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This dissertation aims to realize the goal of developing robust control solutions that can enable legged robots to navigate complex unknown environments. The idea of creating articulated-legged machines that can mimic animal locomotion has fueled the imagination of many researchers. These legged robots are designed to assist humans in their day-to-day tasks and challenging scenarios such as monitoring remote, inhospitable environments, disaster response, and other dangerous environments. Despite several decades of research, legged robots have yet to reach the dexterity or dynamic stability needed for real-world deployments. A fundamental gap exists in the understanding and development of reliable and scalable algorithms required for the real-time planning and control of legged robots. The overarching goal of this thesis is to formally develop computationally tractable, robust controllers based on nonlinear hybrid systems theory, model predictive control, and optimization for the real-time planning and control of agile locomotion in quadrupedal robots. Toward this objective, this thesis first investigates layered control architectures. In particular, we propose a two-level hierarchical control architecture in which the higher level is based on a reduced-order model predictive control (MPC), and the lower level is based on a full-order quadratic programming (QP) based virtual constraints controller. Specifically, two MPC architectures are explored: 1) An event-based MPC scheme that generates the optimal center of mass (COM) trajectories using a reduced-order linear inverted pendulum (LIP) model, and 2) A time-based MPC scheme that computes the optimal COM and ground reaction forces (GRF) using the reduced-order single rigid body (SRB) dynamics model. The optimal COM trajectories in the event-based MPC and the optimal COM trajectories, along with the ground reaction forces in the time-based MPC, are then tracked by the low-level virtual constraints controller. The event-based MPC scheme is numerically validated on the Vision 60 platform in a physics-based simulation environment. It has significantly reduced the computational burden associated with real-time planning-based MPC schemes. However, owing to the quasi-static nature of the optimal trajectories generated by the LIP model, we explored a time-based MPC scheme using Single Rigid Body Dynamics. This time-based MPC scheme is also numerically validated using the mathematical model of the A1 quadrupedal robot. Most MPC schemes use a reduced-order model to generate optimal trajectories. However, the abstraction and unmodeled dynamics in template models significantly increase the gap between reduced- and full-order models, limiting the robot's full scope and potential. In the second part of the thesis, we aim to develop a computationally tractable robust model predictive control (RMPC) scheme based on convex QPs to bridge this gap. The RMPC framework considers the single rigid body model subject to a set of unmodeled dynamics and plans for the optimal reduced-order trajectory and GRFs. The generated optimal GRFs of the high-level RMPC are then mapped to the full-order model using a low-level nonlinear controller based on virtual constraints and QP. The key innovation of the proposed RMPC framework is that it allows the integration of the hierarchical controller with Reinforcement Learning (RL) techniques to train a neural network to compute the vertices of the uncertainty set numerically. The proposed hierarchical control algorithm is validated numerically and experimentally for robust and blind locomotion of the A1 quadrupedal robot on different indoor and outdoor terrains and at different speeds. The numerical analysis of the RMPC suggests significant improvement in the performance of the rough terrain locomotion compared to the nominal MPC. In particular, the proposed RMPC algorithm outperforms the nominal MPC by over 60% during rough terrain locomotion over 550 uneven terrains. Our experimental studies also indicate a significant reduction in the gap between the reduced full-order models by comparing the desired and actual GRFs. Finally, the last part of the thesis presents a formal approach for synthesizing robust $mathcal{H}_2$- and $mathcal{H}_infty$-optimal MPCs to stabilize the periodic locomotion of legged robots. The proposed algorithm builds on the existing optimization-based control stack. We outline the set of conditions under which the closed-loop nonlinear dynamics around a periodic orbit can be transformed into a linear time-invariant (LTI) system using Floquet theory. We then outline an approach to systematically generate parameterized $mathcal{H}_2$- and $mathcal{H}_infty$- robust controllers using linear matrix inequalities (LMIs). We subsequently established a set of conditions guaranteeing the existence of such robust optimal controllers. The proposed $mathcal{H}_2$- and $mathcal{H}_infty$-optimal MPCs are extensively validated both numerically and experimentally for the robust locomotion of the A1 quadrupedal robot subject to various external disturbances and uneven terrains. Our numerical analysis suggests a significant improvement in the performance of robust locomotion compared to the nominal MPC. / Doctor of Philosophy / Legged robots have always been envisioned to work alongside humans, assisting them in mundane day-to-day tasks to challenging scenarios such as monitoring remote locations, planetary exploration, and supporting relief