Autonomous systems are known today largely due to the latest trends from the automotive industry. In fact, automated systems of varying degrees of autonomy are an integral part of future developments and development plans for many areas of activity. The article presented by the authors Werner Damm and Ralf Kalman from the magazine Informatik-Spektrum Edition 5/2017 presents various industry norms and standards, as well as describes the functionality and requirements for methods, processes and tools for the development of appropriate software.
How much should a technical system and how autonomous it can be?
Today it seems that there are no boundaries for the implementation of more and more advanced autonomous systems. We are on the verge of introducing technologies to the market that independently build complex relationships of the surrounding world based on the data provided, automatic identification of objects and information from sensors at various levels. All this is used to get an accurate digital representation of reality for the realization of the task. Systems are introduced that are capable of analyzing the possible further development of events in the surrounding reality, which goes far beyond the limits of human analytical abilities. Systems are being implemented that independently plan and implement tasks without requiring any support from outside. These systems are endowed with human cognitive abilities, relevant in the context of the task, which allow them to act completely autonomously.
The recently published German high-tech strategy report from the German government shows many possibilities for using autonomous systems. Among them are all types of "Smart Systems", such as Smart Mobility, Smart Health, Smart Production, Smart Energy, whose intelligence is realized on the basis of the above possibilities. They are capable of real-time creating a digital picture of the world, processing data from a variety of information sources, and organizing the joint operation of millions of subsystems in such a way as to ensure the successful fulfillment of goals, such as, for example, optimizing the use of resources. The benefits of this can be applied in many areas of public life: health and transport, energy consumption, productivity and quality of products, prevention of natural disasters and collisions of various vehicles. Philips, for example, when using special wearable sensors for postoperative observation of patients, expects a reduction in postoperative cardiac arrest by 86%, and, through “smart” monitoring of critical health parameters in outpatient treatment, a reduction in its cost by 34%.
Automated control systems have been around for quite a few years. Automation allows you to effectively use technology without the need for manual intervention. Typical tasks of automated control and equipment settings are presented in the form of control circuits, for which mathematical models are created and which are implemented in the form of electronic devices and software.
The modern development of cyber-physical systems goes far beyond these limits. Combining IT with embedded control systems and dynamic interaction with each other ensures their work together through heterogeneous data interfaces. As with automation in the 1980s, autonomous production promises increased efficiency, productivity, and quality.
Such developments are carried out in many areas of application of technical systems. Although their application scenarios differ, in the field of software common problems can be identified and generalized methods for solving them are described. Examples of such methods will be presented in the last part of this article. Of particular interest is the use of self-learning systems. With them, the potential possibilities of autonomy seem limitless, because it is possible to recognize the initially unknown, affecting the operation of the system, the artifacts of the surrounding world, and study the dynamic models relating to them. Thus, new, previously unrealistic possibilities of using equipment are being opened.
The potential market value of technologies arising from these developments is estimated at hundreds of billions of USD. In particular, the study of the European project Platforms4CPS provides the following data:
Thanks to the development of technology, new types of products and services with a high level of automation are emerging on the modern market. This raises the question of in which areas such developments really make sense, and what impact they have on society.
In the conditions of constantly increasing level of autonomy, the quality of interaction between man and technology will definitely change. Today, a person acts not only as an end user, but, in many cases, as part of a human-in-the-Loop management system. Autonomy creates a trend that sets the interaction of man and technology to a higher level of abstraction. The autonomous system gives a person the opportunity to familiarize himself with a part of his digital vision of the world with the help of suitable abstractions, such as, for example, virtual reality technologies that are relevant for solving a specific problem at a given time. Conversely, a person can easily affect complex processes within the system through intuitive human-machine interfaces. This communication, accompanied by a rising level of abstraction, requires, in turn, a certain level of qualification and training. At the same time, jobs for low-skilled staff will disappear as unnecessary.
Continuous use of a large number of data sources will greatly increase the risk of their exposure. The architecture of networked distributed systems will set extremely high demands on its protection in order to avoid the catastrophic impact of possible cyber attacks aimed at disabling individual constituent components.
With growing autonomy, the question also arises of what values ​​the underlying decision-making process should have, and whether they correspond to our own. Based on this, the European Parliament in its resolution of February 16, 2017 decided:
Finally, due to the upcoming entry into the market of autonomous unmanned vehicles, it is necessary to revise the laws on liability for emerging offenses.
