A Rapid Search Method for Scheduling Delivery of Multiple Automated Guided Vehicles

In this study, a method was developed to address the automated guided vehicle (AGV) transportation scheduling problem. For deliveries in factories and warehouses, it is necessary to quickly plan a feasible transportation schedule without delay within a specified time. This study focused on obtaining a transport schedule without delay from the specified time while maintaining the search for a better solution during the execution of the transport task. Accordingly, a method was developed for constructing a solution with a two-dimensional array of delivery tasks for each AGV, arranged in the order in which they are executed, as well as for searching for a schedule by performing exchange and insertion operations. For the exchange and insertion, a method that considers the connectivity between the end point of a task and the start point of the next task was adopted. To verify the effectiveness of the proposed method, numerical simulations were performed assuming an actual transportation task.

Techno-Economic Evaluation of 5G Technology for Automated Guided Vehicles in Production

The fifth generation of mobile communication (5G) is expected to bring immense benefits to automated guided vehicles by improving existing respectively enabling 5G-distinctive network control systems, leading to higher productivity and safety. However, only 1% of production companies have fully deployed 5G yet. Most companies currently lack an understanding of return on investment and of technical use-case benefits. Therefore, this paper analyses the influence of 5G on an automated guided vehicle use case based on a five-step evaluation model. The analysis is conducted with a use case in the Digital Experience Factory in Aachen. It shows a difference of net present value between 4G and 5G of 1.3 M€ after 10 years and a difference of return of investment of 66%. Furthermore, analysis shows an increase of mobility (13%), productivity (20%) and safety (136%). This indicates a noticeable improvement of a 5G-controlled automated guided vehicle compared to a 4G-controlled automated guided vehicle.

A Novel Automated Guided Vehicle (AGV) Remote Path Planning Based on RLACA Algorithm in 5G Environment

Remote control and monitoring will become the future trend. High-quality automated guided vehicle (AGV) path planning through web pages or clients can reduce network data transmission capacity and server resource occupation. Many Remote path planning algorithms in AGV navigation still have blind search, path redundancy, and long calculation time. This paper proposed an RLACA algorithm based on 5G network environment through remote control of AGV. The distribution of pheromone in each iteration of the ant colony algorithm had an impact on the follow-up. RLACA algorithm changed the transfer rules and pheromone distribution of the ant colony algorithm to improve the efficiency of path search and then modify the path to reduce path redundancy. Considering that there may be unknown obstacles in the virtual environment, the path obtained by the improved ant colony algorithm is used as the training data of reinforcement learning to obtain the Q-table. During the movement, the action of each step is selected by the Q-table until the target point is reached. Through experimental simulation, it can be concluded that the enhanced ant colony algorithm can quickly obtain a reasonable and adequate path in a complex environment and effectively avoid unknown obstacles in the environment.

A Randomized Greedy Algorithm for Piecewise Linear Motion Planning

We describe and implement a randomized algorithm that inputs a polyhedron, thought of as the space of states of some automated guided vehicle R, and outputs an explicit system of piecewise linear motion planners for R. The algorithm is designed in such a way that the cardinality of the output is probabilistically close (with parameters chosen by the user) to the minimal possible cardinality.This yields the first automated solution for robust-to-noise robot motion planning in terms of simplicial complexity (SC) techniques, a discretization of Farber’s topological complexity TC. Besides its relevance toward technological applications, our work reveals that, unlike other discrete approaches to TC, the SC model can recast Farber’s invariant without having to introduce costly subdivisions. We develop and implement our algorithm by actually discretizing Macías-Virgós and Mosquera-Lois’ notion of homotopic distance, thus encompassing computer estimations of other sectional category invariants as well, such as the Lusternik-Schnirelmann category of polyhedra.