programs in disaster situations. Furthermore, research into legged locomotion can aid in designing and developing powered prosthetic limbs and exoskeletons. With these advantages in mind, several researchers have created sophisticated-legged robots and even more complicated algorithms to control them. Despite this, a significant gap exists between the agility, mobility, and dynamic stability shown by the existing legged robots and their biological counterparts. To work alongside humans, legged robots have to interact with complex environments and deal with uncertainties in the form of unplanned contacts and unknown terrains. Developing robust control solutions to accommodate disturbances explicitly marks the first step towards safe and reliable real-world deployment of legged robots. Toward this objective, this thesis aims to establish a formal foundation to develop computationally tractable robust controllers for the real-time planning and control of legged robots. Initial investigations in this thesis report on the use of layered control architectures, specifically event-based and time-based Model Predictive Control(MPC) schemes. These layered control architectures consist of an MPC scheme built around a reduced-order model at the high level and a virtual constraints-based nonlinear controller at the low level. Using these layered control architectures, this thesis proposed two robust control solutions to improve the rough terrain locomotion of legged robots. The first proposed robust control solution aims to mitigate one of the issues of layered control architecture. In particular, layered control architectures rely on a reduced order model at the high level to remain computationally tractable. However, the approximation of fullorder models with reduced-order models limits the full scope and potential of the robot. The proposed algorithm aims to bridge the gap between reduced- and full-order models with the integration of model-free Reinforcement Learning (RL) techniques. The second algorithm proposes a formal approach to generate robust optimal control solutions that can explicitly accommodate the disturbances and stabilize periodic legged locomotion. Under some mild conditions, the MPC control solution is analyzed, and an auxiliary feedback control solution that can handle disturbances explicitly is proposed. The thesis also theoretically establishes the sufficient conditions for the existence of such controllers. Both the proposed control solutions are extensively validated using numerical simulations and experiments using an A1 quadrupedal robot as a representative example.

No / There is an increasing interest in modelling of agents interacting as crowd and a simulation of such scenarios that map to real-life situations. This paper presents a generic state-based abstract model for crowd behaviour that can be mapped onto different agent-based systems. In particular, the abstract model is mapped into the simulation framework NetLogo. We have used the model to simulate a real-life case study of high density diverse crowd such as the Hajj ritual at the mosque in Mecca (Makkah). The computational model is based on real data extracted from videos of the ritual. We also present a methodology for extracting significant data, parameters, and patterns of behaviour from real-world videos that has been used as an early stage validation to demonstrate that the obtained simulations are realistic.

This thesis synthesizes general and scalable distributed nonlinear control algorithms with application to legged robots. It explores both naturally decentralized problems in legged locomotion, such as the collaborative control of human-lower extremity prosthesis and the decomposition of high-dimensional controllers of a naturally centralized problem into a net- work of low-dimensional controllers while preserving equivalent performance. In doing so, strong nonlinear interaction forces arise, which this thesis considers and sufficiently addresses. It generalizes to both symmetric and asymmetric combinations of subsystems. Specifically, this thesis results in two distinct distributed control algorithms based on the decomposition approach. Towards synthesizing the first algorithm, this thesis presents a formal foundation based on de- composition, Hybrid Zero Dynamics (HZD), and scalable optimization to develop distributed controllers for hybrid models of collaborative human-robot locomotion. This approach con- siders a centralized controller and then decomposes the dynamics and parameterizes the feedback laws to synthesize local controllers. The Jacobian matrix of the Poincaré map with local controllers is studied and compared with the centralized ones. An optimization problem is then set up to tune the parameters of the local controllers for asymptotic stability. It is shown that the proposed approach can significantly reduce the number of controller parameters to be optimized for the synthesis of distributed controllers, deeming the method computationally tractable. To evaluate the analytical results, we consider a human amputee with the point of separation just above the knee and assume the average physical parameters of a human male. For the lower-extremity prosthesis, we consider the PRleg, a powered knee-ankle prosthetic leg, and together, they form a 19 Degrees of Freedom (DoF) model. A multi-domain hybrid locomotion model is then employed to rigorously assess the performance of the afore-stated control algorithm via numerical simulations. Various simulations involving