Efficient Intelligent detection is a key technology in automatic harvesting robots. However, citrus detection is still a challenging task because of varying illumination, random occlusion and colour similarity between...
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Efficient Intelligent detection is a key technology in automatic harvesting robots. However, citrus detection is still a challenging task because of varying illumination, random occlusion and colour similarity between fruits and leaves in natural conditions. In this paper, a detection method called Lemon-YOLO (L-YOLO) is proposed to improve the accuracy and real-time performance of lemon detection in the natural environment. The SE_ResGNet34 network is designed to replace DarkNet53 network in YOLOv3 algorithm as a new backbone of feature extraction. It can enhance the propagation of features, and needs less parameter, which helps to achieve higher accuracy and speed. Moreover, the SE_ResNet module is added to the detection block, to improve the quality of representations produced from the network by strengthening the convolutional features of channels. The experimental results show that the proposed L-YOLO has an average accuracy(AP) of 96.28% and a detection speed of 106 frames per second (FPS) on the lemon test set, which is 5.68% and 28 FPS higher than the YOLOv3, respectively. The results indicate that the L-YOLO method has superior detection performance. It can recognize and locate lemons in the natural environment more efficiently, providing technical support for the machine's picking lemon and other fruits.
The proper sizing of the electronic target to implement complex controllers is barely tackled at engineering schools. An incorrect selection leads, at best, to inefficient use of resources and unnecessarily high costs...
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The proper sizing of the electronic target to implement complex controllers is barely tackled at engineering schools. An incorrect selection leads, at best, to inefficient use of resources and unnecessarily high costs. In this work, the criteria to implement the electronic Lyapunov-based controller for non-linear trajectory tracking of a P3-DX robot is focused on. After comparing pros and cons of the four alternatives, test results are shown with two of them: NUC8i5INH microcomputer and STM32F767 single chip board.
As deep neural networks are spreading to almost all fields, flight systems in the unmanned aerial vehicle (UAV) domain are undergoing various transitions to intelligent systems. Among these transitions-in a bid to red...
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As deep neural networks are spreading to almost all fields, flight systems in the unmanned aerial vehicle (UAV) domain are undergoing various transitions to intelligent systems. Among these transitions-in a bid to reduce flight risk-is the active research domain of autonomous navigation for intelligent UAVs. The autonomous trail-following flight system that this letter introduces can safely consolidate flight control and mission control within the latest commercial hardware platform. The resource usage and degradation of pass-through delay in vision-based convolutional neural network workloads show that virtualisation overhead is not significantly negative, and the overall performance of the introduced system is acceptable. Real-time cooperation is also verified as achievable-in that the workloads incur minimal communication delay-between the controls. Finally, the actual field test analysis demonstrates the applicability of our autonomous UAV system, whereby our system controls the UAV to follow the centre of a set trail.
Vehicular named data network (VNDN) is the next-generation network architecture for intelligent transportation system. Contrary to the conventional transmission control protocol/internet protocol (TCP/IP) communicatio...
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Vehicular named data network (VNDN) is the next-generation network architecture for intelligent transportation system. Contrary to the conventional transmission control protocol/internet protocol (TCP/IP) communication model, VNDN follows a data-centric approach where the user is interested in 'WHAT' instead of 'WHERE'. Interest flooding attack (IFA) is one of the prominent security concerns in VNDNs. In IFA, attackers request for non-existent content to exhaust network resources and cause Interest packet flooding across the network. A novel attack mitigation scheme to counter IFA in VNDN has been proposed in this study. The proposed priority-based per-flow Interest rate monitoring (PP-FIRM) scheme determines the suspicious flow of malicious incoming Interest packets in attacked vehicles. A priority flag is assigned to the incoming flow of Interest packets that detects the occurrence of an attack. The priority of incoming Interest packet flow is calculated using a collaborative or neighbour-assisted approach. A comparison with another attack mitigation scheme validates that the proposed scheme performs better in terms of an improved cache hit ratio and Interest satisfaction ratio during the attack window. Besides this, pending Interest table utilisation, packet collisions rate, Interest packets retransmission count, end-to-end delay, and the ratio of timed out Interest packets have also been reduced. Furthermore, the scalability of the proposed research strategy is also evaluated by changing the density of attackers in real time. Moreover, in the proposed attack mitigation model, the rate of incoming legitimate Interest packets increases by reducing the drop rate of valid Interest packets.
Ensuring that safety-critical cyber-physical systems (CPSs) continue to satisfy correctness and safety specifications even under faults or adversarial attacks is very challenging, especially in the presence of legacy ...
