For quadrotors, imposing multiple dynamic constraints on the state simultaneously to achieve safe control is a challenging problem. In this paper, a cascaded control archi-tecture based on quadratic programming method...
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The perception capability of robotic systems relies on the richness of the dataset. Although Segment Anything Model 2 (SAM2), trained on large datasets, demonstrates strong perception potential in perception tasks, it...
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Transmission lines are a critical component of an electrical grid, and ensuring their effective detection is of utmost importance. However, problems such as fine-grained identification, target occlusion, and backgroun...
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Aiming at the charging and navigation strategy of electric vehicles in the road-electricity coupling scenario, this paper proposes a hybrid planning travel scheme based on the pre-charging and charging warning model b...
Aiming at the charging and navigation strategy of electric vehicles in the road-electricity coupling scenario, this paper proposes a hybrid planning travel scheme based on the pre-charging and charging warning model based on the road traffic congestion predicted by the big data of the Internet of Vehicles. This model integrally balances the interests of users and power grid in the coupled road-electric network, and solves the optimal path scheme under different travel scenarios based on collaborative filtering algorithm and user feedback, considering the influence of vehicle departure time and remaining power in short time scale. It is experimentally demonstrated that the proposed route hybrid recommendation model has better planning effect compared with the single travel model.
Data centers are the key infrastructure for information services. The monitoring of the thermal environment of the data center computer room is an important work for its safe operation. This paper proposed a mobile ro...
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The quantity forecast of incoming dustcarts in the waste transfer station is essential to enhancing the operational efficiency of smart sanitation, because it is helpful for the station management and the planning of ...
The quantity forecast of incoming dustcarts in the waste transfer station is essential to enhancing the operational efficiency of smart sanitation, because it is helpful for the station management and the planning of resources. In this study, a seasonal autoregressive integrated moving average (SARIMA) model is suggested to forecast the incoming dustcarts of a waste transfer station. The dataset utilized contains both the hourly-sampled quantity and proportion of residual waste dustcarts. The outcomes of single step and multi-step forecasting are examined with different performance measures in order to confirm SARIMA’s effectiveness. The experimental results show that the SARIMA model has better prediction results compared with the LSTM model. In addition, SARIMA model has high accuracy in both single step and multi-step forecasting, but multi-step forecasting is more effective for solving real-world issues because of its less time-consuming.
visual localization is considered an essential capability in robotics and has attracted increasing interest for the past few years. However, most proposed visual localization systems assume that the surrounding enviro...
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visual localization is considered an essential capability in robotics and has attracted increasing interest for the past few years. However, most proposed visual localization systems assume that the surrounding environment is static, which is difficult to maintain in real-world scenarios due to the presence of moving objects. In this paper, we present DFR-SLAM, a real-time and accurate RGB-D SLAM based on ORB-SLAM2 that achieves satisfactory performance in a variety of challenging dynamic scenarios. At the core of our system lies a motion consensus filtering algorithm estimating the initial camera pose and a graph-cut optimization framework combining long-term observations, prior information, and spatial coherence to jointly distinguish dynamic and static visual features. Other systems for dynamic environments detect dynamic components by using the information from short time-span frames, whereas our system uses observations from a long period of keyframes. We evaluate our system using dynamic sequences from the public TUM dataset, and the evaluation demonstrates that the proposed system outperforms the original ORB-SLAM2 system significantly. In addition, our system provides competitive localization accuracy with satisfactory real-time performance compared to closely related SLAM systems designed to adapt to dynamic environments.
Humanoid robots are the culmination of robotics technology and play an important role in serving the major strategic needs of the country. They can assist or replace humans in dangerous, dirty, and repetitive environm...
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Deception attacks are employed to compromise cyber-physical systems through fake data injection. This paper concentrates on the distributed resilient estimation issue of multi-sensor networked systems under deception ...
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ISBN:
(数字)9798331518493
ISBN:
(纸本)9798331518509
Deception attacks are employed to compromise cyber-physical systems through fake data injection. This paper concentrates on the distributed resilient estimation issue of multi-sensor networked systems under deception attacks. In order to detect deception attacks, we utilize Kullback-Leibler(K-L) divergence as a criterion to distinguish the discrepancy between the deceived information and the estimated information. When the attack does not exist, the transmitted information can be restored to ensure the resilient estimation performance. Based on the extended Kalman filter design method, a distributed resilient estimation with a dual-gain mechanism is developed. This advanced approach dynamically adjusts the weighting balance between the predictive model and sensor data inputs, achieving the optimal estimation during the shutdown and activation of spoofing attacks. Finally, numerical simulations are provided to further illustrate the results.
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