Air traffic management (ATM) under its current paradigm is reaching its structural limits considering the continuously growing demand. The need for a decrease in traffic workload opens numerous problems for optimization, from capacity balancing to conflict solving, using many different degrees of freedom, such as re-routing, flight-level changes, or ground-holding schemes. These problems are usually of a large dimension (there are 30 000 daily flights in Europe in the year 2012) and highly combinatorial, hence challenging for current problem solving technologies. We give brief tutorials on ATM and constraint programming (CP), and survey the literature on deploying CP technology for modelling and solving combinatorial problems that occur in an ATM context.

As acknowledged by the SESAR (Single European Sky ATM (Air Traffic Management) Research) program, current Air Traffic Control (ATC) systems must be drastically improved to accommodate the predicted traffic growth in Europe. In this context, the Episode 3 project aims at assessing the performance of new ATM concepts, like 4D-trajectory planning and strategic deconfliction.

One of the bottlenecks impeding ATC performances is the hourly capacity constraints defined on each en-route ATC sector to limit the rate of aircraft. Previous works were mainly focused on optimizing the current ground holding slot allocation process devised to satisfy these constraints. We propose to estimate the cost of directly solving all conflicts in the upper airspace with ground holding, provided that aircraft were able to follow their trajectories accurately.

We present a Constraint Programming model of this large-scale combinatorial optimization problem and the results obtained with the FaCiLe (Functional Constraint Library). We study the effect of uncertainties on the departure time and estimate the cost of improving the robustness of our solutions with the Complete Air Traffic Simulator (CATS). Encouraging results were obtained without uncertainty but the costs of robust solutions are prohibitive. Our approach may however be improved, for example, with a prior flight level allocation and the dynamic resolution of remaining conflicts with one of CATS’ modules.

This study addresses the kinematics of a six-degrees-of-freedom parallel manipulator whose moving platform is a regular triangular prism. The moving and fixed platforms are connected to each other by means of two identical parallel manipulators. Simple forward kinematics and reduced singular regions are the main benefits offered by the proposed parallel manipulator. The Input–Output equations of velocity and acceleration are systematically obtained by resorting to reciprocal-screw theory. A case study, which is verified with the aid of commercially available software, is included with the purpose to exemplify the application of the method of kinematic analysis.

Morphological analysis and disambiguation are crucial stages in a variety of natural language processing applications, especially when languages with complex morphology are concerned. We present a system which disambiguates the output of a morphological analyzer for Hebrew. It consists of several simple classifiers and a module that combines them under the constraints imposed by the analyzer. We explore several approaches to classifier combination, as well as a back-off mechanism that relies on a large unannotated corpus. Our best result, around 83 percent accuracy, compares favorably with the state of the art on this task.

This paper proposes a precise outdoor localization algorithm with the integration of Global Positioning System (GPS) and Inertial Navigation System (INS). To achieve precise outdoor localization, two schemes are recently proposed, which consist of de-noising the INS signals and fusing the GPS and INS data. To reduce the noise from the internal INS sensors, the discrete wavelet transform and variable threshold method are utilized, and to fuse the GPS and INS data while filtering out the noise caused by the acceleration, deceleration, and unexpected slips, the Unscented Particle Filter (UPF) is adopted. Conventional de-noising methods mainly employ a combination of low-pass and high-pass filters, which results in signal distortion. This newly proposed system also utilizes the vibration information of the actuator according to the fluctuations of the velocity to minimize the signal distortion. The UPF resolves the nonlinearities of the actuator and non-normal distributions of the noise more effectively than the conventional particle filter (PF) or Extended Kalman Filter–PF. The superiority of the proposed algorithm was verified through experiments, and the results are reported.

Safety-critical software must be thoroughly verified before being exploited in commercial applications. In particular, any TCAS (Traffic Alert and Collision Avoidance System) implementation must be verified against safety properties extracted from the anti-collision theory that regulates the controlled airspace. This verification step is currently realized with manual code reviews and testing. In our work, we explore the capabilities of Constraint Programming for automated software verification and testing. We built a dedicated constraint solving procedure that combines constraint propagation with Linear Programming to solve conditional disjunctive constraint systems over bounded integers extracted from computer programs and safety properties. An experience we made on verifying a publicly available TCAS component implementation against a set of safety-critical properties showed that this approach is viable and efficient.

This paper considers the problem of generating conflict-free movement schedules for a set of vehicles that are operating simultaneously in a common airspace. In both civilian air traffic management and military air campaign planning contexts, it is crucial that the movements of different vehicles be coordinated so as to avoid collisions and near misses. Our approach starts from a view of airspace management as a 4D resource allocation problem, where the space in which vehicles must maneuver is itself managed as a capacitated resource. We introduce a linear octree representation of airspace capacity to index vector-based vehicle routes and efficiently detect regions of potential conflict. Generalizing the notion of contention-based search heuristics, we next define a scheduling algorithm that proceeds by first solving a relaxed version of the problem to construct a spatial capacity profile (represented as an octree), and then using spatio-temporal regions where demand exceeds capacity to make conflict-avoiding vehicle routing and scheduling decisions. We illustrate the utility of this basic representation and search algorithm in two ways. First, to demonstrate the overall viability of the approach, we present experimental results using data representing a realistically sized air campaign planning domain. Second, we define a more abstract notion of ‘encounter set’, which tolerates some amount of conflict on the assumption that on-board deconfliction processes can take appropriate avoidance maneuvers at execution time, and show that generation of this more abstract form of predictive guidance can be obtained without loss in computational efficiency.

Spelling errors in digital documents are often caused by operational and cognitive mistakes, or by the lack of full knowledge about the language of the written documents. Computer-assisted solutions can help to detect and suggest replacements. In this paper, we present a new string distance metric for the Persian language to rank respelling suggestions of a misspelled Persian word by considering the effects of keyboard layout on typographical spelling errors as well as the homomorphic and homophonic aspects of words for orthographical misspellings. We also consider the misspellings caused by disregarded diacritics. Since the proposed string distance metric is custom-designed for the Persian language, we present the spelling aspects of the Persian language such as homomorphs, homophones, and diacritics. We then present our statistical analysis of a set of large Persian corpora to identify the causes and the types of Persian spelling errors. We show that the proposed string distance metric has a higher mean average precision and a higher mean reciprocal rank in ranking respelling candidates of Persian misspellings in comparison with other metrics such as the Hamming, Levenshtein, Damerau–Levenshtein, Wagner–Fischer, and Jaro–Winkler metrics.