the application of unknown external forces and altering the physical parameters of the human model unbeknownst to the local controllers still result in stable amputee loco- motion, demonstrating the inherent robustness of the proposed control algorithm. In the later part of this thesis, we are interested in developing distributed algorithms for the real-time control of legged robots. Inspired by the increasing popularity of Quadratic programming (QP)-based nonlinear controllers in the legged locomotion community due to their ability to encode control objectives subject to physical constraints, this thesis exploits the idea of distributed QPs. In particular, this thesis presents a formal foundation to systematically decompose QP-based centralized nonlinear controllers into a network of lower-dimensional local QPs. The proposed approach formulates a feedback structure be- tween the local QPs and leverages a one-step communication delay protocol. The properties of local QPs are analyzed, wherein it is established that their steady-state solutions on periodic orbits (representing gaits) coincide with that of the centralized QP. The asymptotic convergence of local QPs' solutions to the steady-state solution is studied via Floquet theory. Subsequently, to evaluate the effectiveness of the analytical results, we consider an 18 DoF quadrupedal robot, A1, as a representative example. The network of distributed QPs mentioned earlier is condensed to two local QPs by considering a front-hind decomposition scheme. The robustness of the distributed QP-based controller is then established through rigorous numerical simulations that involve exerting unmodelled external forces and intro- ducing unknown ground height variations. It is further shown that the proposed distributed QPs have reduced sensitivity to noise propagation when compared with the centralized QP. Finally, to demonstrate that the resultant distributed QP-based nonlinear control algorithm translates equivalently well to hardware, an extensive set of blind locomotion experiments on the A1 robot are undertaken. Similar to numerical simulations, unknown external forces in the form of aggressive pulls and pushes were applied, and terrain uncertainties were introduced with the help of arbitrarily displaced wooden blocks and compliant surfaces. Additionally, outdoor experiments involving a wide range of terrains such as gravel, mulch, and grass at various speeds up to 1.0 (m/s) reiterate the robust locomotion observed in numerical simulations. These experiments also show that the computation time is significantly dropped when the distributed QPs are considered over the centralized QP. / Doctor of Philosophy / Inspiration from animals and human beings has long driven the research of legged loco- motion and the subsequent design of the robotic counterparts: bipedal and quadrupedal robots. Legged robots have also been extended to assist human amputees with the help of powered prostheses and aiding people with paraplegia through the development of exoskeleton suits. However, in an effort to capture the same robustness and agility demonstrated by nature, our design abstractions have become increasingly complicated. As a result, the en- suing control algorithms that drive and stabilize the robot are equivalently complicated and subjected to the curse of dimensionality. This complication is undesirable as failing to compute and prescribe a control action quickly destabilizes and renders the robot uncontrollable. This thesis addresses this issue by seeking nature for inspiration through a different perspective. Specifically, through some earlier biological studies on cats, it was observed that some form of locality is implemented in the control of animals. This thesis extends this observation to the control of legged robots by advocating an unconventional solution. It proposes that a high-dimensional, single-legged agent be viewed as a virtual composition of multiple, low-dimensional subsystems. While this outlook is not new and forms precedent to the vast literature of distributed control, the focus has always been on large-scale systems such as power networks or urban traffic networks that preserve sparsity, mathematically speaking. On the contrary, legged robots are underactuated systems with strong interaction forces acting amongst each subsystem and dense mathematical structures. This thesis considers this problem in great detail and proposes developments that provide theoretical stability guarantees for the distributed control of interconnected legged robots. As a result, two distinctly different distributed control algorithms are formulated. We consider a naturally decentralized structure appearing in the form of a human-lower extremity prosthesis to synthesize distributed controllers using the first control algorithm. Subsequently, the resultant local controllers are rigorously validated through extensive full- order simulations. In order to validate the second algorithm, this thesis considers the problem of quadrupedal locomotion as a representative example. It assumes for the purposes of control synthesis that the quadruped is comprised of two subsystems separated at the geometric center, resulting in a front and hind subsystem. In addition to rigorous validation via numerical simulations, in the latter part of this thesis, to demonstrate that distributed controllers preserve practicality, rigorous and extensive experiments are undertaken in indoor and outdoor settings on a readily available quadrupedal robot A1.

