Ce manuscrit traite de la conception d’un robot quadrirotor bio-inspiré. Ce robot, nommé QuadMorphing, s’inspire de l’oiseau et possède la capacité de se replier en vol afin de diminuer son envergure. Cette particularité est intéressante pour des problématiques d’évitement d’obstacles dans des milieux encombrés.Le travail présenté ici contient une présentation du robot où la plateforme mécatronique y est décrite en détails. Puis, des résultats expérimentaux sont présentés et commentés afin de quantifier les performances du prototype QuadMorphing durant des scénarios de franchissement d’obstacles.La deuxième partie de cette thèse traite de l’estimation de la taille d’obstacles en vol grâce à une perception visuelle monoculaire. Deux algorithmes d’estimation ont été simulés afin d’être validés pour être ensuite mis en place sur une nouvelle version du robot qui a été testée expérimentalement. Ces estimations permettent par la suite de rendre le robot plus autonome pour éviter les collisions avec son environnement et actionner son système de changement de forme si cela est nécessaire. / This paper describes a bio-inspired quadrotor design. This robot, called QuadMorphing, is inspired by birds and has the ability to fold its mechanical structure to reduce its wingspan during the flight. This feature could be useful for obstacle avoidance task in cluttered environments.The work presented here contains a full description of the mechatronic structure. Then, experimental results are presented and discussed in order to quantify the QuadMorphing performances during obstacle avoidance scenarios.The second part of this thesis deals with estimating obstacle size during flight using monocular visual perception. Two estimation algorithms were simulated in order to be validated and then implemented for experimental testing on a new version of the robot. In order to make the robot autonomous, the estimation of the size of the obstacle allows the robot to avoid collisions with its environment and to perform its morphological reduction if necessary.

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Obstacle detection is one of the major focus area on image processing. For mobile robots, obstacle detection and collision avoidance is a notorious problem and is still a part of the modern research. There are already a lot of research have been done so far for obstacle detection and collision avoidance. This thesis evaluates the existing various well-known methods and sensors for collision free navigation of the mobile robot. For moving obstacle detection purpose the frame difference approach is adopted. Robotino® is used as the mobile robot platform and additionally Microsoft Kinect is used as 3D sensor. For getting information from the environment about obstacle, the 9-built-in distance sensor of Robotino® and 3D depth image data from the Kinect is used. The combination is done to get the maximum advantages for obstacles information. The detection of moving object in front of the sensor is a major interest of this work.

This compilation thesis presents an overarching framework on the utilization of nonlinear model predictive control(NMPC) for various applications in the context of Unmanned Aerial Vehicle (UAV) path planning and collision avoidance. Fast and novel optimization algorithms allow for NMPC formulations with high runtime requirement, as those posed by controlling UAVs, to also have sufficiently large prediction horizons as to in an efficient manner integrate collision avoidance in the form of set-exclusion constraints that constrain the available position-space of the robot. This allows for an elegant merging of set-point reference tracking with the collision avoidance problem, all integrated in the control layer of the UAV. The works included in this thesis presents the UAV modeling, cost functions, constraint definitions, as well as the utilized optimization framework. Additional contributions include the use case on multi-agent systems, how to classify and predict trajectories of moving (dynamic) obstacles, as well as obstacle prioritization when an aerial agent is in the precense of more obstacles, or other aerial agents, than can reasonably be defined in the NMPC formulation. For the cases of dynamic obstacles and for multi-agent distributed collision avoidance this thesis offers extensive experimental validation of the overall NMPC framework. These works push the limits of the State-of-the-Art regarding real-time real-life implementations of NMPC-based collision avoidance. The works also include a novel RRT-based exploration framework that combines path planning with exploration behavior. Here, a multi-path RRT * planner plans paths to multiple pseudo-random goals based on a sensor model and evaluates them based on the potential information gain, distance travelled, and the optimimal actuation along the paths.The actuation is solved for as as the solutions to a NMPC problem, implying that the nonlinear actuator-based and dynamically constrained UAV model is considered as part of the combined exploration plus path planning problem. To the authors best knowledge, this is the first time the optimal actuation has been considered in such a planning problem. For all of these applications, the utilized optimization framework is the Optimization Engine: a code-generation framework that generates a custom Rust-based solver from a specified model, cost function, and constraints. The Optimization Engine solves general nonlinear and nonconvex optimization problems, and in this thesis we offer extensive experimental validation of the utilized Proximal-Averaged Newton-type method for Optimal Control (PANOC) algorithm as well as both the integrated Penalty Method and Augmented Lagrangian Method for handling the nonlinear nonconvex constraints that result from collision avoidance problems.

