Comparative Study Methods of Trajectory Tracking Control for Robot Manipulator (Dept. E  )

Hassan, Rezaei Soleymanpour; Bendary, Fahmy; Elserafi, Kamel Ahmed; Ghanem, A; Soliman, M.

doi:10.21608/bfemu.2020.101868

Cited by 2 publications

(2 citation statements)

References 8 publications

(11 reference statements)

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“…Then, the trajectory error information is obtained, and the error is substituted into the control law for calculation, and the iteration coefficient and compensation torque of the first iteration process are obtained. It is then transmitted to the controller, which operates the robot for a second movement [28]. Repeating the above operations continuously can obtain the error data of each movement, as shown in Figure 7.…”

Section: Displacement Trajectory Tracking Control Experimentmentioning

confidence: 99%

Artificial Intelligence in Agricultural Picking Robot Displacement Trajectory Tracking Control Algorithm

Hua-ying

2022

Wireless Communications and Mobile Computing

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With the development and leap of artificial intelligence technology, more and more robots have penetrated into all walks of life. Today’s agriculture is changing in the direction of modernization and automation. On the one hand, because of rising labor costs, it cannot afford to consume a large amount of labor for agricultural operations. On the other hand, the population is growing rapidly, and traditional agricultural production, picking, and other links have been unable to keep pace with the development of the times. Therefore, it is very necessary to use artificial intelligence technology to transform traditional agriculture. The purpose of this paper is to use artificial intelligence technology to plan, track, and optimize the displacement trajectory of the agricultural picking robot, so as to improve the working efficiency of the picking robot. In this paper, the neural network, the D-H modeling method of the manipulator, and the forward and reverse motion of the manipulator are explained and analyzed, and based on the relevant algorithms of neural network, the manipulator is modeled, and then the, forward and reverse motion of the manipulator is analyzed in detail, and the digital model of the picking robot is constructed. Then, the angle and motion speed of each joint of the robot are analyzed to reduce the motion trajectory error caused by friction and other factors. Then, the simulation experiment of the displacement trajectory tracking control is carried out, and the linear trajectory motion and the arc trajectory motion are deeply analyzed, and the axis error is greatly reduced after 6 iterations. Finally, the displacement trajectory is optimized. The optimized total movement time is shortened by 6.84 seconds, which enables the picking robot to not only ensure work efficiency but also accurately complete the planned displacement trajectory. After continuous experiments on the algorithm model and the picking robot, the actual trajectory of the picking robot at 0.7 seconds can be expected. The trajectories are completely coincident, indicating that the neural network plays a very important role in the trajectory research of picking robots.

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Section: Displacement Trajectory Tracking Control Experimentmentioning

confidence: 99%

Artificial Intelligence in Agricultural Picking Robot Displacement Trajectory Tracking Control Algorithm

Hua-ying

2022

Wireless Communications and Mobile Computing

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“…Using drones for spraying can reduce the labor pressure on farmers and improve the comfort and safety of their work (Hasan S. F. et al, 2020). These drones are equipped with advanced navigation and sensing technologies, allowing for precise spraying of field crops (Hassan R. et al, 2020). By controlling flight height, speed, and liquid dosage reasonably, the amount of pesticide used can be effectively reduced, waste can be minimized, and spraying accuracy can be improved (Huang Y. et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Agricultural Uav Crop Spraying Path Planning Based on Pso-A* Algorithm

FAN

2023

INMATEH

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Currently, drones have been gradually applied in the field of agriculture, and have been widely used in various types of agricultural aerial operations such as precision sowing, pesticide spraying, and vegetation detection. The use of agricultural UAVs for pesticide spraying has become an important task in the agricultural plant protection process. However, in the crop spraying process of agricultural UAVs, it is necessary to traverse multiple spray points and plan obstacle avoidance paths, which greatly affects the efficiency of agricultural UAV crop spraying operations. To address the above issues, traditional particle swarm optimization (PSO) algorithms have strong solving capabilities, but they are prone to falling into local optima. Therefore, this study proposes an improved PSO algorithm combined with the A* algorithm, which introduces a nonlinear convergence factor balancing algorithm for global search and local development capabilities in the traditional PSO algorithm, and adopts population initialization to enhance population diversity, so that the improved PSO algorithm has stronger model solving capabilities. This study designs two scenarios for agricultural UAV crop spraying path planning: one without obstacles and one with obstacles. Experimental simulation results show that using the PSO algorithm to solve the obstacle-free problem and then using the A* algorithm to correct the path obstructed by obstacles in the obstacle scenario, the agricultural UAV crop spraying trajectory planning based on the PSO-A* algorithm is real and effective. This research can provide theoretical basis for agricultural plant protection and solve the premise of autonomous operation of UAVs.

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Comparative Study Methods of Trajectory Tracking Control for Robot Manipulator (Dept. E )

Cited by 2 publications

References 8 publications

Artificial Intelligence in Agricultural Picking Robot Displacement Trajectory Tracking Control Algorithm

Artificial Intelligence in Agricultural Picking Robot Displacement Trajectory Tracking Control Algorithm

Agricultural Uav Crop Spraying Path Planning Based on Pso-A* Algorithm

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