Key technologies of mobile handling robots (AGV):
(1) Guidance and positioning technology. As the core part of AGV technology research, the quality of guidance and positioning technology will be directly related to the performance stability, degree of automation and application practicability of AGV. Table 1 shows the common guidance and positioning technology of AGV.
(2) Path planning and task scheduling technology.
First, travel path planning. 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 present, a large number of artificial intelligence algorithms have been applied to AGV driving path planning at home and abroad, such as ant colony algorithm, genetic algorithm, graph theory method, virtual force method, neural network and A* algorithm.
Second, job task scheduling. Job task scheduling refers to processing tasks according to the request of the current job, including sorting tasks based on certain rules and arranging suitable AGV processing tasks. It is necessary to comprehensively consider multiple factors such as the task execution times, power supply time, working and idle time of each AGV to achieve a reasonable application and optimal allocation of resources.
Third, multi-machine coordination work. 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, multi-agent theory-based control method and multi-robot control method based on Petri net theory.
(3) Motion control technology. Different wheel mechanisms and layouts have different steering and control methods. At this stage, the steering and driving methods of AGV include the following two: two-wheel differential driving steering mode, that is, two independent driving wheels are fixed coaxially and parallel to the middle of the car body, and other The support function of the free universal wheel, the controller can realize the steering of any turning radius by adjusting the speed and steering of the two driving wheels; the steering wheel controls the steering mode, that is, the turning is realized by controlling the yaw angle of the steering wheel, and its existence is the smallest Turning radius restrictions. The 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, neural network control method and fuzzy control method.
(4) Information fusion technology. Information fusion refers to the use of the association and combination of multi-source information to fully identify, analyze, estimate and schedule data, complete the task of issuing decisions and accurately processing information, and make appropriate estimates of the surrounding environment and combat conditions. At present, the information fusion technologies researched and applied in the field of guidance mainly include Kalman filter, Bayesian estimation method and D-S evidence reasoning, among which Kalman filter is the most widely used. Kalman filtering has good real-time performance, but it is based on a strict mathematical model. When the guided model has a large modeling error or the system characteristics change, it often leads to filter divergence. 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 reasoning, neural network, expert system) can be studied according to the guidance requirements and characteristics of AGV. method etc.