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Recent developments in autopilot technology have increased the potential use of micro air vehicles (MAVs). As the number of applications increase, the demand on MAVs to operate autonomously in any scenario increases. Currently, MAVs cannot reliably fly in cluttered environments because of the difficulty to detect and avoid obstacles. The main contribution of this research is to offer obstacle detection and avoidance strategies using laser rangers and cameras coupled with computer vision processing. In addition, we explore methods of visual target tracking and task allocation. Utilizing a laser ranger, we develop a dynamic geometric guidance strategy to generate paths around detected obstacles. The strategy overrides a waypoint planner in the presence of pop-up-obstacles. We develop a second guidance strategy that oscillates the MAV around the waypoint path and guarantees obstacle detection and avoidance. Both rely on a laser ranger for obstacles detection and are demonstrated in simulation and in flight tests. Utilizing EO/IR cameras, we develop two guidance strategies based on movement of obstacles in the camera field-of-view to maneuver the MAV around pop-up obstacles. Vision processing available on a ground station provides range and bearing to nearby obstacles. The first guidance law is derived for single obstacle avoidance and pushes the obstacle to the edge of the camera field-of-view causing the vehicle to avoid a collision. The second guidance law is derived for two obstacles and balances the obstacles on opposite edges of the camera field-of-view, maneuvering between the obstacles. The guidance strategies are demonstrated in simulation and flight tests. This research also addresses the problem of tracking a ground based target with a fixed camera pointing out the wing of a MAV that is subjected to constant wind. Rather than planning explicit trajectories for the vehicle, a visual feedback guidance strategy is developed that maintains the target in the field-of-view of the camera. We show that under ideal conditions, the resulting flight paths are optimal elliptical trajectories if the target is forced to the center of the image plane. Using simulation and flight tests, the resulting algorithm is shown to be robust with respect to gusts and vehicle modeling errors. Lastly, we develop a method of a priori collision avoidance in assigning multiple tasks to cooperative unmanned air vehicles (UAV). The problem is posed as a combinatorial optimization problem. A branch and bound tree search algorithm is implemented to find a feasible solution using a cost function integrating distance traveled and proximity to other UAVs. The results demonstrate that the resulting path is near optimal with respect to distance traveled and includes a significant increase in expected proximity distance to other UAVs. The algorithm runs in less than a tenth of a second allowing on-the-fly replanning.

No description available.

Autonomous Unmanned Aerial Vehicles (UAV) havegreat potential in executing various complex tasks due to theirflexibility and relatively small size. The aim of this paper is todevelop a motion planner capable of generating a trajectory withaggressive maneuvers through narrow spaces without collision.The approach utilizes a framework using an optimized variantof the Rapidly-exploring Random Tree (RRT) algorithm, calledRRT*, with a Control Barrier Functions (CBF) based obstacleavoidance algorithm as well as a motion primitive generator. If amotion primitive collides with an obstacle, the obstacle avoidancealgorithm will attempt to reach the end state of a motion primitivein a collision free manner while complying with the actuationconstraints. From the collision free trajectories an optimal path iscontinuously searched for by RRT* by minimizing a cost in jerk.The performance of RRT* and the obstacle avoidance are testedin simulations independently and jointly, in several differentscenarios. The resulting motion planner successfully finds ahigh-level trajectory for the different scenarios. Limitations ofthe method as well as possible areas of improvements are alsodiscussed at the end of this paper. / Autonoma UAV har goda möjligheter för att utföra flera olika komplexa uppgifter tack vare deras flexibilitet och storlek. Denna rapport redogör för en rörelseplaneringsalgoritm som kombinerar manövrerbarheten hos en UAV för att skapa en kollisionsfri bana som innehåller aggressiva manövreringar genom trånga utrymmen. Tillvägagångssättet innefattar att kombinera Rapidly-exploring Random Tree (RRT*) med en algoritm för att undvika hinder baserad på Control Barrier Functions (CBF), samt att låta banan delas upp i segment, så kallade motion primitives, som genereras var för sig. Om en motion primitive kolliderar kommer den hinderundvikande algoritmen göra ett försök att nå dess målposition medan kollision undviks och manövreringsbegränsningarna uppfylls. Med en samling genomförbara motion primitives söker RRT* efter en kontinuerlig bana optimerad med hänsyn till en kostnad i ryck. Prestandan för RRT* och den hinderundvikande algoritmen simuleras både separat och tillsammans. Den resulterande rörelseplaneraren lyckas hitta en genomförbar bana för vardera scenario. Begränsningar av metoden samt potentiella förbättringsområden diskuteras i slutet av denna rapport. / Kandidatexjobb i elektroteknik 2021, KTH, Stockholm