This dissertation aims to realize the goal of deploying legged robots that cooperatively walk to transport objects in complex environments. More than half of the Earth's continent is unreachable to wheeled vehicles---this motivates the deployment of collaborative legged robots to enable the accessibility of these environments and thus bring robots into the real world. Although significant theoretical and technological advances have allowed the development of distributed controllers for complex robot systems, existing approaches are tailored to the modeling and control of multi-agent systems composed of collaborative robotic arms, multi-fingered robot hands, aerial vehicles, and ground vehicles, but not collaborative legged agents. Legged robots are inherently unstable, unlike most of the systems where these algorithms have been deployed. Models of cooperative legged robots are further described by high-dimensional, underactuated, and complex hybrid dynamical systems, which complicate the design of control algorithms for coordination and motion control. There is a fundamental gap in knowledge of control algorithms for safe motion control of these inherently unstable hybrid dynamical systems, especially in the context of collaborative work. The overarching goal of this dissertation is to create a formal foundation based on scalable optimization and robust and nonlinear control to develop distributed and hierarchical feedback control algorithms for cooperative legged robots to transport objects in complex environments. We first develop a hierarchical nonlinear control algorithm, based on model predictive control (MPC), quadratic programming (QP), and virtual constraints, to generate and stabilize locomotion patterns in a real-time manner for dynamical models of single-agent quadrupedal robots. The higher level of the proposed control scheme is developed based on an event-based MPC that computes the optimal center of mass (COM) trajectories for a reduced-order linear inverted pendulum (LIP) model subject to the feasibility of the net ground reaction force (GRF). QP-based virtual constraint controllers are developed at the lower level of the proposed control scheme to impose the full-order dynamics to track the optimal trajectories while having all individual GRFs in the friction cone. The analytical results are numerically verified to demonstrate stable and robust locomotion of a 22 degree of freedom (DOF) quadrupedal robot, in the presence of payloads, external disturbances, and ground height variations. We then present a hierarchical nonlinear control algorithm for the real-time planning and control of cooperative locomotion of legged robots that collaboratively carry objects. An innovative network of reduced-order models subject to holonomic constraints, referred to as interconnected LIP dynamics, is presented to study quasi-statically stable cooperative locomotion. The higher level of the proposed algorithm employs a supervisory controller, based on event-based MPC, to effectively compute the optimal reduced-order trajectories for the interconnected LIP dynamics. The lower level of the proposed algorithm employs distributed nonlinear controllers to reduce the gap between reduced- and full-order complex models of cooperative locomotion. We numerically investigate the effectiveness of the proposed control algorithm via full-order simulations of a team of collaborative quadrupedal robots, each with a total of 22 DOFs. The dissertation also investigates the robustness of the proposed control algorithm against uncertainties in the payload mass and changes in the ground height profile. Finally, we present a layered control approach for real-time trajectory planning and control of dynamically stable cooperative locomotion by two holonomically constrained quadrupedal robots. An innovative and interconnected network of reduced-order models, based on the single rigid body (SRB) dynamics, is developed for trajectory planning purposes. At the higher level of the control scheme, two different MPC algorithms are proposed to address the optimal control problem of the interconnected SRB dynamics: centralized and distributed MPCs. The MPCs compute the reduced-order states, GRFs, and interaction wrenches between the agents. The distributed MPC assumes two local QPs that share their optimal solutions according to a one-step communication delay and an agreement protocol. At the lower level of the control scheme, distributed nonlinear controllers are employed to impose the full-order dynamics to track the prescribed and optimal reduced-order trajectories and GRFs. The effectiveness of the proposed layered control approach is verified with extensive numerical simulations and experiments for the blind, robust, and cooperative locomotion of two holonomically constrained A1 robots with different payloads on different terrains and in the presence of external disturbances. It is shown that the distributed MPC has a performance similar to that of the centralized MPC, while the computation time is reduced significantly. / Doctor of Philosophy / Future cities will include a complex and interconnected network of collaborative robots that cooperatively work with each other and people to support human societies. Human-centered communities, including factories, offices, and homes, are developed for humans who are bipedal walkers capable of stepping over gaps, walking up/down stairs, and climbing ladders. One of the most challenging problems in deploying the next generation of collaborative robots is maneuvering in those complex environments. Although significant theoretical and technological advances have allowed the development of distributed controllers for motion control of multi-agent robotic systems, existing approaches do not address the collaborative locomotion problem of legged robots. Legged robots are inherently unstable with nonlinear and hybrid natures, unlike most systems where these algorithms have been deployed. Furthermore, the evolution of legged collaborative robot teams that cooperatively manipulate objects can be represented by high-dimensional and complex dynamical systems, complicating the design of control algorithms for coordination and motion control. This dissertation aims to establish a formal foundation based on nonlinear control and optimization theory to develop hierarchical feedback control algorithms for effective motion control of legged robots. The proposed layered control algorithms are developed based on interconnected reduced-order models. At the high level, we formulate cooperative locomotion as an optimal control problem of the reduced-order models to generate optimal trajectories. To realize the generated optimal trajectories, nonlinear controllers at the low level of the hierarchy impose the full-order models to track the trajectories while sustaining stability. The effectiveness of the proposed layered control approach is verified with extensive numerical simulations and experiments for the blind and stable cooperative locomotion of legged robots with different payloads on different terrains and subject to external disturbances. The proposed architecture's robustness is shown under various indoor and outdoor conditions, including landscapes with randomly placed wood blocks, slippery surfaces, gravel, grass, and mulch.

