A* Based Routing and Scheduling Modules for Multiple AGVs in an Industrial Scenario

Abstract

A multi-AGV based logistic system is typically associated with two fundamental problems, critical for its overall performance: the AGV’s route planning for collision and deadlock avoidance; and the task scheduling to determine which vehicle should transport which load. Several heuristic functions can be used according to the application. This paper proposes a time-based algorithm to dynamically control a fleet of Autonomous Guided Vehicles (AGVs) in an automatic warehouse scenario. Our approach includes a routing algorithm based on the A* heuristic search (TEA*—Time Enhanced A*) to generate free-collisions paths and a scheduling module to improve the results of the routing algorithm. These modules work cooperatively to provide an efficient task execution time considering as basis the routing algorithm information. Simulation experiments are presented using a typical industrial layout for 10 and 20 AGVs. Moreover, a comparison with an alternative approach from the state-of-the-art is also presented.

Algorithms for Smooth, Safe and Quick Routing on Sensor-Equipped Grid Networks

“…In [29], the D* Lite search algorithm is run on a reachability graph obtained from a suitable coloured Petri net that models feasible multi-AGV trajectories. A Time Enhanced A* search is proposed in [30] to find collision-free route plans in a time-expanded network, and integrated with tabu search techniques to further improve the efficiency by changing the assignment of transport tasks between robots. The Conflict Based Search proposed in [31], and further enhanced in [32], explores a constraint tree whose nodes are evaluated through nominal shortest routes and, in case of collisions, branches are generated corresponding to alternative vehicle priorities.…”

Navigating Unstructured Space: Deep Action Learning-Based Obstacle Avoidance System for Indoor Automated Guided Vehicles

“…Achieving the large-scale use of automatic guided vehicles (AGVs) called for by Industry 4.0 is a daunting undertaking. Most AGVs nowadays are used in massive industries, such as Amazon, Alibaba, Lotte, Carrefour, Walmart, and Pinduoduo [1][2][3][4][5][6]. AGVs have shown great benefits in the logistics field and have led to a significant reduction in handling and transportation costs.…”

“…The studies in [3,27,28] examined the roles of AI, robotics, and data mining in AGV navigation, and concluded that effective algorithms for navigating indoor spaces rely heavily on the extraction of appropriate local features for performing keyframe selection, localization, and relative posture calculation. Many features and feature processing methods have been proposed, including segments of invariant column [4], SIFT (Scale Invariant Feature Transform) [29,30], and FREAK (Fast Retina Keypoint) [29].…”

Measurement of a Vibration on a Robotic Vehicle

“…The vehicle is able to sense its surroundings using sensors (ultrasonic, infrared, etc.). A robotic vehicle can be controlled manually (the operator controls the vehicle manually) or autonomously (the vehicle moves along a designated path) [1][2][3][4][5].…”