Abstract

A new attractive potential field function is proposed in this paper for manipulator trajectory planning. Existing attractive potential field constructs a global minimum through which maneuvering objects move down the gradient of the potential field toward this global minimum. The proposed method constructs a potential field with two minima. The purpose of these two minima is to create a dual attraction between links rather than affecting each link by the preceding one through kinematic constraints.

Abstract

International audience; Air traffic management (ATM) aims at providing companies with a safe and ideally optimal aircraft trajectory planning. Air traffic controllers act on flight paths in such a way that no pair of aircraft come closer than the regulatory separation norm. With the increase of traffic, it is expected that the system will reach its limits in a near future: a paradigm change in ATM is planned with the introduction of trajectory based operations. This paper investigate a mean of producing realistic air routes from the output of an automated trajectory design tool. For that purpose, an entropy associated with a system of curves is defined and a mean of iteratively minimizing it is presented. The network produced is suitable for use in a semi-automated ATM system with human in the loop. Document type: Part of book or chapter of book

Abstract

International audience The two first CEAS (Council of European Aerospace Societies) Specialist Conferences on Guidance, Navigation and Control (CEAS EuroGNC) were held in Munich, Germany in 2011 and in Delft, The Netherlands in 2013. ONERA The French Aerospace Lab, ISAE (Institut Supérieur de l’Aéronautique et de l’Espace) and ENAC (Ecole Nationale de l’Aviation Civile) accepted the challenge of jointly organizing the 3rd edition. The conference aims at promoting new advances in aerospace GNC theory and technologies for enhancing safety, survivability, efficiency, performance, autonomy and intelligence of aerospace systems. It represents a unique forum for communication and information exchange between specialists in the fields of GNC systems design and operation, including air traffic management. This book contains the forty best papers and gives an interesting snapshot of the latest advances over the following topics:l Control theory, analysis, and designl Novel navigation, estimation, and tracking methodsl Aircraft, spacecraft, missile and UAV guidance, navigation, and controll Flight testing and experimental resultsl Intelligent control in aerospace applicationsl Aerospace robotics and unmanned/autonomous systemsl Sensor systems for guidance, navigation and controll Guidance, navigation, and control concepts in air traffic control systemsFor the 3rd CEAS Specialist Conference on Guidance, Navigation and Control the International Program Committee conducted a formal review process. Each paper was reviewed in compliance with standard journal practice by at least two independent and anonymous reviewers. The papers published in this book were selected from the conference proceedings based on the results and recommendations from the reviewers. Document type: Part of book or chapter of book

Abstract

This thesis targets the problem of motion planning for automated vehicles. As a prerequisite for their on-road deployment, automated vehicles must show an appropriate and reliable driving behavior in mixed traffic, i.e. alongside human drivers. Besides the uncertainties resulting from imperfect perception, occlusions and limited sensor range, also the uncertainties in the behavior of other traffic participants have to be considered. Related approaches for motion planning in mixed traffic often employ a deterministic problem formulation. The solution of such formulations is restricted to a single trajectory. Deviations from the prediction of other traffic participants are accounted for during replanning, while large uncertainties lead to conservative and over-cautious behavior. As a result of the shortcomings of these formulations in cooperative scenarios and scenarios with severe uncertainties, probabilistic approaches are pursued. Due to the need for real-time capability, however, a holistic uncertainty treatment often induces a strong limitation of the action space of automated vehicles. Moreover, safety and traffic rule compliance are often not considered. Thus, in this work, three motion planning approaches and a scenario-based safety approach are presented. The safety approach is based on an existing concept, which targets the guarantee that automated vehicles will never cause accidents. This concept is enhanced by the consideration of traffic rules for crossing and merging traffic, occlusions, limited sensor range and lane changes. The three presented motion planning approaches are targeted towards the different predominant uncertainties in different scenarios, while operating in a continuous action space. For non-interactive scenarios with clear precedence, a probabilistic approach is presented. The problem is modeled as a partially observable Markov decision process (POMDP). In contrast to existing approaches, the underlying assumption is that the prediction of the future progression of the uncertainty in the behavior of other traffic participants can be performed independently of the automated vehicle's motion plan. In addition to this prediction of currently visible traffic participants, the influence of occlusions and limited sensor range is considered. Despite its thorough uncertainty consideration, the presented approach facilitates planning in a continuous action space. Two further approaches are targeted towards the predominant uncertainties in interactive scenarios. In order to facilitate lane changes in dense traffic, a rule-based approach is proposed. The latter seeks to actively reduce the uncertainty in whether other vehicles willingly make room for a lane change. The generated trajectories are safe and traffic rule compliant with respect to the presented safety approach. To facilitate cooperation in scenarios without clear precedence, a multi-agent approach is presented. The globally optimal solution to the multi-agent problem is first analyzed regarding its ambiguity. If an unambiguous, cooperative solution is found, it is pursued. Still, the compliance of other vehicles with the presumed cooperation model is checked, and a conservative fallback trajectory is pursued in case of non-compliance. The performance of the presented approaches is shown in various scenarios with intersecting lanes, partly with limited visibility, as well as lane changes and a narrowing without predefined right of way.