Tails are frequently utilized by animals to enhance their motion agility, dexterity, and versatility, such as a cheetah using its tail to change its body orientation while its legs are all off the ground and a monkey using its tail to stabilize its locomotion on branches. However, limited by technology and application scenarios, most existing legged robots do not include a robotic tail on board. This research aims to explore the possibilities of adding this missing part on legged robots and investigate the tail's functionalities on enhancing the agility, dexterity, and versatility of legged locomotion. In particular, this research focuses on animal-like serpentine tail structure, due to its larger workspace and higher dexterity. The overall research approach consists of two branches: a theoretical branch that focuses on dynamic modeling, analysis, and control of the legged robots with a serpentine robotic tail; and an empirical branch that focuses on hardware development and experiments of novel serpentine robotic tails and novel legged robots with tail. More specifically, the theoretical work includes modeling and control of a general quadruped platform and a general biped platform, equipped with one of the two general serpentine tail structures: an articulated-structure tail or a continuum-structure tail. Virtual work principle-based formulation was used to formulate the dynamic model. Both classic feedback linearization-based control and optimization-based control were used to coordinate the leg motions and the tail motion. Comparative studies on different tail structures as well as numerical analyses on robotic locomotion were performed to investigate the dynamic effects of serpentine robotic tails. The empirical work includes the developments and experiments of two novel serpentine robotic tail mechanisms and one first-of-its-kind quadruped robot ("VT Lemur") equipped with a serpentine robotic tail. To develop these novel robots, a systematic approach based on dynamic analysis was used. Various experiments were then conducted using the robot hardware. Both the theoretical and empirical results showed that the serpentine robotic tail has significant effects on enhancing the agility, dexterity, and versatility of legged robot motion. / Doctor of Philosophy / Quadruped robots have made impressive progresses over the past decade and now can easily achieve complicated, highly dynamic motions, such as the backflip of the MIT Mini Cheetah robot and the gymnastic parkour motions of the Atlas robot from Boston Dynamics, Inc. However, by looking at nature, many animals use tails to achieve highly agile and dexterous motions. For instance, monkeys are observed to use their tails to grasp branches and to balance their bodies during walking. Kangaroos are found to use their tails as additional limbs to propel and assist their locomotion. Cheetahs and kangaroo rats are thought to use their tails to help maneuvering. Therefore, this research aims to understand the fundamental principles behind these biological observations and develop novel legged robots equipped with a serpentine robotic tail. More specifically, this research aims to answer three key questions: (1) what are the functional benefits of adding a serpentine robotic tail to assist legged locomotion, (2) how do animals control their tail motion, and (3) how could we learn from these findings and enhance the agility, dexterity, and versatility of existing legged robots. To answer these questions, both theoretical investigations and experimental hardware testing were performed. The theoretical work establishes general dynamic models of legged robots with either an articulated tail or a continuum tail. A corresponding motion control framework was also developed to coordinate the leg and tail motions. To verify the proposed theoretical framework, a novel quadruped robot with a serpentine robotic tail was developed and tested.

