AGV trolley obstacle avoidance sensing technology: refers to the AGV trolley with an automatic ranging system. After measuring the obstacle distance, it will perform multi-level deceleration buffer parking according to different obstacle distances, and will quantify the obstacle distance measurement in real time At the same time, the intelligent AGV trolley adopts covered obstacle measurement, and is not affected by various external interference factors, and its anti-interference ability is very strong.
AGV car navigation and positioning technology: As the core part of AGV technology research, the pros and cons of navigation and positioning technology will directly affect the performance stability, automation and application practicability of AGV.
Driving path planning of AGV trolley: Driving path planning refers to solving the path problem of AGV from the starting point to the target point, that is, the problem of "how to go". At this stage, a large number of artificial intelligence algorithms at home and abroad have been applied to AGV driving path planning, such as ant colony algorithm, genetic algorithm, graph theory, virtual force method, neural network and A* algorithm.
AGV trolley job task scheduling: refers to processing tasks according to the current job request, including sorting tasks based on certain rules and arranging appropriate AGV processing tasks. It is necessary to comprehensively consider multiple factors such as the number of task execution times, power supply time, work and idle time of each AGV, in order to achieve reasonable application and optimal allocation of resources.
AGV trolley multi-machine coordination: refers to how to effectively use multiple AGVs to jointly complete a complex task, and solve a series of problems such as system conflicts, resource competition and deadlocks that may occur in the process. The commonly used multi-machine coordination methods include distributed coordination control method, road traffic rule control method, control method based on multi-agent theory and multi-robot control method based on Petri net theory.
AGV trolley motion control technology: different wheel mechanisms and layouts have different steering and control methods. At this stage, AGV steering drive methods include the following two: two-wheel differential drive steering method, that is, two independent drive wheels are fixed coaxially and parallel. In the middle of the car body, other free universal wheels are used as support. The controller can realize steering at any turning radius by adjusting the speed and steering of the two driving wheels; the steering wheel controls the steering mode, that is, by controlling the yaw angle of the steering wheel. When turning, there is a limitation of a small turning radius.
The motion control system forms a closed loop system through the feedback of the encoder installed on the drive shaft. At present, the AGV path tracking methods based on two-wheel differential drive mainly include: PID control method, optimal predictive control method, expert system control method, nerve Network control method and fuzzy control method.
AGV car information fusion technology: Information fusion refers to the use of multiple sources of information to fully identify, analyze, estimate, and dispatch data, complete the tasks of decision-making and processing information, and appropriately estimate the surrounding environment and battle conditions. Currently, the information fusion technology used in the research and application of the guide domain mainly includes Kalman filtering, Bayesian estimation and D-S evidence reasoning, among which Kalman filtering is widely used. Kalman filtering has good real-time performance, but it is based on a strict mathematical model. When the guidance model has a large modeling error or the system characteristics change, it will often cause the filtering to diverge. In order to improve the robustness and adaptive ability of the filtering algorithm, suitable adaptive Kalman filtering algorithm, robust filtering algorithm or intelligent filtering (such as fuzzy inference, neural network, expert system) can be studied according to the guidance requirements and characteristics of AGV Methods etc.
Shenzhen Dongjin Intelligent Technology Co., Ltd. has focused on the development and production of AGV trolleys, AGV handling robots, AGV handling vehicles, laser AGV forklifts, and heavy-duty AGVs for ten years. It is an agv car manufacturer and agv intelligent robot manufacturer that masters core technologies. : Http://www.djagvs.com/.