics and vision (ICARCV)

作者： Fang Liu Bo Hu Ranran Li Yong Li School of Information Science and Engineering Central South University Changsha Global Energy Interconnection Development and Coonertaion Orzanization Beijing China College of Electrical and Information Engineering Hunan University Changsha China

the photovoltaic (PV) energy, as clean and renewable energy, has become increasingly important. With energy storage system, the PV power can become schedulable and the use efficiency of PV power can be greatly improved. the tracking output is one of the running modes for energy storage to adjust the PV power generation. However, the research on the tracking output do not consider the fluctuation of PV power generation, the upper and lower limits of prediction errors, or only consider tracking the power curve. In this paper, a novel control method is proposed based on the super short-term prediction PV power and the prediction errors distribution law to obtain the charge and discharge strategy for energy storage ahead. In this control method, the objective function includes three parts, respectively the D-value between the planned output and actual output of PV power and energy storage system, the change of the state of charge (SOC) with different weight, the constraint condition considers the power demand, flexibility requirement and storage costs comprehensive. then the prediction errors distribution law can be obtained by analyzing prediction errors of the PV power, the proper errors correction parameters can be selected according to the law to get a more accurate predication range and make the energy storage scheduling strategy more flexible. the particle swarm optimization (PSO) algorithm is used to get the optimal charge and discharge power strategy in advance. With the data collected from typical PV power station as an example, the simulation results verify the feasibility and flexibility of the proposed strategy, it also provides effective reference scheme for day-ahead control of battery energy storage systems.

关键词： Forecasting Power generation Energy storage Discharges (electric) State of charge Computer aided software engineering Fluctuations

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Driving-PASS: An Automatic Driving Performance Assessment System for Stroke Drivers Based on ANN and SVM

Driving-PASS: An Automatic Driving Performance Assessment Sy...

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international conference on control, automation, robotics and vision

作者： Sanghoon Jeon Joonwoo Son Myoungouk Park Bawul Kim Sang Hyuk Son Yongsoon Eun Department of Information and Communication Engineering DGIST Republic of Korea HumanLab DGIST Republic of Korea

Although many stroke survivors are not fully capable of driving, they drive again without any formal assessment due to an absence of valid screening tools. this leads to an elevated risk of accidents. Although an on-road test is considered a standard assessment method for items relevant to actual driving, it may be dangerous to evaluate all stroke drivers with the on-road test. For safe pre-screening of unsuitable stroke drivers, we propose an automatic Driving Performance Assessment System for Stroke drivers (Driving-PASS). Driving-PASS aims to provide not only information about problematic driving assessment items but also a decision about fitness to drive. the problematic driving items are classified by abnormal classifiers while the decision item is determined by a decision classifier in Driving-PASS. For designing the system, we firstly propose a subjective assessment method consisting of ten assessment items and one decision item. And then, we propose an automated method of the subjective assessment method with a machine learning approach (i.e., ANN and SVM) by using assessment criteria from five expert's judgments. Evaluation results demonstrate that Driving-PASS automatically assess not only the ten assessment items (total average Accuracy of 90% and F1-score of 88%) but also the decision item (Accuracy of 93% and F1-score of 92%). We expect Driving-PASS provides analytical assessment results that can be used in driving rehabilitation programs and contributes to reducing the risk of vehicle accidents by pre-screening unsuitable stroke drivers with high accuracy and reliability.

关键词： Vehicles Velocity control Machine learning Measurement Standards Road transportation

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A Neuro-Inspired Approach to Solve a Simultaneous Location and Mapping Task Using Shared Information in Multiple Robots Systems

A Neuro-Inspired Approach to Solve a Simultaneous Location a...

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international conference on control, automation, robotics and vision

作者： Matheus Chaves Menezes Edison Pignaton de Freitas Sen Cheng Alexandre Cesar Muniz de Oliveira Paulo Rogerio de Almeida Ribeiro Graduate Program on Computer Science Federal University of Maranhão São Luís Brazil Graduate Program on Computer Science Federal University of Rio Grande do Sul Porto Alegre Brazil Computational Neuroscience Ruhr University Bochum Bochum Germany Department of Informatics Federal University of Maranhão São Luís Maranhão Department of Computer Engineering Federal University of Maranhão São Luís Maranhão

this paper presents a neuro-inspired mapping approach that uses partial information shared by multiple robots to reduce the time to create a map of an entire environment. Robots using a neurobiologically inspired algorithm, namely RatSLAM, map an environment sharing video information among themselves. RatSLAM, which is based on the navigation system present in the hippocampus of rodents' brain, has been widely used on simultaneous localization and mapping (SLAM) problem. this proposal has been able to generate suitable maps mainly when there is redundant information, e.g. a scene is seen more than once, since this fact activates local view cells that inject activity inside the pose cells via an excitatory link. the work here reported extends this approach by merging partial information acquired by multiple robots. the results from the performed experiments show that the final map built by two robots with shared information is similar to one built by two robots performing the same mapping task individually, i.e. without sharing information. However, the time spent to generate the whole map with the proposed shared approach was smaller than the one without the shared information. thus, the current approach allows creating a complete map of an environment within a reduced time using multiple robots.

关键词： Navigation Simultaneous localization and mapping Feature extraction Robot vision systems Task analysis Hippocampus

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