This thesis addresses two important issues when operating the novel three legged mobile robot STriDER (Self-excited Tripedal Dynamic Experimental Robot); how to stand up after falling down while minimizing the motor torques at the joints and considerations for gait planning. STriDER uses a unique tripedal gait to walk with high energy efficiency and has the ability to change directions. In the first version of STriDER, the concept of passive dynamic locomotion was emphasized; however, for the new version, all joints are actively controlled for robustness. The robot is inherently stable when all three feet are on the ground due to its tripod stance, but it can still fall down if it trips while taking a step or if unexpected external forces act on it. The unique structure of STriDER makes the simple task of standing up challenging for a number of reasons; the high height of the robot and long limbs require high torque at the actuators due to its large moment arms; the joint configuration and length of the limbs limit the workspace where the feet can be placed on the ground for support; the compact design of the joints allows limited joint actuation motor output torque; three limbs do not allow extra support and stability in the process of standing up. This creates a unique problem and requires novel strategies to make STriDER stand up. This thesis examines five standing up strategies unique to STriDER: three feet pushup, two feet pushup, one foot pushup, spiral pushup, and feet slipping pushup. Each strategy was analyzed and evaluated considering constraints such as static stability, friction at the feet, kinematic configuration and joint motor torque limits to determine optimal design and operation parameters. Using the findings from the analysis, experiments were conducted for all five standing up strategies to determine the most efficient standing up strategy for a given prototype using the same design and operation parameters for each method. Also, a literature review was conducted for human standing from a chair and human pushup exercises and the conclusions were compared to the analysis presented in this thesis. Many factors contribute to the development of STriDER's gait. Several considerations for gait planning as the robot takes a step are investigated, including: stability, dynamics, the body's maximum and minimum allowable heights, the swing legs foot clearance to the ground, and the range of the subsequent swing foot contact positions. A static stability margin was also developed to asses the stability of STriDER. This work will lay the foundation for future gait generation research for STriDER. Additionally, guidelines for future work on single step gait generation based on kinematics and dynamics are discussed. The findings presented will advance the capabilities and adaptability of the novel robot STriDER. By studying standing up strategies and gait planning issues, the most efficient control methods can be implement for standing up and preparing to take a step and lay out the foundations for future research and development on STriDER. / Master of Science

This diploma thesis analyses Ulrich's concept of the multiple-roles model and the implementation of the HR Operating Model through selected theoretical bases, published qualitative and quantitative researches conducted especially in the environment of European organizations of the secondary and tertiary sector of the market. HR Operating Model is also analysed through own qualitative research conducted in the personnel department of a selected organization operating in the field of fast moving consumer goods, which becomes a part of a multinational organization. Part of the qualitative research is a descriptive case study that monitors the transformation of the organizational structure of the personnel department, the revision and the implementation of the personnel processes, the degree of its' adaptation to the specific local conditions, the descriptive case study also identifies the positive and the negative consequences of the transformation in the local personnel department. Keywords HR Operating Model, Three-legged Model, multiple-role model, HR Business Partner, transformation of the personnel department

A cause d’une anthropisation toujours plus forte des écosystèmes, de plus en plus de menaces pèsent sur la biodiversité. Parmi celles-ci, l’exposition aux contaminants est particulièrement problématique pour les organismes vivants. Emis et utilisés dans les pays industrialisés, ces contaminants hautement persistants dans l’environnement vont gagner les régions polaires puis se bio-accumuler dans les organismes vivants au cours du temps et se bio-amplifier le long du réseau trophique. Ainsi, les oiseaux marins, longévifs et situés dans les maillons supérieurs de la chaine alimentaire, sont particulièrement exposés et vulnérables à une exposition chronique à ces contaminants. A travers une perturbation endocrinienne, ces contaminants vont pouvoir impacter certains mécanismes physiologiques et traits comportementaux, entrainant in fine des conséquences à long-terme sur la fitness des individus et populations. Ma thèse s’articule autour de trois grandes familles de contaminants : i) les composés perfluoroalkylés (PFASs), encore largement utilisés dans plusieurs secteurs industriels et agricoles et en augmentation dans l’environnement ; (ii) les composés organochlorés dits « d’héritage » (OCs), interdits depuis des années mais entrainants toujours des effets délétères