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Ensuring that safety-critical cyber-physical systems (CPSs) continue to satisfy correctness and safety specifications even under faults or adversarial attacks is very challenging, especially in the presence of legacy components for which accurate models are unknown to the designer. Current techniques for secure-by-design systems engineering do not provide an end-to-end methodology for a designer to provide real-time assurance for safety-critical CPSs by identifying system dynamics and updating control strategies in response to newly discovered faults, attacks or other changes such as system upgrades. We propose a new methodology, along with an integrated framework implemented in MATLAB to guarantee the resilient operation of safety-critical CPSs with unknown dynamics. The proposed framework consists of three main components. The runtime monitor evaluates the system behaviour on-the-fly against its correctness specifications expressed as signal temporal logic formulas. The model synthesiser incorporates a sparse identification approach that is used to continually update the plant model and control policies to adapt to any changes in the system or the environment. The decision and control module designs a controller to ensure that the correctness specifications are satisfied at runtime. For evaluation, we apply our proposed framework to ensure the resilient operations of two CPS case studies.
ROBOHOOD Inc, an art and tech start-up specialising in artificial intelligence and robotics, says it has created the world's first AI-robotic technology that enables users to create physical paintings from an idea...
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ROBOHOOD Inc, an art and tech start-up specialising in artificial intelligence and robotics, says it has created the world's first AI-robotic technology that enables users to create physical paintings from an idea to canvas without human involvement.
Traditional constant admittance controller (CAC) in uncertain contact environment is highly dependent on environmental location and stiffness, which are always difficult to obtain in most applications. To address this...
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Traditional constant admittance controller (CAC) in uncertain contact environment is highly dependent on environmental location and stiffness, which are always difficult to obtain in most applications. To address this problem, a fuzzy adaptive admittance controller (FAAC) based on fuzzy inference rules is proposed in this paper. Firstly, an adaptive admittance controller (AAC) that can compensate for the admittance parameters in real time based on force error information is proposed. Secondly, a fuzzy logic loop is introduced into the adaptive term of the AAC system to reduce the overshoot when force tracking, thus creating the FAAC. The detailed design method of FAAC is presented, and the stability conditions are analysed. Finally, simulations and experiments are conducted in several dynamic environments to demonstrate the force tracking performance of the proposed controller.
Automation is everywhere in the automobile industry. Ethical dilemmas pose a major challenge while designing such systems. It becomes imperative to discuss these dilemmas, thereby helping the software architects to de...
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Automation is everywhere in the automobile industry. Ethical dilemmas pose a major challenge while designing such systems. It becomes imperative to discuss these dilemmas, thereby helping the software architects to design safe, secure, and reliable software for autonomous vehicles.
Recently, the global concern for protection coordination is growing with the impact of distributed generators connected to medium voltage distribution system. Effective protection strategies need to be developed in or...
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Recently, the global concern for protection coordination is growing with the impact of distributed generators connected to medium voltage distribution system. Effective protection strategies need to be developed in order to avoid undesirable tripping when distributed generators based on Voltage Source Inverters are connected to the medium voltage grid. According to Spanish grid code requirements, the inverter controller response in this paper is assessed under grid faults integrating low voltage ride through capabilities. This paper presents a novel use of a communication-based directional relay system with artificial neural network, an appropriate option for smart grids protection. A protection strategy is proposed using two algorithms. The first algorithm is based on gathering data of all the protective devices in the grid and send it to a centralized controller. The second algorithm is based on a zone controller using the communication between the peer protection devices in the same line. One of the main advantages of the zone controller is that no need to modify the protection devices setting in case of temporary grid reconfigurations. The behaviour of the protection algorithms is validated through both simulations in MATLAB-Simulink and experimental results.
Global optical flow estimation is the foundation stone for obtaining odometry which is used to enable aerial robot navigation. However, such a method has to be of low latency and high robustness whilst also respecting...
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Global optical flow estimation is the foundation stone for obtaining odometry which is used to enable aerial robot navigation. However, such a method has to be of low latency and high robustness whilst also respecting the size, weight, area and power (SWAP) constraints of the robot. A combination of cameras coupled with inertial measurement units (IMUs) has proven to be the best combination in order to obtain such low latency odometry on resource-constrained aerial robots. Recently, deep learning approaches for visual inertial fusion have gained momentum due to their high accuracy and robustness. However, an equally noteworthy benefit for robotics of these techniques are their inherent scalability (adaptation to different sized aerial robots) and unification (same method works on different sized aerial robots). To this end, we present a deep learning approach called PRGFlow for obtaining global optical flow and then loosely fuse it with an IMU for full 6-DoF (Degrees of Freedom) relative pose estimation (which is then integrated to obtain odometry). The network is evaluated on the MSCOCO dataset and the dead-reckoned odometry on multiple real-flight trajectories without any fine-tuning or re-training. A detailed benchmark comparing different network architectures and loss functions to enable scalability is also presented. It is shown that the method outperforms classical feature matching methods by 2x under noisy data. The supplementary material and code can be found at .
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