This dissertation discusses vision-based static obstacle avoidance for a fleet of nonholonomic robots tasked to arrive at a final destination simultaneously. Path generation for each vehicle is computed using a single polynomial function that incorporates the vehicle constraints on velocity and acceleration and satisfies boundary conditions by construction. Furthermore, the arrival criterion and a preliminary obstacle avoidance scheme is incorporated into the path generation. Each robot is equipped with an inertial measurement unit that provides measurements of the vehicle's position and velocity, and a monocular camera that detects obstacles. The obstacle avoidance algorithm deforms the vehicle's original path around at most one obstacle per vehicle in a direction that minimizes an obstacle avoidance potential function. Deconfliction of the vehicles during obstacle avoidance is achieved by imposing a separation condition at the path generation level. Two estimation schemes are applied to estimate the unknown obstacle parameters. The first is an existing method known in the literature as Identifier-Based Observer and the second is a recently-developed fast estimator. It is shown that the performance of the fast estimator and its effect on the obstacle avoidance algorithm can be arbitrarily improved by the appropriate choice of parameters as compared to the Identifier-Based Observer method. Coordination in time of all vehicles is completed in an outer loop which adjusts the desired velocity profile of each vehicle in order to meet the simultaneous arrival constraints. Simulation results illustrate the theoretical findings. / Ph. D.

Coordenação de robôs móveis é um tópico importante de pesquisa dado que existem tarefas que podem ser desenvolvidas de forma mais eficiente e com menor custo por um grupo de robôs do que por um só robô. Nesta dissertação é apresentado um estudo sobre coordenação de robôs móveis para o problema de navegação em ambientes externos. Para isso, foi desenvolvido um sistema de localização utilizando os dados de odometria e do receptor GPS, e um sistema de desvio de obstáculos para planejar a trajetória livre de obstáculos. Os movimentos coordenados foram realizados em função de um líder e qualquer robô da formação pode assumir a liderança. A liderança é assumida pelo robô que ultrapassar a distância mínima a um obstáculo. Movimentos estáveis são gerados através de uma lei de controle descentralizada baseada nas coordenadas dos robôs. Para garantir a estabilidade da formação quando há alternância de líder ou remoção de robôs, foi feito controle tolerante a falhas para um grupo de robôs móveis. O controle tolerante a falhas é baseado em controle H \'INFINITO\' por realimentação da saída de sistemas lineares sujeitos a saltos Markovianos para garantir a estabilidade da formação quando um dos robôs é perdido durante o movimento coordenado. Os resultados do sistema de localização mostram que o uso de filtro robusto para a fusão de dados produz uma melhor estimativa da posição do robô móvel. Os resultados também mostram que o sistema de desvio de obstáculos é capaz de gerar uma trajetória livre de obstáculos em ambientes desconhecidos. E por fim, os resultados do sistema de coordenação mostram que o grupo de robôs mantém a formação desejada percorrendo a trajetória de referência na presença de distúrbios ou quando um robô sai da formação. / Coordination of mobile robots is an important topic of research because there are tasks that may be too difficult for a single robot to perform alone, these tasks can be performed more efficiently and cheaply by a group of mobile robots. This dissertation presents a study on the coordination of mobile robots to the problem of navigation in outdoor environments. To solve this problem, a localization system using data from odometry and GPS receiver, and an obstacle avoidance system to plan the collision-free trajectory, were developed. The coordinated motions are performed by the robots that follow a leader, and any robot of the formation can assume the leadership. The leadership is assumed by a robot when it exceeds the threshold distance to an obstacle. Stable motions are generated by a decentralized control law based on the robots coordinates. To ensure the stability formation when there is alternation of leader or one of the robots is removed, we made a fault tolerant control for a group of mobile robots. The fault tolerant approach is based on output feedback H \'INFINITE\' control of Markovian jump linear systems to ensure stability of the formation when one of the robots is lost during the coordinated motion. The results of the localization system show that the use of robust filter for data fusion produces a better estimation of the mobile robots position. The results also show that the obstacle avoidance system is capable of generating a path free from obstacles in unknown environments. Finally, the results of the coordination system show that the group of robots maintain the desired formation along the reference trajectory in the presence of disturbance or removal of one of them.