Transport operations in container terminals: Literature overview, trends, research directions and classification scheme

The research presented in this paper approaches the issue of navigation using an automated guided vehicle (AGV) in industrial environments. The work describes the navigation system of a flexible AGV intended for operation in partially structured warehouses and with frequent changes in the floor plant layout. This is achieved by incorporating a high degree of on-board autonomy and by decreasing the amount of manual work required by the operator when establishing the a priori knowledge of the environment. The AGV's autonomy consists of the set of automatic tasks, such as planner, perception, path planning and path tracking, that the industrial vehicle must perform to accomplish the task required by the operator. The integration of these techniques has been tested in a real AGV working on an industrial warehouse environment.

Autonomous navigation of an automated guided vehicle in industrial environments

The increased popularity of wireless networks has enabled the development of localization techniques that rely on WiFi signal strength. These systems are cheap, effective, and require no modifications to the environment. In this paper, we present a WiFi localization algorithm that generates WiFi maps using Gaussian process regression, and then estimates the global position of an autonomous vehicle in an industrial environment using a particle filter. This estimate can be used for bootstrapping a higher-resolution localizer, or for cross-checking and localization redundancy. The system has been designed to operate both indoors and outdoors, using only the existing wireless infrastructure. It has been integrated with an existing laser-beacon localizer to aid during initialization and for recovery after a failure. Experiments conducted at an industrial site using a large forklift-type autonomous vehicle are presented.

WiFi position estimation in industrial environments using Gaussian processes

Powertrain electrification continues to be a growing trend in vehicular applications. Electric powertrains have numerous advantages over traditional mechanical and hydraulic powertrains but there are still important challenges to overcome for long-term commercial success. This research presents a technological assessment of present and future developments of powertrain electrification in non-road mobile machinery (NRMM). The challenges and opportunities of NRMM electrification are described in detail. The trends and drivers related to technological development such as regulations, policies and market development are analyzed, and technology enablers are highlighted. Future scenarios are formulated based on the prevailing trends and drivers, development of key components, scientific literature and status of the non-road mobile machinery industry. Some recommendations are also given in relation to the development of hybrid and electric powertrains for NRMM. The key findings of this research indicate that the electrification of NRMM is slowly started and the progress is demonstrated by hybridization of some specific, successful mobile machines. In short-term, high component and technology development costs remain the main barrier for higher adoption of electric and hybrid powertrains. In the long-term scenario, many NRMM can operate autonomously and powertrain electrification has become mainstream technology.

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Overview of Powertrain Electrification and Future Scenarios for Non-Road Mobile Machinery

For many players in the service and manufacturing sectors, the cost associated with transportation processes is a key factor in competitiveness and market success. Innovations such as wireless connectivity, RF identification (RFID), and the Internet of Things have already transformed these industries, and this trend-stimulated by modern wireless technologies, wave-based imaging systems, and autonomous driving-is set to continue.

Steady Delivery: Wireless Local Positioning Systems for Tracking and Autonomous Navigation of Transport Vehicles and Mobile Robots

This article describes the development and implementation of an autonomous straddle carrier (AutoStrad) for the movement of shipping containers in a port environment. In this article, we describe the vehicle and the control, navigation, safety, and pilot functions, which enable autonomous operation. The description of the implementation of the complete system also addresses issues of operational safety, scheduling, and planning of operations, interfaces to quay-side cranes, and road-side hauler vehicles. The complete production system, consisting of 18 autonomous straddle carriers, has been in continuous operation for the past two years at the Port of Brisbane, Australia. This article concludes by distilling some of the key technical and commercial lessons learned in moving from a concept through prototype to fully operational system.

M. Stevens

Papers

Field and service applications - An autonomous straddle carrier for movement of shipping containers - From Research to Operational Autonomous Systems