Unmanned Aerial Vehicles (UAVs) pose a multi-input and multi-output (MIMO) dynamic structure, making their simultaneous guidance and control too complicated to be maintained via conventional scalar controllers. In thi...
详细信息
ISBN:
(纸本)9781467372350
Unmanned Aerial Vehicles (UAVs) pose a multi-input and multi-output (MIMO) dynamic structure, making their simultaneous guidance and control too complicated to be maintained via conventional scalar controllers. In this paper, a multivariable optimal controller is introduced based upon LQG LTR design approach to effectively control the UAV attitude in the presence of noise and disturbance. The regulator design problem is solved by generating an optimal state estimate using a Kalman filter. A loop transfer recovery (LTR) procedure is developed to allow good recovery of the full state feedback properties, enhancing stability and performance robustness. This scheme facilitates proper integration of system's gain at different frequencies in order to provide optimal bandwidth and yet weakening the noise effects. The corresponding rate of return gains is set in frequency-domain to achieve robust performance characteristics. A set of tests is conducted on an UAV simulation case study to explore its performance under different scenarios. The results clearly demonstrate well performances in the face of the induced noise and couplings between the system channels.
As of October 2023, the number of new energy vehicles in China has surpassed 18 million. The surge in electrical vehicle (EV) numbers has led to significant adverse impacts on the power grid due to the charging load t...
详细信息
ISBN:
(数字)9798350375138
ISBN:
(纸本)9798350375145
As of October 2023, the number of new energy vehicles in China has surpassed 18 million. The surge in electrical vehicle (EV) numbers has led to significant adverse impacts on the power grid due to the charging load they create. Unorderly large-scale charging of EVs, in particular, exacerbates the problem by adding additional peaks to the existing demand peaks, severely affecting the stability of the power system. Therefore, it holds paramount significance in guiding EVs toward orderly charging. This paper commences by analyzing the impact of unorderly EV charging on the power grid, culminating in an orderly charging strategy. This strategy clearly defines the expected outcomes through an objective function and ensures feasibility and effectiveness through constraint conditions. The orderly charging strategy undergoes optimization with the aid of an enhanced Whale Optimization Algorithm (WOA), followed by corresponding case studies. This paper considers multiple objectives and constraints in the construction of the orderly charging strategy and effectively optimizes the strategy using an enhanced optimization algorithm, thereby facilitating orderly charging for EV users and reducing fluctuations in grid load.
Measuring linear and angular displacement with high accuracy requires expensive equipment and may interfere with the measurements due to sensors weight and size. In this paper, we propose a low cost vision based appro...
详细信息
ISBN:
(纸本)9781467372350
Measuring linear and angular displacement with high accuracy requires expensive equipment and may interfere with the measurements due to sensors weight and size. In this paper, we propose a low cost vision based approach for measuring linear & angular displacement that can make the earthquake research more *** keep detecting process as fast as possible and meet real-time demands, a typical marker with a peculiar color inside chooses and all instructions performed by Intel CVLib filters. So according to the tests, if the minimum requirement are met, the proposed method can detect marker up to 25 frames per second. Finally, a prototype software implemented to analysis functionality and accuracy of proposed algorithm. The results shown that this vision-based method can achieve accuracy of 0.1mm, with an off-the-shelf three pixel per millimeter, and it can be perfectly used instead of traditional instruments such as LVDT.
Intelligent robotic systems are becoming fundamental actors in industrial and hazardous facilities scenarios. Aiming to increase personnel safety and machine availability, robots can help perform repetitive and danger...
