Spanish Initiative for Fully Automated Stowage on Roll-on/roll-off Operations

In the past decades, social development has motivated a notable growth on transportation necessities. In 2020, higher tendencies are expected, so transportation demand will grow about a 20%. Besides, one of the foundations of the UE's Green Policy initiative for freight is the transportation sea-to-ground through the so-called “Short sea shipping” or “Motorways of the sea”. Facing this scenario, it is needed the development of technologies and solutions which contribute to raise the profitability, flexibility and efficiency of marine transportation. This will lead to more competitive freight, so investing on such technologies is a guarantee of success. On this basis, within the framework of the Innterconecta 2013 programme, funded by the Spanish Ministry of Economy and Competitiveness through the Centre for Industrial Technological Development (CDTI), the project AUTOPORT is being developed, which objectives are here detailed. The main objective of the project is to develop the technologies needed for a fully automated stowage on roll-on/roll-off ships in order to improve the logistic flow, reduce stowage times and maximize the efficiency of the space occupation in hold. This will be accomplished by both the automation of logistic processes and terminal trucks. Automation of processes aims for obtaining a stowage plan which reduces to the minimum the obstructions between cargo and trucks in the process and also the imbalance of the hold, in order to allow easy and smooth load operations even in rough sea conditions. Automation of terminal trucks consist in the efficient use of localization, path planning and control for taking a specifically designated roll trailer and stowing it on the exact hold location pointed by the stowage plan, all without human intervention.CDTI - Spanish Ministry of Economy and Competitiveness (AUTOPORT

FPGA implementation of embedded fuzzy controllers for robotic applications

Fuzzy-logic-based inference techniques provide efficient solutions for control problems in classical and emerging applications. However, the lack of specific design tools and systematic approaches for hardware implementation of complex fuzzy controllers limits the applicability of these techniques in modern microelectronics products. This paper discusses a design strategy that eases the implementation of embedded fuzzy controllers as systems on programmable chips. The development of the controllers is carried out by means of a reconfigurable platform based on field-programmable gate arrays. This platform combines specific hardware to implement fuzzy inference modules with a general-purpose processor, thus allowing the realization of hybrid hardware/soffivare solutions. As happens to the components of the processing system, the specific fuzzy elements are conceived as configurable intellectual property modules in order to accelerate the controller design cycle. The design methodology and tool chain presented in this paper have been applied to the realization of a control system for solving the navigation tasks of an autonomous vehicle

FPGA Implementation of Embedded Fuzzy Controllers for Robotic Applications

Fuzzy-logic-based inference techniques provide efficient solutions for control problems in classical and emerging applications. However, the lack of specific design tools and systematic approaches for hardware implementation of complex fuzzy controllers limits the applicability of these techniques in modern microelectronics products. This paper discusses a design strategy that eases the implementation of embedded fuzzy controllers as systems on programmable chips. The development of the controllers is carried out by means of a reconfigurable platform based on field-programmable gate arrays. This platform combines specific hardware to implement fuzzy inference modules with a general-purpose processor, thus allowing the realization of hybrid hardware/software solutions. As happens to the components of the processing system, the specific fuzzy elements are conceived as configurable intellectual property modules in order to accelerate the controller design cycle. The design methodology and tool chain presented in this paper have been applied to the realization of a control system for solving the navigation tasks of an autonomous vehicle. © 2007 IEEE.Ministerio de Educación y Ciencia TEC2005-04359/MIC y DPI2005-02293Junta de Andalucía TIC2006-635 y TEP2006-37

A design environment for synthesis of embedded fuzzy controllers on FPGAs

This paper presents a design environment for the synthesis of embedded fuzzy controllers on FPGAs. It provides a novel implementation technique that allows accelerating the exploration of the design space of fuzzy control modules, as well as a codesign flow that eases their integration into complex control systems and the joint development of hardware and software components. The set of CAD tools supporting this environment includes specific fuzzy logic design tools provided by Xfuzzy, FPGA synthesis and implementation tools from Xilinx, and modeling and simulation facilities from Matlab. As demonstrated by the analyzed design examples, the described development strategy takes advantage of flexibility and ease of configuration offered by the different tools to dramatically speed up the stages of description, synthesis, and functional verification of embedded fuzzy control system

Control of autonomous multibody vehicles using artificial intelligence

The field of autonomous driving has been evolving rapidly within the last few years and a lot of research has been dedicated towards the control of autonomous vehicles, especially car-like ones. Due to the recent successes of artificial intelligence techniques, even more complex problems can be solved, such as the control of autonomous multibody vehicles. Multibody vehicles can accomplish transportation tasks in a faster and cheaper way compared to multiple individual mobile vehicles or robots. But even for a human, driving a truck-trailer is a challenging task. This is because of the complex structure of the vehicle and the maneuvers that it has to perform, such as reverse parking to a loading dock. In addition, the detailed technical solution for an autonomous truck is challenging and even though many single-domain solutions are available, e.g. for pathplanning, no holistic framework exists. Also, from the control point of view, designing such a controller is a high complexity problem, which makes it a widely used benchmark. In this thesis, a concept for a plurality of tasks is presented. In contrast to most of the existing literature, a holistic approach is developed which combines many stand-alone systems to one entire framework. The framework consists of a plurality of modules, such as modeling, pathplanning, training for neural networks, controlling, jack-knife avoidance, direction switching, simulation, visualization and testing. There are model-based and model-free control approaches and the system comprises various pathplanning methods and target types. It also accounts for noisy sensors and the simulation of whole environments. To achieve superior performance, several modules had to be developed, redesigned and interlinked with each other. A pathplanning module with multiple available methods optimizes the desired position by also providing an efficient implementation for trajectory following. Classical approaches, such as optimal control (LQR) and model predictive control (MPC) can safely control a truck with a given model. Machine learning based approaches, such as deep reinforcement learning, are designed, implemented, trained and tested successfully. Furthermore, the switching of the driving direction is enabled by continuous analysis of a cost function to avoid collisions and improve driving behavior. This thesis introduces a working system of all integrated modules. The system proposed can complete complex scenarios, including situations with buildings and partial trajectories. In thousands of simulations, the system using the LQR controller or the reinforcement learning agent had a success rate of >95 % in steering a truck with one trailer, even with added noise. For the development of autonomous vehicles, the implementation of AI at scale is important. This is why a digital twin of the truck-trailer is used to simulate the full system at a much higher speed than one can collect data in real life.Tesi

Trends in vehicle motion control for automated driving on public roads

In this paper, we describe how vehicle systems and the vehicle motion control are affected by automated driving on public roads. We describe the redundancy needed for a road vehicle to meet certain safety goals. The concept of system safety as well as system solutions to fault tolerant actuation of steering and braking and the associated fault tolerant power supply is described. Notably restriction of the operational domain in case of reduced capability of the driving automation system is discussed. Further we consider path tracking, state estimation of vehicle motion control required for automated driving as well as an example of a minimum risk manoeuver and redundant steering by means of differential braking. The steering by differential braking could offer heterogeneous or dissimilar redundancy that complements the redundancy of described fault tolerant steering systems for driving automation equipped vehicles. Finally, the important topic of verification of driving automation systems is addressed