Driving Cars by Means of Genetic Algorithms

Proceedings of: 10th International Conference on Parallel Problem Solving From Nature, PPSN 2008. Dortmund, Germany, September 13-17, 2008The techniques and the technologies supporting Automatic Vehicle Guidance are an important issue. Automobile manufacturers view automatic driving as a very interesting product with motivating key features which allow improvement of the safety of the car, reducing emission or fuel consumption or optimizing driver comfort during long journeys. Car racing is an active research field where new advances in aerodynamics, consumption and engine power are critical each season. Our proposal is to research how evolutionary computation techniques can help in this field. As a first goal we want to automatically learn to drive, by means of genetic algorithms, optimizing lap times while driving through three different circuits.Publicad

Cyber-Physical Embedded Systems with Transient Supervisory Command and Control: A Framework for Validating Safety Response in Automated Collision Avoidance Systems

The ability to design and engineer complex and dynamical Cyber-Physical Systems (CPS) requires a systematic view that requires a definition of level of automation intent for the system. Since CPS covers a diverse range of systemized implementations of smart and intelligent technologies networked within a system of systems (SoS), the terms “smart” and “intelligent” is frequently used in describing systems that perform complex operations with a reduced need of a human-agent. The difference between this research and most papers in publication on CPS is that most other research focuses on the performance of the CPS rather than on the correctness of its design. However, by using both human and machine agency at different levels of automation, or autonomy, the levels of automation have profound implications and affects to the reliability and safety of the CPS. The human-agent and the machine-agent are in a tidal lock of decision-making using both feedforward and feedback information flows in similar processes, where a transient shift within the level of automation when the CPS is operating can have undesired consequences. As CPS systems become more common, and higher levels of autonomy are embedded within them, the relationship between human-agent and machine-agent also becomes more complex, and the testing methodologies for verification and validation of performance and correctness also become more complex and less clear. A framework then is developed to help the practitioner to understand the difficulties and pitfalls of CPS designs and provides guidance to test engineering design of soft computational systems using combinations of modeling, simulation, and prototyping

Advances in Intelligent Robotics and Collaborative Automation

This book provides an overview of a series of advanced research lines in robotics as well as of design and development methodologies for intelligent robots and their intelligent components. It represents a selection of extended versions of the best papers presented at the Seventh IEEE International Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications IDAACS 2013 that were related to these topics. Its contents integrate state of the art computational intelligence based techniques for automatic robot control to novel distributed sensing and data integration methodologies that can be applied to intelligent robotics and automation systems. The objective of the text was to provide an overview of some of the problems in the field of robotic systems and intelligent automation and the approaches and techniques that relevant research groups within this area are employing to try to solve them.The contributions of the different authors have been grouped into four main sections:• Robots• Control and Intelligence• Sensing• Collaborative automationThe chapters have been structured to provide an easy to follow introduction to the topics that are addressed, including the most relevant references, so that anyone interested in this field can get started in the area

Advances in Intelligent Robotics and Collaborative Automation

This book provides an overview of a series of advanced research lines in robotics as well as of design and development methodologies for intelligent robots and their intelligent components. It represents a selection of extended versions of the best papers presented at the Seventh IEEE International Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications IDAACS 2013 that were related to these topics. Its contents integrate state of the art computational intelligence based techniques for automatic robot control to novel distributed sensing and data integration methodologies that can be applied to intelligent robotics and automation systems. The objective of the text was to provide an overview of some of the problems in the field of robotic systems and intelligent automation and the approaches and techniques that relevant research groups within this area are employing to try to solve them.The contributions of the different authors have been grouped into four main sections:• Robots• Control and Intelligence• Sensing• Collaborative automationThe chapters have been structured to provide an easy to follow introduction to the topics that are addressed, including the most relevant references, so that anyone interested in this field can get started in the area