详细信息
ISBN:
(纸本)9781450352802
Intelligent robotic systems are becoming fundamental actors in industrial and hazardous facilities scenarios. Aiming to increase personnel safety and machine availability, robots can help perform repetitive and dangerous tasks which humans either prefer to avoid or are unable to do because of hazards, space constraints or the extreme environments in which they take place, such as outer space or radioactive experimental areas. Teleoperated robots need user friendly and safety tools to be safely operated in harsh environments where the intervention scenarios are unstructured and most of the time dangerous for human intervention. In many robotic interventions in harsh environments, a dual arms robotic system is needed to perform difficult task such as cutting, drilling etc. To ensure the safety of the robotic system and the machines to be tele-manipulated, as well as increasing the uptime of the plants, a real-time reconfigurable self-collision avoidance system coupled to a virtual augmented reality scenario is fundamental to help the operator during the intervention. In addition, it is important to provide to the operator a uniform control system, in order to not create confusion when several operations are performed using different robotic platforms. For this reason, it is vital that the self-collision avoidance system is adaptable to the current robot hardware and software configurations. In this paper, a novel reconfigurable collision avoidance system for robot manipulation running in real time is presented. The novelty of the proposed solution is the capability to be adaptable to different robots configuration and installation taking into account different parameters like the type and the number of robotic arms, as well as their orientation. The novel system is able to avoid collision not only within the robot itself, but it can avoid collision also with external unexpected objects. The structure of the novel solution is presented, as well as its validation in the
In recent years, many researchers have shown their interest on the dynamic modeling and control of two-wheeled mobile robots. In this concern, designing the self-balancing robots and reducing undesired vibrations are ...
详细信息
ISBN:
(纸本)9781467372350
In recent years, many researchers have shown their interest on the dynamic modeling and control of two-wheeled mobile robots. In this concern, designing the self-balancing robots and reducing undesired vibrations are of great importance. For this purpose, the majority of publications are focused on application of relatively complex control approaches without improving the robot structure. Therefore, in this paper we introduce a new two-wheeled mobile robot which, despite its relative simple structure, fulfills the required level of self-balancing without applying any certain complex controller. To reach this goal, the robot structure is designed in way that its center of gravity to be located below the wheels' axle level. In the final model of the robot, the attention is more paid to obtaining a self-balancing model in which the robot's arms and other equipment follow relatively low oscillations when the robot is subjected to a sudden change. For this purpose, after assembling the robot using the Sim-Mechanics toolbox of Matlab, several simulations are arranged to investigate the robot ability in fulfilling the required tasks. Further verifications are carried out by performing various experiments on the real model. Based on the obtained results, an acceptable level of balancing, oscillation reduction, and power supply is observed.
Abstact: Deep learning is widely used in intelligent picking, but the adverse effects of different environmental scenes on target detection and recognition are crucial to picking robots’ accurate and efficient work. ...
详细信息
ISBN:
(纸本)9781450385862
Abstact: Deep learning is widely used in intelligent picking, but the adverse effects of different environmental scenes on target detection and recognition are crucial to picking robots’ accurate and efficient work. First, the data set needed for the experiment was manually created. The data set selected 925 navel orange images, including 290 backlit sunny days, 310 forward light, and 325 cloudy days. The training and test sets were divided into 8:2. Then, we studied the detection of navel orange based on the improved model of single-stage target detection network PP-YOLO. Used the backbone network ResNet with deformable convolution to extract image features and combined with FPN (feature pyramid network) for feature fusion to achieve multi-scale detection. The K-means clustering algorithm clustered the appropriate Anchor size for the target navel orange, which reduced the training time and the confidence error of the prediction frame. Loaded the pre-trained model and compared the model performance with the original PP-YOLO, YOLO-v4, YOLO-v3, and Faster RCNN network. Analyzed the Loss curve and AP curve of the training log, the task of detecting navel oranges under sunny, sunny, and cloudy conditions was realized. Finally, the improved PP-YOLO detection accuracy was 90.81%, 92.46%, and 94.31%, and the recognition efficiency reached 72.3 fps, 73.71 fps, and 74.9 fps, respectively. The model performance is better than the other four, with better *** CONCEPTS • Computing methodologies∼Artificial intelligence∼Computer vision∼Computer vision tasks∼Vision for robotics
Detecting anomalies in satellite telemetry data is pivotal in ensuring its safe operations. Although there exist various data-driven techniques for the task of determining abnormal parts of the signal, they are virtua...
详细信息
暂无评论