ACC (Adaptive Cruise Control) is a crucial technology to control the automatic driving of a vehicle, which can automatically adjust the driving status of the vehicle according to the driving status of the vehicle in front so that the vehicle always keeps a safe distance from the vehicle in front, which can primarily relieve the driver's driving pressure and avoid tailgating. The research is of great significance to the realization of intelligent driving of vehicles. In this paper, the fuzzy PID control method is used to study the vehicle ACC system. Firstly, the basic concept and key components of the ACC system are explained, the vehicle longitudinal dynamics model is established, and the primary ranges of , and parameters are determined through simulation; based on this, the fuzzy PID control algorithm of vehicle ACC is designed considering three different driving conditions of fixed speed and following cruise mode. The fuzzy PID control algorithm of the vehicle ACC system is designed and established. The joint simulation model of MATLAB is used to simulate the vehicle ACC system under three different driving conditions. The simulation results show that the vehicle ACC system designed in this paper ensures the safety and stability of following the vehicle while providing the vehicle's ride's comfort.
In the conventional Proportional Integral Derivation (PID) controller, the parameters are often adjusted according to the formulas and actual application. However, this empirical method will bring two disadvantages. First, testing the program takes much time and usually needs help to reach the optimal solution. Second, the PID parameters will not adapt to the new environment when the situation changes. This paper proposed a method by employing a Block Particles Swarm Optimization (BPSO) to enhance the conventional Proportional Integral Derivation (PID) algorithm to overcome the mentioned disadvantages. The genetic algorithm (GA) first optimized the PID parameters. However, its optimization time is relatively long. Then, a Block Particle Swarm Optimization (BPSO) algorithm is designed to solve the problem of long optimization time. This method was then applied to the wall-following robot problem by realistically simulating it to confirm the performance. After Compared with conventional methods, the proposed method shows a relatively stable solution.
This paper will focus on the application of machine vision in mobile robots and take moving to the appropriate position and grasping the designated target as the task. This paper will describe and simulate the vision technology that mobile robots need to apply in the task process. This paper mainly uses the camera as the sensor. The difficulties of vision technology are mainly divided into three parts: scene depth information acquisition, positioning and mapping, and image processing. In order to obtain the depth information of the scene, this paper mainly introduces the depth information acquisition methods of monocular camera and binocular camera. In the aspect of localization and mapping, this paper mainly introduces the simulation of visual odometer to understand the basic process of mobile robot obtaining navigation map and its own route. Then, the gray gradient 2D maximum entropy algorithm is introduced to segment the scene and target, and extract features to judge the required target. Compared with other segmentation algorithms, the gray gradient 2D maximum entropy algorithm has higher segmentation accuracy, but the operation time is longer. This paper has simply optimized its operation efficiency. Finally, this paper describes the positioning method of grasping the object with a two degree of freedom manipulator using the knowledge of inverse kinematics. Because of the epidemic situation, schools cannot obtain experimental equipment. This paper mainly demonstrates the effectiveness of the algorithm through simulation.
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