Abstract

Tez (Doktora) -- İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, 2015 Thesis (PhD) -- İstanbul Technical University, Institute of Science and Technology, 2015 AUTOFLY-Aid olarak adlandırılan bu tez çalışması, dinamik 4-Boyutlu rota yönetimi ile çarpışmadan kaçınma ve verimli uçuş rotaları planlamaya yarayan yeni nesil uçuş karar destek algoritma ve cihazlarının geliştirilmesi ve kavramsal tasarımının gerçekleştirilmesini amaçlamıştır. Geliştirilen karar destek sistemleri halihazırda var olan kokpit içi çarpışmadan kaçınma sistemlerinin (bknz. TCAS) eksikliklerini gidermeyi vizyonlamanın ötesinde, uçuşta veri değişimi, sanal gerçeklik tabanlı karar destek, hızla değişen durumlar için otonom uçuş kontrolü sağlama gibi fonksiyonlara olanak sağlayan ek kavramsal aviyonikler ve prosedürler geliştirilmesi de amaçlanmıştır. AUTOFLY-Aid’in ana konseptleri; a) SESAR ve NextGen modernizasyonlarının 2020+ vizyonları ve ötesindeki havasahasının kokpit içerisinden algısının matematiksel olarak modellenmesi, b) anlık ve orta-mesafede kompozit bir hava sahasında hızla değişen durumlara karşı alternatifleriyle beraber uçulabilir rotalar ve manevralar üreten 4-boyutlu rota planlama algoritmalarının geliştirilmesi, c) değişen  durumlarda pilota görsel anlama ve durumsal farkındalık kazandıracak sanal gerçeklik karar destek sistemleri, otonom uçuş kontrolü sağlama ve bunun gibi yenilikçi prosedürler içeren bu uçuş otomasyonu sistemlerinin Boeing 737-800 Uçuş Simülatörü içerisine entegrasyonu ve testlerinin yapılmasıdır.  Tamamen merkezi olarak taktiksel seviyede uçuşa müdahale modelinden, daha etkin stratejik seviyede planlama yapma ve daha fazla otomasyon destekli ve daha aktif arayüzler içeren merkezcil olmaktan uzak taktik operasyonlar hem SESAR hem de NextGen gelecek paradigma değişimlerinde ana mesele olarak durmaktadır. Bu yeni nesil Hava Trafik Yönetimi (ATM) konseptleri “en iyi karar noktası”, “en iyi karar zamanı” ve “en iyi karar vericiyi” değerlendirilmesiyle insanın ATM sistemi içerisindeki rolünü ciddi şekilde değiştirecektir. Bu amaçlar doğrultusunda, AUTOFLY-Aid yerdeki hava trafik kontrolörünün bir takım iş yükü ve sorumluluklarını etkin bir şekilde kokpit içerisine taşımayı amaçlamıştır. Geliştirilmiş olan otomasyon sistemi sürekli olarak dinamik çevresel ve operasyonel değişkenleri izleyerek ya da yer sistemlerinden veri linkleri aracılığıyla toplayarak uçuş güvenliğini ve verimliliğini gözlemler ve dinamik uçuş rotası planlaması yapar. Bu sistem gerekli otomasyon seviyesini gerekli aksiyon sürelerini değerlendirerek  “en iyi karar vericiyi”, “nerede insan iyi, nerede makina iyi” sorgusu yaparak belirler. Orta seviye güvenlik modunda, pilot görsel karar destek sistemlerini kullanarak (örneğin sanal tünel içerisinde uçuş) en üst seviye durumsal farkındalık ile güvenli ve verimli uçuşunu gerçekleştirebilmektedir. Bu görsel karar destek sunumları kokpitin kendi duyargaları ve yer-hava arası veri paylaşımları ile edindiği bilgilerin bileşkesinden elde edilmektedir. Eğer gerekli reaksiyon süresi izin verir ise, pilot bu göstergeler üzerinden alternatif rota planları üretebilir, sonuçları değerlendirebilir, tekrar planlama talep edebilir. Anlık bir tehdit algısı oluştuğunda (anlık reaksiyon gerekli olduğu ya da geç kalınan reaksiyon tespiti olduğunda) otomasyon sistemi potansiyel kritik problemi (havada çarpışma, yere çarpma vb.) çözmek amacıyla uçuş kontrolünü ele geçirebilmektedir. Bu hibrid yaklaşım gerekli aksiyon zamanları değerlendirmesi yaparak bu şekilde bir otonomi seviyesi geçişlerini kontrol edebilmektedir. This thesis, namely, AUTOFLY-Aid Project, aims to develop