Comme les robots effectuent de plus en plus de tâches en interaction avec l'homme ou dans un environnement humain, ils doivent assurer la sécurité et le confort des hommes. Dans ce contexte, le robot doit adapter son comportement et agir en fonction des évolutions de l'environnement et des activités humaines. Les robots développés sur la base de l'apprentissage ou d'un planificateur de mouvement ne sont pas en mesure de réagir assez rapidement, c'est pourquoi nous proposons d'introduire un contrôleur de trajectoire intermédiaire dans l'architecture logicielle entre le contrôleur bas niveau et le planificateur de plus haut niveau. Le contrôleur de trajectoire que nous proposons est basé sur le concept de générateur de trajectoire en ligne (OTG), il permet de calculer des trajectoires en temps réel et facilite la communication entre les différents éléments, en particulier le planificateur de chemin, le générateur de trajectoire, le détecteur de collision et le contrôleur. Pour éviter de replanifier toute une trajectoire en réaction à un changement induit par un humain, notre contrôleur autorise la déformation locale de la trajectoire et la modification de la loi d'évolution pour accélérer ou décélérer le mouvement. Le contrôleur de trajectoire peut également commuter de la trajectoire initiale vers une nouvelle trajectoire. Les fonctions polynomiales cubiques que nous utilisons pour décrire les trajectoires fournissent des mouvements souples et de la flexibilité sans nécessiter de calculs complexes. De plus, les algorithmes de lissage que nous proposons permettent de produire des mouvements esthétiques ressemblants à ceux des humains. Ce travail, mené dans le cadre du projet ANR ICARO, a été intégré et validé avec les robots KUKA LWR de la plate-forme robotique du LAAS-CNRS. / In order to perform a large variety of tasks in interaction with human or in human environments, a robot needs to guarantee safety and comfort for humans. In this context, the robot shall adapt its behavior and react to the environment changes and human activities. The robots based on learning or motion planning are not able to adapt fast enough, so we propose to use a trajectory controller as an intermediate control layer in the software structure. This intermediate layer exchanges information with the low level controller and the high level planner. The proposed trajectory controller, based on the concept of Online Trajectory Generation (OTG), allows real time computation of trajectories and easy communication with the different components, including path planner, trajectory generator, collision checker and controller. To avoid the replan of an entire trajectory when reacting to a human behaviour change, the controller must allow deforming locally a trajectory or accelerate/decelerate by modifying the time function. The trajectory controller must also accept to switch from an initial trajectory to a new trajectory to follow. Cubic polynomial functions are used to describe trajectories, they provide smoothness, flexibility and computational simplicity. Moreover, to satisfy the objective of aesthetics, smoothing algorithm are proposed to produce human-like motions. This work, conducted as part of the ANR project ICARO, has been integrated and validated on the KUKA LWR robot platform of LAAS-CNRS.

This degree project addresses poaching challenges by employing predictive analysis of animal movements and their correlation with the dynamic environment using a machine learning approach. The goal is to provide accurate predictions of animal movements, enabling rangers to intercept potential threats and safeguard wildlife from snares. A wide analysis considers previous studies on animal movements and both animal and environment data availability. To efficiently represent the dynamic environment and correlate it with animal movement data, accurate matching of environment variables to each animal measurement is crucial. We selected multiple environment datasets to capture a sufficient amount ofenvironmental properties. Due to practical constraints, daily representation of the environment is not achievable, and weekly mean or monthly mode values are used instead. Data insights are obtained through the training of a regression neural network using the filtered environmental and animal movement data. The results highlight the significant role ofenvironmental features in predicting animal movements, emphasizing their importance for accurate predictions. Despite some offset and few erroneous predictions, a strong similarity between animal predicted trajectory and animal true trajectory was achieved, indicating that the model is capable to capture general patterns and to correctly tune in predictions of detailed movements as well. The overall offset of the trajectories is still a weak point of this model, but it may just indicate the presence of some underlying systematic error that can be corrected through further work. The integration of such a developed prediction model into existing frameworks could assist law enforcingauthorities in preventing poaching activities.

