In this thesis we address the constructibility problem for a ground vehicle moving across an environment instrumented with ranging sensors. When the measurements collected by the vehicle along the trajectory are sufficiently informative, the global constructibility property is achieved and the vehicle is able to localise itself in the environment without relying on prior information on its state. When this condition is not met, the system can still achieve local (or weak) constructibility, where localising the robot requires some initial information on the state, such as a sufficiently small set containing the initial position of the robot, or some inaccessible areas of the Cartesian plane. First, we address the global problem: we show that extending the well--known solutions for the positioning problem, e.g. trilateration, is not trivial and leads to unintuitive results where constructibility is not attained. By building an abstract trajectory, which contains all the relevant information to reconstruct the actual trajectory followed by the vehicle, we analyse how global constructibility properties are affected by the shape of the abstract trajectory, the number of sensors, their deployment in the environment, and the distribution of measurements among the beacons. To describe local constructibility, we build the Constructibility Gramian for a robot described by the unicycle kinematic model. We rely on this tool for a twofold aim: (a) we build the same abstract trajectory presented for the global analysis and define necessary and sufficient conditions to attain local constructibility, and (b) in an environment instrumented with two beacons and for straight trajectories followed by the vehicle, we measure local constructibility by means of the smallest eigenvalue of the Constructibility Gramian, and we analyse how this metric is affected by the geometry of the scenario, e.g. the distance between anchors, and the distance between the trajectory and the line joining the anchors. Lastly, we extend the devised results to multiagent systems, both for constructibility analysis and for trajectory planning algorithms. We build the Constructibility Gramian for the multiagent system with relative ranging measurements and assess local constructibility property. Then, we propose a trajectory planning algorithm where a pair of vehicles without a priori information achieve global constructibility with both absolute and relative measurements. Moreover, we propose a variation of the Constructibility Gramian, limited to the position variable and hence called Position Gramian, and use this tool in a Model Predictive Control framework to plan the trajectory of a tracker vehicle aiming at simultaneously localising itself and a collaborative target through ranging measurements.

The study of self-assembling robots represents a promising strand within the emerging field of modular robots research. Self-assembling robots have the potential to autonomously adapt their bodies to new tasks and changing environments long after their initial deployment by forming new or reorganizing existing physical connections to peer robots. In previous research, many approaches have been presented to enable self-assembling robots to form composite morphologies. Recent technological advances have also increased the number of robots able to form such morphologies by at least two orders of magnitude. However, to date, composite robot morphologies have not been able to solve real-world tasks nor have they been able to adapt to changing conditions entirely without human assistance or prior knowledge.In this thesis, we identify three reasons why self-assembling robots may not have been able to fully unleash their potential and propose appropriate solutions. First, composite morphologies are not able to show sensorimotor coordination similar to those seen in their monolithic counterparts. We propose "mergeable nervous systems" -- a novel methodology that unifies independent robotic units into a single holistic entity at the control level. Our experiments show that mergeable nervous systems can enable self-assembling robots to demonstrate feats that go beyond those seen in any engineered or biological system. Second, no proposal has been tabled to enable a robot in a decentralized multirobot system select its communication partners based on their location. We propose a new form of highly scalable mechanism to enable "spatially targeted communication" in such systems. Third, the question of when and how to trigger a self-assembly process has been ignored by researchers to a large extent. We propose "supervised morphogenesis" -- a control methodology that is based on spatially targeted communication and enables cooperation between aerial and ground-based self-assembling robots. We show that allocating self-assembly related decision-making to a robot with an aerial perspective of the environment can allow robots on the ground to operate in entirely unknown environments and to solve tasks that arise during mission time. For each of the three propositions put forward in this thesis, we present results of extensive experiments carried out on real robotic hardware. Our results confirm that we were able to substantially advance the state of the art in self-assembling robots by unleashing their potential for morphological adaptation through enhanced sensorimotor coordination and by improving their overall autonomy through cooperation with aerial robots. / Doctorat en Sciences de l'ingénieur et technologie / info:eu-repo/semantics/nonPublished

With the development of technology and availability of data, it is sometimes easier to learn the control policies directly from the data, rather than modeling a plant and designing a controller. Modeling a plant is not always possible due to the complexity of the plant. Data-enabled predictive control (DeePC) is a recently proposed approach that combines system identification, estimation, and control in a single optimization problem. DeePC is primarily designed for LTI systems. The purpose of this thesis is to extend the application of DeePC to nonlinear systems with a particular focus on a non-holonomic ground robot. To reach this goal, we decompose the system states into different working modes where each mode can be linearly approximated. Furthermore, the data collection policies were also evaluated to conclude how they affect the performance of the DeePC. We identified several key challenges in this direction, namely: data-demanding structure, high computational complexity, and performance deterioration with increased non-linearity. While these challenges prohibited the application of DeePC to the ground robot system; we successfully applied the method to a benchmark non-linear system, the inverted pendulum on cart problem, and studied the effect of various design choices on control performance. Our observations indicate potential areas of improvement toward enabling DeePC for highly nonlinear systems. / Med utvecklingen av teknik och tillgänglighet av data är det ibland enklare att lära sig styrpolicyerna direkt från data, snarare än att modellera ett system och designa en styrenhet. Att modellera ett system är inte alltid möjligt på grund av systemets komplexitet. Data aktiverad prediktiv kontroll (DeePC) är en nyligen föreslagen metod som kombinerar systemidentifiering, uppskattning och kontroll i ett enda optimeringsproblem. DeePC är främst designad för LTI-system. Syftet med denna avhandling är att utöka tillämpningen av DeePC till icke-linjära system med särskilt fokus på en icke-holonomisk markrobot. För att nå detta mål delar vi upp systemtillstånden i olika arbetslägen där varje läge kan approximeras linjärt. Dessutom utvärderades datainsamlingspolicyerna för att dra slutsatser om hur de påverkar DeePCs prestation. Vi identifierade ett antal nyckelutmaningar i denna riktning, nämligen: datakrävande struktur, hög beräkningskomplexitet och prestandaförsämring med ökad icke-linjäritet. Även om de utmaningerna hindrade tillämpningen av DeePC på markrobot systemet; har vi framgångsrikt tillämpat metoden på ett benchmark icke-linjärt system, problemet med inverterad pendel på vagn, och studerade effekten av olika designval på kontrollprestanda. Våra observationer indikerar potentiella förbättringsområden för att möjliggöra DeePC för mycket olinjära system.