and demonstrate novel flight deck automation support algorithms and tools for potential conflict avoidance and performance-optimal flight using "dynamic 4D trajectory management". The developed automation support system is envisioned not only to improve the primary shortcomings of existing on-board traffic collision avoidance systems (e.g. TCAS), but also to develop new conceptual add-on avionics and procedures enabling intent data exchange, decision support systems with augmented reality and flight control hand-over implementation in dynamically evolving scenarios. The main concepts which has been developed in AUTOFLY-Aid project are a) design and development of the mathematical models of the full composite airspace picture from the flight deck perspective, as seen/measured/informed by the aircraft flying in the sky of the SESAR and NextGen 2020+ vision and beyond, b) design and development of a dynamic 4D trajectory planning algorithm can generate at real-time flyable (i.e. dynamically and performance-wise feasible) alternative trajectories for both short-term and mid-term scale across the evolving stochastic composite airspace picture and c) development and testing of the automation support system on a Boeing 737-800 Flight Simulator with conceptual procedures, automated flight control implementations, and reality augmented based decision support demonstrations providing the flight crew with quantified and visual understanding of evolving situation. Evaluation from a purely centralized tactical intervention model towards a more strategic planning and progressive introduction of more autonomous and decentralized tactical operation with more proactive systems are key concepts in both NextGen and SESAR future ATM paradigm shift vision. Implementing of these new-generation ATM concepts will significantly change the human role in the ATM system by considering "best decision place", "best decision time" and "the best decision player". Through these objectives, AUTOFLY-Aid envisions to take some of the work off the controller by delegating some responsibility to flight decks in an efficient manner. The developed automation system offers persistent in-flight hazard and flight efficiency monitoring and tactical flight trajectory planning as a function of look-ahead time and dynamically changing environmental/operational conditions (and with uncertainty reduction in a feedback loop) obtained via both in-flight sense and ground-air data link. The automation system switches autonomy level according to the required response time in order to find "the best decision player" through asking "where are men better at, where are machines better at". In mid-term safety assurance mode, it is expected that pilot uses a visual decision support tools (e.g. tunnel-in-the-sky visualization) with fully situational awareness for safe and performance optimal flight. These visual advisories are generated by fusing all tactical level information feed from both on-board sensing and ground-air data/information exchange. If the reaction time permits, the system allows pilots to freely switch between the generated alternative plans, modify the solution or request re-planning. In any case of the immediate potential threat is detected (i.e. immediate response is required or late response is detected), the autonomous system may take over the flight control to solve safety-critical situation happening "almost surely" (e.g. midair collision, terrain collision etc.). This hybrid approach allows dynamic role assignment by switching between defined autonomy level modes in terms of the "required response time". Doktora PhD