In this dissertation, we focus on the strategy that places and stabilizes the path of an aerial drogue, which is towed by a mothership aircraft using a long flexible cable, onto a horizontally flat orbit by maneuvering the mothership in the presence of wind. To achieve this goal, several studies for towed cable systems are conducted, which include the dynamic modeling for the cable, trajectory generation strategies for the mothership, trajectory-tracking control law design, and simulation and flight test implementations. First, a discretized approximation method based on finite element and lumped mass is employed to establish the mathematical model for the towed cable system in the simulation. Two approaches, Gauss's Principle and Newton's second law, are utilized to derive the equations of motion for inelastic and elastic cables, respectively. The preliminary studies for several key parameters of the system are conducted to learn their sensitivities to the system motion in the steady state. Flight test results are used to validate the mathematical model as well as to determine an appropriate number of cable links. Furthermore, differential flatness and model predictive control based methods are used to produce a mothership trajectory that leads the drogue onto a desired orbit. Different desired drogue orbits are utilized to generate required mothership trajectories in different wind conditions. The trajectory generation for a transitional flight in which the system flies from a straight and level flight into a circular orbit is also presented. The numerical results are presented to illustrate the required mothership orbits and its maneuverability in different wind conditions. A waypoint following based strategy for mothership to track its desired trajectory in flight test is developed. The flight test results are also presented to illustrate the effectiveness of the trajectory generation methods. In addition, a nonlinear time-varying feedback control law is developed to regulate the mothership to follow the desired trajectory in the presence of wind. Cable tensions and wind disturbance are taken into account in the design model and Lyapunov based backstepping technique is employed to develop the controller. The mothership tracking error is proved to be capable of exponentially converging to an ultimate bound, which is a function of the upper limit of the unknown component of the wind. The simulation results are presented to validate the controller. Finally, a trajectory-tracking strategy for unmanned aerial vehicles is developed where the autopilot is involved in the feedback controller design. The trajectory-tracking controller is derived based on a generalized design model using Lyapunov based backstepping. The augmentations of the design model and trajectory-tracking controller are conducted to involve the autopilot in the closed-loop system. Lyapunov stability theory is used to guarantee the augmented controller is capable of driving the vehicle to exponentially converge to and follow the desired trajectory with the other states remaining bounded. Numerical and Software-In-the-Loop simulation results are presented to validate the augmented controller. This method presents a framework of implementing the developed trajectory-tracking controllers for unmanned aerial vehicles without any modification to the autopilot.

A large proportion of movement in urban area is constrained to a road network such as pedestrian, bicycle and vehicle. That movement information is commonly collected by Global Positioning System (GPS) sensor, which has generated large collections of trajectories. A contiguous sequential pattern (CSP) in these trajectories represents a certain number of objects traversing a sequence of spatially contiguous edges in the network, which is an intuitive way to study regularities in network-constrained movement. CSPs are closely related to route choices and traffic flows and can be useful in travel demand modeling and transportation planning. However, the efficient and scalable extraction of CSPs and effective visualization of the heavily overlapping CSPs are remaining challenges. To address these challenges, the thesis develops two algorithms and a visual analytics system. Firstly, a fast map matching (FMM) algorithm is designed for matching a noisy trajectory to a sequence of edges traversed by the object with a high performance. Secondly, an algorithm called bidirectional pruning based closed contiguous sequential pattern mining (BP-CCSM) is developed to extract sequential patterns with closeness and contiguity constraint from the map matched trajectories. Finally, a visual analytics system called sequential pattern explorer for trajectories (SPET) is designed for interactive mining and visualization of CSPs in a large collection of trajectories. Extensive experiments are performed on a real-world taxi trip GPS dataset to evaluate the algorithms and visual analytics system. The results demonstrate that FMM achieves a superior performance by replacing repeated routing queries with hash table lookups. BP-CCSM considerably outperforms three state-of-the-art algorithms in terms of running time and memory consumption. SPET enables the user to efficiently and conveniently explore spatial and temporal variations of CSPs in network-constrained movement. / <p>QC 20171122</p>