Adoption of vehicular ad hoc networking protocols by networked robots

This paper focuses on the utilization of wireless networking in the robotics domain. Many researchers have already equipped their robots with wireless communication capabilities, stimulated by the observation that multi-robot systems tend to have several advantages over their single-robot counterparts. Typically, this integration of wireless communication is tackled in a quite pragmatic manner, only a few authors presented novel Robotic Ad Hoc Network (RANET) protocols that were designed specifically with robotic use cases in mind. This is in sharp contrast with the domain of vehicular ad hoc networks (VANET). This observation is the starting point of this paper. If the results of previous efforts focusing on VANET protocols could be reused in the RANET domain, this could lead to rapid progress in the field of networked robots. To investigate this possibility, this paper provides a thorough overview of the related work in the domain of robotic and vehicular ad hoc networks. Based on this information, an exhaustive list of requirements is defined for both types. It is concluded that the most significant difference lies in the fact that VANET protocols are oriented towards low throughput messaging, while RANET protocols have to support high throughput media streaming as well. Although not always with equal importance, all other defined requirements are valid for both protocols. This leads to the conclusion that cross-fertilization between them is an appealing approach for future RANET research. To support such developments, this paper concludes with the definition of an appropriate working plan

Ein mehrschichtiges sicheres Framework für Fahrzeugsysteme

In recent years, significant developments were introduced within the vehicular domain, evolving the vehicles to become a network of many embedded systems distributed throughout the car, known as Electronic Control Units (ECUs). Each one of these ECUs runs a number of software components that collaborate with each other to perform various vehicle functions. Modern vehicles are also equipped with wireless communication technologies, such as WiFi, Bluetooth, and so on, giving them the capability to interact with other vehicles and roadside infrastructure. While these improvements have increased the safety of the automotive system, they have vastly expanded the attack surface of the vehicle and opened the door for new potential security risks. The situation is made worse by a lack of security mechanisms in the vehicular system which allows the escalation of a compromise in one of the non-critical sub-systems to threaten the safety of the entire vehicle and its passengers. This dissertation focuses on providing a comprehensive framework that ensures the security of the vehicular system during its whole life-cycle. This framework aims to prevent the cyber-attacks against different components by ensuring secure communications among them. Furthermore, it aims to detect attacks which were not prevented successfully, and finally, to respond to these attacks properly to ensure a high degree of safety and stability of the system.In den letzten Jahren wurden bedeutende Entwicklungen im Bereich der Fahrzeuge vorgestellt, die die Fahrzeuge zu einem Netzwerk mit vielen im gesamten Fahrzeug verteile integrierte Systeme weiterentwickelten, den sogenannten Steuergeräten (ECU, englisch = Electronic Control Units). Jedes dieser Steuergeräte betreibt eine Reihe von Softwarekomponenten, die bei der Ausführung verschiedener Fahrzeugfunktionen zusammenarbeiten. Moderne Fahrzeuge sind auch mit drahtlosen Kommunikationstechnologien wie WiFi, Bluetooth usw. ausgestattet, die ihnen die Möglichkeit geben, mit anderen Fahrzeugen und der straßenseitigen Infrastruktur zu interagieren. Während diese Verbesserungen die Sicherheit des Fahrzeugsystems erhöht haben, haben sie die Angriffsfläche des Fahrzeugs erheblich vergrößert und die Tür für neue potenzielle Sicherheitsrisiken geöffnet. Die Situation wird durch einen Mangel an Sicherheitsmechanismen im Fahrzeugsystem verschärft, die es ermöglichen, dass ein Kompromiss in einem der unkritischen Subsysteme die Sicherheit des gesamten Fahrzeugs und seiner Insassen gefährdet kann. Diese Dissertation konzentriert sich auf die Entwicklung eines umfassenden Rahmens, der die Sicherheit des Fahrzeugsystems während seines gesamten Lebenszyklus gewährleistet. Dieser Rahmen zielt darauf ab, die Cyber-Angriffe gegen verschiedene Komponenten zu verhindern, indem eine sichere Kommunikation zwischen ihnen gewährleistet wird. Darüber hinaus zielt es darauf ab, Angriffe zu erkennen, die nicht erfolgreich verhindert wurden, und schließlich auf diese Angriffe angemessen zu reagieren, um ein hohes Maß an Sicherheit und Stabilität des Systems zu gewährleisten