These topics therefore go beyond their purely professional sphere. How should autonomous systems be designed so that they bring not only economic benefits, but would also be positively perceived by society? These problems should be the subject of study in computer science. It is time to rethink the existing processes and design techniques in which the social impact assessment of autonomous systems being developed should be included on an ongoing basis.
The most famous example is autonomous vehicles in the automotive industry. Many manufacturers announced the release on the market of the corresponding cars in the next 3-4 years. However, the support systems that are already available today make it possible to realize amazing things. Despite this, the path from partially automated driving (some manufacturers also speak of “manned” driving in this case) to fully autonomous driving is still very far away. With a partially automated (corresponds to the 2nd level of automation for SAE ) the main responsibility lies with the person, and he must be able to independently intervene in the process as soon as possible. In addition, the possibility of using such systems is limited to a strictly normalized environment (for example, driving on a highway). At the highly automated driving level (automation level 3 for SAE), the driver is allowed to pay attention to other things, that is, the software guarantees complete driving safety or, in the event of any error, puts the system into a safe state, for example, stopping the vehicle on the curb. Fully automated cars (automation level 4 for SAE), which do their job completely without driver assistance, represent a higher degree of autonomy, and they do not need any indication as to speed or environmental conditions.
Significant impact on the development of this industry has, first of all, not the desire of ordinary people to transfer control of their car to other hands, but the needs of new transport companies in the relevant services, opening up new market segments or offering more efficient and fast public transport within populated areas. In freight transportation, automation allows you to unload the driver, who can devote the released time to other tasks and will thus work more productively.
In railway and, in particular, in underground transport some processes are already automated. There is a simplified model here, since the system operates on a homogeneous landscape, where there is no intersection of transport routes and many of the routes are isolated from each other. On the other hand, the superior system of management and coordination of processes is added to this, because of which the International Union of Public Transport ( UITP ) included in its classification an upstream system of monitoring and control. The automated train system contains the following three components: safety, train management and train monitoring. Safety is controlled by keeping the distance between the trains, as well as controlling their speed. The control ensures the movement of the train according to the schedule and regulates, for example, the opening and closing of the doors of the cars. Supervision of trains controls, in turn, all routes and the entire infrastructure and transmits the relevant information to the control center.
Such a system can be most easily implemented in the metro on the basis of vehicle homogeneity and infrastructure isolation. However, the relevant concepts can be transferred to other areas of railway transport, up to large sorting stations. At the same time, there are still problems when observing and controlling the movement of international transport or because of the complexity of the environment, such as, for example, the movement of suburban trains at railway stations of various types. The engine advancing the automation of railway transport is the high economic benefit of the proposed solutions, achieved, for example, by saving energy with concerted processes of acceleration and braking in one transport network.
In air transport, automated flight control has been used for a long time. For drones, used mainly for military purposes, the level of autonomy was increased in terms of self-planning tasks and mission management. The ten autonomy levels of ALFUS (Autonomy Levels for Unmanned Systems) use three projections to characterize the capabilities of the system: independence from human intervention, complexity of tasks, and complexity of the environment. Together they characterize the ability of battery life. When searching for technological solutions for a higher degree of autonomy, such topics as behavior in a group, adaptive communication between devices, and self-study are also added, which, as yet, have not touched upon the other taxonomies mentioned above.
In production, automated processes are standard with the introduction of programmable logic controllers (PLCs) in the 1980s. Such processes, however, have little flexibility and are focused on mass production. Individualized production, or market-driven changes in the product portfolio, lead to costly retrofitting of production lines and equipment re-equipment. In the process of developing digital technologies and based on the concept of Industry 4.0, individualized production seeks to achieve the same level of efficiency and quality as in mass production. At the same time, it should automatically adapt to changing conditions and new production goals. The Frauenhofer Research Society offers 5 evolutionary steps that accompany this development. First of all, it is required to ensure the collection and processing of production data. This will be the basis for support systems that assist in work and in decision making. At the third stage, the integration of production stages into a single data exchange network and their integration with each other provide the necessary conditions for optimizing the entire system as a whole. To increase the elasticity of production in the fourth stage, the system requires the ability to transform and reconfigure. And at the last fifth level, the production system must be able to organize itself. To date, production systems have settled at levels from the first (production data collection) to the third (production united by a network of common data, such as in the production of cars). To move on to the next stage, as a rule, a complete restructuring of the entire production architecture is required, which, respectively, is costly.
