This paper proposes a methodology for modeling and controlling a flexible material handling system (MHS), composed of multiple automated guided vehicles (AGVs), suitable for Flexible Manufacturing Systems (FMSs). The AGVs incorporate artificial intelligent techniques to: i) facilitate the configuration and adaptation when there are layout modifications and ii) simplify the interaction between them using simple coordination models. In order to achieve higher flexibility, the MHS makes use of a decentralized navigation control, which increases autonomy and scalability, and a distributed Petri net for solving task allocation and traffic control problems. In order to facilitate the integration with the manufacturing processes, tasks dispatched by manufacturing cells are allocated by the MHS itself, taking into account pending transportation tasks and the system's performance. The whole system has been tested in a real factory and is currently in operational use.

Purpose – The paper aims to describe the design and development of an automated guided vehicle (AGV) that incorporates artificial intelligence techniques to increase its autonomy and flexibility. The aim is developing a flexible AGV that operates as a flexible material handling system (MHS) in dynamic industrial environments. Design/methodology/approach – Introduces the entire on-board control system including hardware and software designs. The sensory system consists of a laser navigation system for localisation and a security laser scanner for sensing the environment. The software architecture is instantiated in a CPU that is connected to low level controllers through a CAN bus. Simplicity, flexibility, robustness and safety were concerned in the design process. Findings – The developed prototype is able to operate in partially structured and dynamic environments, is easily configured using an approximated description of the workplace and is able to adapt when slight floor layout modifications. This development shows that current technology permits introducing intelligent vehicles in complex manufacturing systems. Practical implications – The prototype is successfully tested in a real factory, operating as a flexible MHS, transporting pallets between production and storage lines. Originality/value – A novel flexible AGV is designed and developed to operate as a flexible MHS in dynamic industrial environments. The system satisfies the safety and robustness requirements of industrial applications. The flexible MHS results especially suitable for manufacturing systems that suffers from cyclic and seasonal variations and for flexible manufacturing systems where the possibility of choosing alternative routes is a must.

Numerous approaches have been proposed to generate well-balanced gaits in biped robots that show excellent performance in simulated environments. However, in general, the dynamic balance of the robots decreases dramatically when these methods are tested in physical platforms. Since humanoid robots are intended to collaborate with humans and operate in everyday environments, it is of paramount importance to test such approaches both in physical platforms and under severe conditions. In this work, the special characteristics of the Nao humanoid platform are analyzed and a control system that allows robust walking and disturbance rejection is proposed. This approach combines the zero moment point (ZMP) stability criterion with angular momentum suppression and step timing control. The proposed method is especially suitable for platforms with limited computational resources and sensory and sensory-motor capabilities.

The problem of attitude estimation is broadly addressed using the Kalman filter formalism and unit quaternions to represent attitudes. This paper is also included in this framework, but introduces a new viewpoint from which the notions of “multiplicative update” and “covariance correction step” are conceived in a natural way. Concepts from manifold theory are used to define the moments of a distribution in a manifold. In particular, the mean and the covariance matrix of a distribution of unit quaternions are defined. Non-linear versions of the Kalman filter are developed applying these definitions. A simulation is designed to test the accuracy of the developed algorithms. The results of the simulation are analyzed and the best attitude estimator is selected according to the adopted performance metric.

We present a decentralized approach used to coordinate several flexible automated guided vehicles (AGVs) operating in flexible manufacturing systems (FMSs). The decentralized coordination is based on a control policy for planning the collision-free motion, which takes into account kinematics and safety constrains. Besides this, in order to avoid deadlocks in critical zones, such as doors and corridors, a set of hazardous zones is defined, where mutual exclusion should be ensured. In order to consider the performance of the whole system, transportation tasks are allocated to the more appropriate member of the multi-robot team by a centralized auction. The interaction of AGVs operating under different collision conditions has been tested and evaluated in a real industrial setting. The primary contribution of this paper is to show empirically that the presented decentralized approach is viable, effective and safety for coordinating physical multi-robot systems in industrial environments.