Autonomous vehicle guidance in unknown environments

Gaining from significant advances in their performance granted by technological evolution, Autonomous Vehicles are rapidly increasing the number of fields of possible and effective applications. From operations in hostile, dangerous environments (military use in removing unexploded projectiles, survey of nuclear power and chemical industrial plants following accidents) to repetitive 24h tasks (border surveillance), from power-multipliers helping in production to less exotic commercial application in household activities (cleaning robots as consumer electronics products), the combination of autonomy and motion offers nowadays impressive options. In fact, an autonomous vehicle can be completed by a number of sensors, actuators, devices making it able to exploit a quite large number of tasks. However, in order to successfully attain these results, the vehicle should be capable to navigate its path in different, sometimes unknown environments. This is the goal of this dissertation: to analyze and - mainly - to propose a suitable solution for the guidance of autonomous vehicles. The frame in which this research takes its steps is the activity carried on at the Guidance and Navigation Lab of Sapienza – Università di Roma, hosted at the School of Aerospace Engineering. Indeed, the solution proposed has an intrinsic, while not limiting, bias towards possible space applications, that will become obvious in some of the following content. A second bias dictated by the Guidance and Navigation Lab activities is represented by the choice of a sample platform. In fact, it would be difficult to perform a meaningful study keeping it a very general level, independent on the characteristics of the targeted kind of vehicle: it is easy to see from the rough list of applications cited above that these characteristics are extremely varied. The Lab hosted – even before the beginning of this thesis activity – a simple, home-designed and manufactured model of a small, yet performing enough autonomous vehicle, called RAGNO (standing for Rover for Autonomous Guidance Navigation and Observation): it was an obvious choice to select that rover as the reference platform to identify solutions for guidance, and to use it, cooperating to its improvement, for the test activities which should be considered as mandatory in this kind of thesis work to validate the suggested approaches. The draft of the thesis includes four main chapters, plus introduction, final remarks and future perspectives, and the list of references. The first chapter (“Autonomous Guidance Exploiting Stereoscopic Vision”) investigates in detail the technique which has been deemed as the most interesting for small vehicles. The current availability of low cost, high performance cameras suggests the adoption of the stereoscopic vision as a quite effective technique, also capable to making available to remote crew a view of the scenario quite similar to the one humans would have. Several advanced image analysis techniques have been investigated for the extraction of the features from left- and right-eye images, with SURF and BRISK algorithm being selected as the most promising one. In short, SURF is a blob detector with an associated descriptor of 64 elements, where the generic feature is extracted by applying sequential box filters to the surrounding area. The features are then localized in the point of the image where the determinant of the Hessian matrix H(x,y) is maximum. The descriptor vector is than determined by calculating the Haar wavelet response in a sampling pattern centered in the feature. BRISK is instead a corner detector with an associated binary descriptor of 512 bit. The generic feature is identified as the brightest point in a sampling circular area of N pixels while the descriptor vector is calculated by computing the brightness gradient of each of the N(N-1)/2 pairs of sampling points. Once left and right features have been extracted, their descriptors are compared in order to determine the corresponding pairs. The matching criterion consists in seeking for the two descriptors for which their relative distance (Euclidean norm for SURF, Hamming distance for BRISK) is minimum. The matching process is computationally expensive: to reduce the required time the thesis successfully explored the theory of the epipolar geometry, based on the geometric constraint existing between the left and right projection of the scene point P, and indeed limiting the space to be searched. Overall, the selected techniques require between 200 and 300 ms on a 2.4GHz clock CPU for the feature extraction and matching in a single (left+right) capture, making it a feasible solution for slow motion vehicles. Once matching phase has been finalized, a disparity map can be prepared highlighting the position of the identified objects, and by means of a triangulation (the baseline between the two cameras is known, the size of the targeted object is measured in pixels in both images) the position and distance of the obstacles can be obtained. The second chapter (“A Vehicle Prototype and its Guidance System”) is devoted to the implementation of the stereoscopic vision onboard a small test vehicle, which is the previously cited RAGNO rover. Indeed, a description of the vehicle – the chassis, the propulsion system with four electric motors empowering the wheels, the good roadside performance attainable, the commanding options – either fully autonomous, partly autonomous with remote monitoring, or fully remotely controlled via TCP/IP on mobile networks - is included first, with a focus on different sensors that, depending on the scenario, can integrate the stereoscopic vision system. The intelligence-side of guidance subsystem, exploiting the navigation information provided by the camera, is then detailed. Two guidance techniques have been studied and implemented to identify the optimal trajectory in a field with scattered obstacles: the artificial potential guidance, based on the Lyapunov approach, and the A-star algorithm, looking for the minimum of a cost function built on graphs joining the cells of a mesh over-imposed to the scenario. Performance of the two techniques are assessed for two specific test-cases, and the possibility of unstable behavior of the artificial potential guidance, bouncing among local minima, has been highlighted. Overall, A-star guidance is the suggested solution in terms of time, cost and reliability. Notice that, withstanding the noise affecting information from sensors, an estimation process based on Kalman filtering has been also included in the process to improve the smoothness of the targeted trajectory. The third chapter (“Examples of Possible Missions and Applications”) reports two experimental campaigns adopting RAGNO for the detection of dangerous gases. In the first one, the rover accommodates a specific sensor, and autonomously moves in open fields, avoiding possible obstacles, to exploit measurements at given time intervals. The same configuration for RAGNO is also used in the second campaign: this time, however, the path of the rover is autonomously computed on the basis of the way points communicated by a drone which is flying above the area of measurements and identifies possible targets of interest. The fourth chapter (“Guidance of Fleet of Autonomous Vehicles ”) stresses this successful idea of fleet of vehicles, and numerically investigates by algorithms purposely written in Matlab the performance of a simple swarm of two rovers exploring an unknown scenario, pretending – as an example - to represent a case of planetary surface exploration. The awareness of the surrounding environment is dictated by the characteristics of the sensors accommodated onboard, which have been assumed on the basis of the experience gained with the material of previous chapter. Moreover, the communication issues that would likely affect real world cases are included in the scheme by the possibility to model the comm link, and by running the simulation in a multi-task configuration where the two rovers are assigned to two different computer processes, each of them having a different TCP/IP address with a behavior actually depending on the flow of information received form the other explorer. Even if at a simulation-level only, it is deemed that such a final step collects different aspects investigated during the PhD period, with feasible sensors’ characteristics (obviously focusing on stereoscopic vision), guidance technique, coordination among autonomous agents and possible interesting application cases

