Research on Optimization of the AGV Shortest-Path Model and Obstacle Avoidance Planning in Dynamic Environments

Abstract

This paper proposes a support vector machine (SVM)-based AGV scheduling strategy that enhances the scheduling efficiency of automated guided vehicles (AGVs) in intelligent factories. The developed scheme optimizes the task area division process to endow the AGVs with the ability to avoid obstacles in complex dynamic environments. Specifically, given the two AGV motion cases, i.e., towards a single target point and multiple target points, the optimal path was determined utilizing the exhaustive and the Q-learning methods, while path optimization was realized by utilizing different schemes. Based on the shortest path obtained, a nonlinear programming model with the shortest time as the objective was built, and the AGV’s turning path was proved to be optimal by the non-dominated sorting genetic algorithm (NSGA-II). Several simulation tests and calculation results validated the proposed method’s effectiveness, highlighting that the developed scheme is a rational solution to the obstacle congestion and deadlock problems. Moreover, the experimental results demonstrated the proposed method’s superiority in path planning accuracy and its ability to respond well in complex dynamic environments. Overall, this research provides a reference for developing and applying AGV cluster scheduling in real operational scenarios.

Obstacle Avoidance Design of Aerial Robot Combining A star algorithm Algorithm and Optical Dynamic Capture Method

“…In this paper, carbon plate material is used as the frame of the aerial robot to reduce its weight of the aerial robot, and the design of the outer ring protective frame is added to improve the use frequency of the sensor and greatly improve the endurance of the aerial robot [3][4][5][6]. In addition, the four axis rotor "X" structure is adopted to avoid the phenomenon that the acceleration time is too long and the sensor is blocked due to the "+" structure [7][8][9]. The structure diagram of the aerial robot is shown in Figure 1…”

Rancang bangun sistem robot AGV untuk penyortiran paket ekspedisi dengan fitur anti collision

“…Semakin tinggi keakuratan heuristik, semakin cepat dan bagus lokasi tujuan ditemukan dan dengan tingkat keakuratan yang lebih baik. Algoritma A* yang secara dasar digunakan adalah sebagai berikut [15] 1 Pada [16] mengusulkan strategi penjadwalan AGV berbasis support vector machine (SVM) yang meningkatkan efisiensi penjadwalan AGV. Skema yang dikembangkan mengoptimalkan proses pembagian area tugas untuk memberi AGV kemampuan menghindari rintangan di lingkungan dinamis yang kompleks.…”