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If sufficiently persistent, semi-volatile organic contaminants (SVOCs) can travel long distances through the atmosphere from their points of release and become concentrated in cold, remote regions. As air is sampled for SVOCs to establish both their presence and the success of emission reduction efforts, it becomes helpful to determine sampling site proximity to sources and the origin of the sampled air masses. Comparing three increasingly sophisticated methods for quantifying source proximity of sampling locations, it was judged necessary to account for the actual history of the sampled air through construction of an airshed, especially if wind is highly directional and population distribution is very non-uniform. The airshed concept was improved upon by introducing a ‘geodesic’ grid of equally spaced cells, rather than a simple latitude/longitude grid, to avoid distortion near Earth’s poles and to allow for the comparison of airshed shapes. Assuming that a perfectly round airshed reveals no information about sources allows the significance of each cell of an airshed to be judged based on its departure from roundness. Combining air-mass histories with a 2 year-long series of SVOC air concentrations at Little Fox Lake in Canada’s Yukon Territory did not identify distinct source regions for most analytes, although γ-hexachlorocyclohexane appears to originate broadly in north-eastern Russia and/or Alaska. Based on this remoteness from sources, the site is judged to be well suited to monitor changes in the hemispheric background concentrations of SVOCs. A model-based exploration revealed wet-gaseous deposition as the dominant process responsible for cold-trapping SVOCs in mountain soils. Such cold trapping is particularly effective if precipitation rate increases with altitude and if temperature differences along the mountain are large. Considerable sensitivity of the modeled extent of cold-trapping to parameters as diverse as scale, mean temperature, atmospheric particle concentration and time relative to emission maxima is consistent with the wide variety of observed enrichment behaviour. Concentration gradients of polycyclic aromatic hydrocarbons and polychlorinated biphenyls in air and soil measured on four Western Canadian mountains with variable distance from sources revealed source proximity as the main driver of concentrations at both the whole-mountain scale and along individual mountain transects.

If sufficiently persistent, semi-volatile organic contaminants (SVOCs) can travel long distances through the atmosphere from their points of release and become concentrated in cold, remote regions. As air is sampled for SVOCs to establish both their presence and the success of emission reduction efforts, it becomes helpful to determine sampling site proximity to sources and the origin of the sampled air masses. Comparing three increasingly sophisticated methods for quantifying source proximity of sampling locations, it was judged necessary to account for the actual history of the sampled air through construction of an airshed, especially if wind is highly directional and population distribution is very non-uniform. The airshed concept was improved upon by introducing a ‘geodesic’ grid of equally spaced cells, rather than a simple latitude/longitude grid, to avoid distortion near Earth’s poles and to allow for the comparison of airshed shapes. Assuming that a perfectly round airshed reveals no information about sources allows the significance of each cell of an airshed to be judged based on its departure from roundness. Combining air-mass histories with a 2 year-long series of SVOC air concentrations at Little Fox Lake in Canada’s Yukon Territory did not identify distinct source regions for most analytes, although γ-hexachlorocyclohexane appears to originate broadly in north-eastern Russia and/or Alaska. Based on this remoteness from sources, the site is judged to be well suited to monitor changes in the hemispheric background concentrations of SVOCs. A model-based exploration revealed wet-gaseous deposition as the dominant process responsible for cold-trapping SVOCs in mountain soils. Such cold trapping is particularly effective if precipitation rate increases with altitude and if temperature differences along the mountain are large. Considerable sensitivity of the modeled extent of cold-trapping to parameters as diverse as scale, mean temperature, atmospheric particle concentration and time relative to emission maxima is consistent with the wide variety of observed enrichment behaviour. Concentration gradients of polycyclic aromatic hydrocarbons and polychlorinated biphenyls in air and soil measured on four Western Canadian mountains with variable distance from sources revealed source proximity as the main driver of concentrations at both the whole-mountain scale and along individual mountain transects.