Migration from Teleoperation to Autonomy via Modular Sensor and Mobility Bricks

In this thesis, the teleoperated communications of a Remotec ANDROS robot have been reverse engineered. This research has used the information acquired through the reverse engineering process to enhance the teleoperation and add intelligence to the initially automated robot. The main contribution of this thesis is the implementation of the mobility brick paradigm, which enables autonomous operations, using the commercial teleoperated ANDROS platform. The brick paradigm is a generalized architecture for a modular approach to robotics. This architecture and the contribution of this thesis are a paradigm shift from the proprietary commercial models that exist today. The modular system of sensor bricks integrates the transformed mobility platform and defines it as a mobility brick. In the wall following application implemented in this work, the mobile robotic system acquires intelligence using the range sensor brick. This application illustrates a way to alleviate the burden on the human operator and delegate certain tasks to the robot. Wall following is one among several examples of giving a degree of autonomy to an essentially teleoperated robot through the Sensor Brick System. Indeed once the proprietary robot has been altered into a mobility brick; the possibilities for autonomy are numerous and vary with different sensor bricks. The autonomous system implemented is not a fixed-application robot but rather a non-specific autonomy capable platform. Meanwhile the native controller and the computer-interfaced teleoperation are still available when necessary. Rather than trading off by switching from teleoperation to autonomy, this system provides the flexibility to switch between the two at the operator’s command. The contributions of this thesis reside in the reverse engineering of the original robot, its upgrade to a computer-interfaced teleoperated system, the mobility brick paradigm and the addition of autonomy capabilities. The application of a robot autonomously following a wall is subsequently implemented, tested and analyzed in this work. The analysis provides the programmer with information on controlling the robot and launching the autonomous function. The results are conclusive and open up the possibilities for a variety of autonomous applications for mobility platforms using modular sensor bricks

Drone deep reinforcement learning: A review

Unmanned Aerial Vehicles (UAVs) are increasingly being used in many challenging and diversified applications. These applications belong to the civilian and the military fields. To name a few; infrastructure inspection, traffic patrolling, remote sensing, mapping, surveillance, rescuing humans and animals, environment monitoring, and Intelligence, Surveillance, Target Acquisition, and Reconnaissance (ISTAR) operations. However, the use of UAVs in these applications needs a substantial level of autonomy. In other words, UAVs should have the ability to accomplish planned missions in unexpected situations without requiring human intervention. To ensure this level of autonomy, many artificial intelligence algorithms were designed. These algorithms targeted the guidance, navigation, and control (GNC) of UAVs. In this paper, we described the state of the art of one subset of these algorithms: the deep reinforcement learning (DRL) techniques. We made a detailed description of them, and we deduced the current limitations in this area. We noted that most of these DRL methods were designed to ensure stable and smooth UAV navigation by training computer-simulated environments. We realized that further research efforts are needed to address the challenges that restrain their deployment in real-life scenarios