Apr 1, 2020 · In this study, we focused on resolving AGV scheduling and path planning problems to prevent conflict and deadlock. We assessed the manner in which different AGV strategies influence the working performance of automated container terminals.

Mar 27, 2024 · These challenges manifest in slow convergence speed and low learning efficiency. To tackle this issue, this paper presents a novel multi-AGV path planning method named Particle Filters - Double Deep Q-Network (PF-DDQN)via leveraging Particle Filters (PF) and RL algorithm.

Abstract: This paper addresses a multi-AGV flow-shop scheduling problem with a reinforcement learning method. Each AGV equipped with a robotic manipulator, operates on the fixed tracks, transporting semi-finished products between successive machines.

Dear Editor, This letter presents a multi-automated guided vehicles (AGV) routing planning method based on deep reinforcement learning (DRL) and recurrent neural network (RNN), specifically utilizing proximal policy optimization (PPO) and long short-term memory (LSTM).

This project provides several RL algorithm to control multi-AGV to find collision-free paths in a robotic and mobile fulfiment system (such as Amazon Robot system)

Jun 15, 2023 · The results show that the proposed algorithm can effectively solve the multi-AGV task assignment and path planning problems, and the energy consumption results show that the planned routes can effectively improve energy efficiency.

Dec 29, 2023 · In this paper, we propose a simulation-based AGV scheduling approach for multi-AGV systems within practical shopfloors with limited buffer capacity and battery charging. The proposed approach considers the following three kinds of scheduling rules: job selection, AGV selection, and charging station selection rules; the optimal combination of ...

Nov 6, 2023 · In this paper, we propose an approach to improve the efficiency of traditional deadlock-free scheduling algorithms. Typically, AGVs have to travel to designated starting locations from their parking locations to execute tasks, the time required for which is referred to as preparation time.

This study proposed a reinforcement learning-based method for multi-AGV collaborative control, aiming to achieve coordinated motion and task coordination among multiple AGVs.

Jul 3, 2024 · In this study, we propose an Optimal Time Reuse strategy-based Dynamic multi-AGV Path planning method (OTRDP) to overcome the limitations of the existing dynamic path planning methods.