The levels of autonomy of all the listed applications are shown again in the table, while an attempt is made to present similar degrees of autonomy from different domains at the same level.
Autonomy level | Motor transport | Railway transport | Aviation | Production |
---|---|---|---|---|
0 | No automation | "Rides as he sees" | Data collection and processing | |
one | Auxiliary systems | Auxiliary systems | ||
2 | Partial Automation | Automated security systems with a driver | Limited control | Work in a single network and integration |
3 | Conditional Automation | Automated safety and operation systems with driver | Real-time status diagnostics | Decentralization, adaptation and transformation |
four | High automation | Unmanned operation | Error, breakdown and flight conditions adaptability | |
five | Full automation (autonomy) | Unmanned operation without human control | Self rerouting | Self-organization and autonomy |
6 | Autonomous behavior in the group under any external conditions |
On the basis of the examples given, one can already recognize a lot in common in classifications by levels and goals of autonomy. A generic classification that would successfully combine various aspects together was developed and published by SafeTRANS as part of the technical planning for the implementation of highly automated systems. In it, the essential aspects of automation are divided into four classes:
An essential element of the presented classification is the ability to learn in autopoiesic systems. Today, cyber-physical systems cannot be endowed with such an ability, since there are no relevant regulatory requirements ensuring their reliable and safe operation, because it is impossible to prove the predictability and reliability of the system after its spontaneous change. Recent technological breakthroughs in the field of Deep Learning and high results in recognizing images and identifying patterns show, nevertheless, that developments in this direction and the possibilities of machine learning are developing at a very fast pace. However, there are still many obstacles along the way and more research is needed: neural networks can also develop themselves in an unintended direction or extract patterns from data that should not be recognized. Current research shows, for example, that the process of automatic learning also studies ethically undesirable historical data, such as sexual preferences or racist behavior. Thus, it is necessary to carry out appropriate control by ethical and legal norms. That is why the neural networks today can not be changed after the training phase. However, due to the large amount of input data and the complexity of neural networks, the problem of uncertainty in their behavior still remains.
In particular, in the Asian region, the role of autonomous systems is increasingly seen from the standpoint of their influence on humans. They should help people by simplifying their work. At the same time, a person is still a control element (human-in-the-loop), so in this case we can talk about cooperative intelligence. An example of this is the interaction of man and robot in the joint performance of a task. Such self-sufficient systems of the future transfer experience between cars and people and adapt their behavior. At the same time, there is an opportunity to address ethical issues. Behavior of machines in relation to a person is a field of research of machine ethics. However, even simpler autonomous systems require an interface to a person. Relevant user interfaces are required to clearly inform, make available various services and information and, ideally, take into account the individual behavior of a person in different situations.
This section provides basic recommendations from SafeTRANS regarding safety, availability and the development of highly autonomous systems. The main difficulty for an autonomous system is the recognition of the surrounding reality.
The complexity of the world’s processes makes it impossible to carry out the multiple tests necessary to allow an autonomous system to operate. On this basis, it is recommended to implement an additional system of continuous monitoring of the system and train it on the basis of data obtained as a result of tests in real conditions. The diagram below shows such a meta-level learning process in which the system is tested in real conditions, and observational data after an independent assessment become the basis for the learning process.
SafeTRANS recommends the following activities for developing autonomous systems:
Action area | activity |
---|---|
1. Models of the world | 1.1. Development of a common open industry standard for environmental models in various applications, in accordance with the stage of development and the level of complexity of the system. 1.2. Building a publicly available process and related infrastructure for building virtual systems testing. This requires: accredited institutions; public test environment; real-life testing specifications 1.3. Creation of arguments acceptable to permitting bodies and society, proving the safety of highly automated systems and based on the results of virtual verification and testing in real conditions. |
2. Learning community | 2.1. Building an accessible process for learning based on real-world observations. For this you need: an independent accredited Trust Center; an obligation on the part of commercial organizations to voluntarily provide the authorized Trust Center with the necessary anonymous data; the transfer by the Trust Center of the analysis results back to the validation process. |
3. Architecture | 3.1. Standardization of information exchange protocols between objects and situations in the industry to ensure their interaction with each other. 3.2. Standardized functional architecture for automated systems and their components, which confirms security and provides minimal functionality in simplified modes of use. 3.3. , , . 3.4. / . 3.5. |
4. | 4.1. . 4.2. , . 4.3. , |
5. | 5.1. , . 5.2. , . 5.3. , , , |
Source: https://habr.com/ru/post/421619/
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