sur la biodiversité et (iii) le mercure (Hg), métal lourd non-essentiel ayant une origine à la fois anthropique et naturelle. Basé sur une approche corrélative in natura, je me suis intéressé aux conséquences physiologiques et comportementales d’une exposition chronique à ces trois grandes familles de contaminants présents chez la mouette tridactyle (Rissa tridactyla) de l’Arctique Norvégien (Svalbard) au cours de son cycle reproducteur (depuis l’accouplement jusqu’à l’élevage des poussins). Spécifiquement, j’ai étudié les relations entre ces contaminants et la fertilité (morphologie et motilité des spermatozoïdes), l’expression des signaux sexuels (visuel : coloration des téguments, olfactif : signature chimique), les comportements de soins parentaux (température d’incubation et rotation de l’œuf), le vieillissement cellulaire (longueur des télomères) et la dépense énergétique (métabolisme de base). Je me suis également penché sur de potentiels mécanismes sous-jacents permettant d’expliquer ces relations. Puisque ces mécanismes physiologiques et comportementaux sont fortement impliqués dans la valeur sélective des individus, les possibles conséquences à long terme de cette exposition sur la reproduction et survie des individus sont discutées. Ce travail permet de souligner la forte toxicité de certains composés organochlorés « historiques » (en particulier les chlordanes) et d’apporter de toutes nouvelles connaissances sur la toxicité très mal connue des PFASs chez la faune sauvage. Fait important, ce travail de thèse révèle que les PFASs et les OCs pourraient agir de manière contrastée sur plusieurs mécanismes physiologiques et traits comportementaux. Spécifiquement, une forte exposition à l’oxychlordane, un métabolite du chlordane, pesticide interdit depuis des décennies, est associée à des télomères plus courts, une réduction du métabolisme de base et à une moindre capacité à incuber les œufs. A l’inverse, on observe une élongation des télomères, une augmentation du métabolisme de base et une rotation des œufs accrue chez les individus les plus exposés aux PFASs. Le Hg, au moins en ce qui concerne les paramètres étudiés, ne semble pas jouer un rôle majeur. Cette étude souligne l'importance de tenir compte de plusieurs groupes de contaminants lorsqu'on étudie les conséquences de l'exposition aux contaminants environnementaux chez la faune sauvage. / Due to increasing human activities, a growing number of threats are challenging the fate of biodiversity. Among them, environmental contamination is particularly concerning for living organisms. Used and released in industrialized countries, these highly persistent contaminants can reach remote areas such as the Arctic ecosystem and will biomagnify though food webs and bioaccumulate in organisms. Long-lived seabirds are located in the upper levels of the food chains and thus particularly exposed and sensitive to a chronic contaminants exposure. Through endocrine disruption, these contaminants can impact physiological mechanisms and behavioural traits, inducing in fine, long-term fitness consequences on individuals and populations. My thesis focuses on three groups of contaminants: (i) poly- and perfluoroalkyl substances (PFASs), still broadly used in a vast array of industrial processes and increasing in the Arctic; (ii) “legacy” organochlorine contaminants (OCs, pesticides and industrial compounds), banned from use but still well present in the Arctic and (iii) mercury (Hg), a non-essential metal coming of both natural and anthropic origins. Based on a correlative approach conducted in natura, I investigated the physiological and behavioural consequences of exposure to these contaminants during the whole breeding cycle (from pre-laying to chick-rearing period) in an Arctic seabird, the black-legged kittiwake (Rissa tridactyla) from Svalbard, Norwegian Arctic. Specifically, I examined the relationships between several PFASs, OCs, Hg and fertility (sperm morphology and motility), sexual signaling (visual: integument coloration and olfactory: chemical signature), parental care behaviors (incubation temperature and egg-turning), ageing (telomere length) and energy expenditure (basal metabolic rate). In addition, some potential underlying mechanisms were also studied to better understand the way through which contaminants can be detrimental for kittiwakes. Furthermore, since physiological mechanisms and behavioral traits investigated here are tightly involved in self maintenance and reproduction, possible effects on fitness are then discussed. This PhD work underlines the major role of certain legacy chlorinated organic compounds (e.g. chlordanes) and significantly contributes at documenting the poorly known toxicological consequences of PFASs exposure in wildlife. Importantly, this PhD shows that PFASs and OCs could impact ageing, energy expenditure and some parental care behaviors in a contrasted manner. Specifically, oxychlordane, a metabolite of a banned organochlorine pesticide was associated with decreased telomere length, lowered metabolic rate and reduced ability to incubate the eggs. Conversely, elongated telomere, increased BMR and enhanced egg rotation were observed in birds bearing the highest concentrations of PFASs. Finally, at least for the considered endpoints, Hg appears as a coming minor threat for kittiwakes. This study highlights the importance of considering several groups of contaminants when investigating the consequences of environmental contaminants exposure in wildlife.