Automatic Guided Vehicles or AGVs are increasingly being used in manufacturing plants for transportation tasks. Optimal scheduling of AGVs is a difficult problem. A learning AGV is very attractive in a manufacturing plant since it is hard to manually optimize the scheduling algorithm to each new situation. In this paper we compare four reinforcement learning methods for scheduling AGVs. Q-learn...

Students following the Mechatronics Program stream at the University of Waikato often continue to a MSc degree that can involve the design of, or modification to, an Autonomous Guided Vehicle (AGV). Our challenge is to inspire and extend the capabilities of these students, a success that can perhaps be measured by the sophistication of the AGVs currently being produced by our graduates.

AGV (Automated Guided Vehicles) as one of the most intelligent technologies is widely applied in warehousing operations with a high speed development logistic fields. Automated material systems have proved that they would improve effectiveness loading, unloading and handling company well reducing labor cost strengthening working safety. AGV, part automated systems, preforms powerful actions ful...

A fleet of autonomous ground vehicles (AGV) is envisioned to expand farming arable land suitable for production except being too steep conventional equipment. The success proposed multi-AGV system largely depends on the traction performance individual AGVs unevenly sloped terrain and optimization control variables. Therefore, drawbar pull a prototype AGV was evaluated in soil bin at varying slo...

Hierarchical reinforcement learning (RL) is a general framework which studies how to exploit the structure of actions and tasks to accelerate policy learning in large domains. Prior work in hierarchical RL, such as the MAXQ method, has been limited to the discrete-time discounted reward semiMarkov decision process (SMDP) model. This paper generalizes the MAXQ method to continuous-time discounte...

In this paper, we present the design and performance analysis of an innovative system for tracking Automated Guided Vehicles (AGVs) in indoor industrial scenarios. An on-board odometer provides information about the dynamic state of the AGV, allowing to predict its pose (i.e., its position and orientation). At the same time, an external Ultra-Wide Band (UWB) wireless network provides the inform...

We compare two approaches for a Markovian model in flexible manufacturing systems (FMSs) using Monte Carlo simulation. The model which is a development of Fazlollahtabar and Saidi-Mehrabad (2013), considers two features of automated flexible manufacturing systems equipped with automated guided vehicle (AGV) namely, the reliability of machines and the reliability of AGVs in a multiple AGV jobsho...

A nonlinear model predictive control algorithm is developed for obstacle avoidance in high-speed, large-size autonomous ground vehicles (AGVs) that perceive the environment only through information provided by on-board sensors. The mission of the AGV is to move from its initial configuration to the goal configuration safely. The resulting trajectory should be collision-free and the AGV should b...