Intelligent selection algorithm of vision guided feeding path for AGV car transporting cigarette accessories

During the transportation of cigarette accessories, AGV trolleys often encounter challenges such as complex environments, variable paths, and dynamic obstacles. Traditional path planning methods - such as laser navigation or two-dimensional code navigation - often exhibit limitations including delayed response, poor adaptability, and difficulties in achieving global optimization when dealing with illumination variations, path deviations, and multi-task concurrency. To address these issues, this study investigates a vision-guided intelligent selection algorithm for the feeding path of AGVs in cigarette accessory transportation. The algorithm employs a vision-based AGV path deviation recognition method to identify deviations in the feeding path during cigarette accessory delivery. A multi-step grid approach is utilized to model the feeding environment as a grid map. Within this grid map, an intelligent feeding path selection model based on the pigeon-inspired optimizer (PIO) is applied. An objective function is designed to achieve the shortest path for safely reaching the feeding destination after path deviation. The PIO algorithm optimizes the path to enable vision-guided intelligent selection of the feeding path for AGVs transporting cigarette accessories. Experimental results demonstrate that the proposed algorithm can dynamically plan the optimal feeding path, reduce travel distance, and improve feeding efficiency in vision-guided feeding path selection for AGVs delivering cigarette accessories. • A multi-step grid method is proposed to dynamically adjust the grid granularity; • Take the path key points as landmarks and converge to the global optimal path; • Triggering the pigeon colony algorithm to re-plan the path.