Vehicular consumer electronics, such as autonomous vehicles (AVs), need collecting large amounts of private user information, which face the risk of privacy leakage. To protect the privacy of consumers, researchers ha...
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computerscience developments in medical data processing increase modeling accuracy, speed up data analysis, and obtain automated diagnostics. As a result, the role of machine learning algorithms becomes significant, ...
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This paper proposes a RISC-V extension, named SigWavy, meant to optimize the PWM control for general purpose or application specific designs. The RISC-V extension named above is a PWM control Unit with a dedicated ISA...
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The paper describes the energy consumption from the battery based on the current measurements for various cases, i.e., speed (PWL adjustment) and loads. The main purpose of the research is to have additional and relia...
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Unmanned Aerial Vehicles (UAVs) have extensive applications such as logistics transportation and aerial photography. However, UAVs are sensitive to winds. Traditional control methods, such as proportional- integral-de...
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Unmanned Aerial Vehicles (UAVs) have extensive applications such as logistics transportation and aerial photography. However, UAVs are sensitive to winds. Traditional control methods, such as proportional- integral-derivative controllers, generally fail to work well when the strength and direction of winds are changing frequently. In this work deep reinforcement learning algorithms are combined with a domain randomization method to learn robust wind-resistant hovering policies. A novel reward function is designed to guide learning. This reward function uses a constant reward to maintain a continuous flight of a UAV as well as a weight of the horizontal distance error to ensure the stability of the UAV at altitude. A five-dimensional representation of actions instead of the traditional four dimensions is designed to strengthen the coordination of wings of a UAV. We theoretically explain the rationality of our reward function based on the theories of Q-learning and reward shaping. Experiments in the simulation and real-world application both illustrate the effectiveness of our method. To the best of our knowledge, it is the first paper to use reinforcement learning and domain randomization to explore the problem of robust wind-resistant hovering control of quadrotor UAVs, providing a new way for the study of wind-resistant hovering and flying of UAVs. IEEE
This paper proposes a framework for real-time monitoring of the power consumption of distributed calculation on the nodes of the cluster. The framework allows to visualize and analyze the provider results based on the...
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Lidar and visual data are affected heavily in adverse weather conditions due to sensing mechanisms,which bring potential safety hazards for vehicle *** sensing is desirable to build a more robust navigation *** this p...
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Lidar and visual data are affected heavily in adverse weather conditions due to sensing mechanisms,which bring potential safety hazards for vehicle *** sensing is desirable to build a more robust navigation *** this paper,a cross-modality radar localisation on prior lidar maps is ***,the proposed workflow consists of two parts:first,bird's-eye-view radar images are transferred to fake lidar images by training a generative adversarial network *** with online radar scans,a Monte Carlo localisation framework is built to track the robot pose on lidar *** whole online localisation system only needs a rotating radar sensor and a pre-built global lidar *** the experimental section,the authors conduct an ablation study on image settings and test the proposed system on Oxford Radar Robot Car *** promising results show that the proposed localisation system could track the robot pose successfully,thus demonstrating the feasibility of radar style transfer for metric robot localisation on lidar maps.
The superior performance of object detectors is often established under the condition that the test samples are in the same distribution as the training data. However, in many practical applications, out-of-distributi...
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Multi-object tracking (MOT) is one of the most important problems in computer vision and a key component of any vision-based perception system used in advanced autonomous mobile robotics. Therefore, its implementation...
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A graph with m vertices in each of its d sections is a d-partite graph. In sections of a d-partite graph, the vertices are not connected by any edges. Each pair of vertices from different sections is always connected ...
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