<div>Optimization of atmospheric flight control has long been performed on the ground, prior to mission flight due to large computational requirements used to solve non-linear programming problems. Onboard trajectory optimization enables the creation of new reference trajectories and updates to guidance coefficients in real time. This thesis summarizes the methods involved in solving optimal control problems in real time using convexification and Sequential Convex Programming (SCP). The following investigation provided insight in assessing the use of state of the art SCP optimization architectures and convexification of the hypersonic equations of motion[ 1 ]–[ 3 ] with different control schemes for the purposes of enabling on-board trajectory optimization capabilities.</div><div>An architecture was constructed to solve convexified optimal control problems using direct population of sparse matrices in triplet form and an embedded conic solver to enable rapid turn around of optimized trajectories. The results of this show that convexified optimal control problems can be solved quickly and efficiently which holds promise in autonomous trajectory design to better overcome unexpected environments and mission parameter changes. It was observed that angle of attack control problems can be successfully convexified and solved using SCP methods. However, the use of multiple coupled controls is not guaranteed to be successful with this method when they act in the same plane as one another. The results of this thesis demonstrate that state of the art SCP methods have the capacity to enable onboard trajectory optimization with both angle of attack control and bank angle control schemes.</div><div><br></div>

There is a need for a fuel-optimal required time of arrival (RTA) mode for aircraft flight management systems capable of enabling controlled time of arrival functionality in the presence of wind speed forecast uncertainty. A computationally tractable two-stage stochastic algorithm utilizing a data-driven, location-specific forecast uncertainty model to generate forecast uncertainty scenarios is proposed as a solution. Three years of Aircraft Communications Addressing and Reporting Systems (ACARS) wind speed reports are used in conjunction with corresponding wind speed forecasts from the Rapid Update Cycle (RUC) forecast product to construct an inhomogeneous Markov model quantifying forecast uncertainty characteristics along specific route through the national airspace system. The forecast uncertainty modeling methodology addresses previously unanswered questions regarding the regional uncertainty characteristics of the RUC model, and realizations of the model demonstrate a clear tendency of the RUC product to be positively biased along routes following the normal contours of the jet stream. A two-stage stochastic algorithm is then developed to calculate the fuel optimal stage one cruise speed given a required time of arrival at a destination waypoint and wind forecast uncertainty scenarios generated using the inhomogeneous Markov model. The algorithm utilizes a quadratic approximation of aircraft fuel flow rate as a function of cruising Mach number to quickly search for the fuel-minimum stage one cruise speed while keeping computational footprint small and ensuring RTA adherence. Compared to standard approaches to the problem utilizing large scale linear programming approximations, the algorithm performs significantly better from a computational complexity standpoint, providing solutions in fractional power time while maintaining computational tractability in on-board systems.

Les nouveaux enjeux environnementaux motivent la recherche par les acteurs de l'industrie aéronautique de méthodes de calcul de trajectoires optimales. Les contributions de cette thèse se déclinent selon trois axes. Dans un premier temps, plusieurs techniques d'optimisation de trajectoire avion sont comparées sur un cas simple traité dans la bibliographie universitaire. Puis, un modèle réduit pour prendre en compte le niveau des nuisances sonores dans un algorithme d'optimisation de trajectoire est proposé.Enfin, un problème d'optimisation de trajectoire de montée d'un avion civil est résolu par une approche directe. Les spécificités du problème consistent en la présence de plusieurs phases au sein de la trajectoire, la formulation de contraintes égalités à vérifier par des composantes du vecteur d'état sur des intervalles de temps et enfin la difficulté d'intégration numérique du modèle de bruit. / Forthcoming environmental challenges stimulate the development of trajectory optimization methods by aeronautical actors. This contribution consists in three parts. First, several trajectory optimization techniques are compared. The comparison is based on a simple academic problem. After that, a model is proposed for considering noise nuisance level in the framework of trajectory optimization. Finally, the optimization problem of an ascent phase of a civil aircraft is solved using a direct approach. The specific issues of the problem are tackled with a general formulation. They consist in the presence of several phases along the trajectory, running state equality constraints and tough numerical integration of the noise model.