Transit operators are increasingly interested in improving efficiency, reliability, and performance of commuter trains while reducing their operating costs. In this context, the application of optimal control theory to the problem of train control can help towards achieving some of these objectives. However, the traction and braking systems of commuter trains often exhibit significant time delays, making the control of commuter trains highly challenging. Previous literature on optimal train control ignores delays in actuation due to the inherent difficulty present in the optimal control, and in general, the control, of input-delay systems. In this thesis, optimal control of a commuter train is presented under two cases: (i) equal, and (ii) unequal time delays in the train traction and braking commands. The solution approach uses the economic model predictive control framework, which involves formulating and solving numerical optimization problems to achieve minimum mixed energy-time optimal control in discretized spatial and time domains. The optimization problems are re-solved repeatedly along the track for the remainder of the trip, using the latest sensor measurements. This would essentially establish a feedback mechanism in the control to improve robustness to modelling errors. A key feature of the proposed methods is that they are model-based controllers, they explicitly incorporate model information, including time delays, in controller synthesis hence avoiding performance degradation and potential instability. To address the issue of input-delays, the well-established predictor approach is used to compensate for input-delays. The case of equal traction-braking delays is treated in discretized spatial domain, which uses an already developed convex approximation to the optimization problem. The use of the convex approximation allows for robust and rapid computation of the optimal control solution. The non-equal traction-braking delays scenario is formulated in time domain, leading to a nonconvex optimization problem. An alternative formulation for minimum-time optimal control problems is presented for delay-free systems that simplifies the solution of minimum-time optimal control problems compared to conventional minimum-time optimal control formulations. This formulation along with the predictor approach is used to help solve the train optimal control problem in the case of non-equal traction-braking delays. The non-equal traction-braking delay controller is compared with the equal traction-braking delay controller by insertion of an artificial delay to make the shorter delays equal to the longer delay. Results of numerical simulations demonstrate the validity and effectiveness of the proposed controllers. / Thesis / Master of Applied Science (MASc)

<p dir="ltr">Introduces a new framework and state-of-the-art algorithm in closed-loop prediction for motion planning under differential constraints. More specifically, this work introduces the idea of sampling on specific "sampling regions" rather than the entire workspace to speed-up the motion planning process by orders of magnitude.</p>

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.

<p dir="ltr">Motion planning for Autonomous Vehicles (AVs) and Connected Autonomous Vehicles (CAVs) involves the crucial task of translating road environmental data obtained from sensors and connectivity devices into a sequence of executable vehicle actions. This task is critical for AVs and CAVs, because the efficacy of their driving decisions and overall performance depend on the quality of motion planning.</p><p dir="ltr">In the context of motion planning technologies, several fundamental questions and challenges remain despite the widespread adoption of advanced learning-based methods, including deep learning (DL) and deep reinforcement learning (DRL). In this regard, the following critical questions need to be answered: 1) How to design suitable DL architectures to comprehensively understand the driving scenario by integrating data from diverse sources including sensors and connectivity devices? 2) How to effectively use the fused information to make improved driving decisions, accounting for various optimality criteria? 3) How to leverage vehicle connectivity to generate cooperative decisions for multiple CAVs, in a manner that optimizes system-wide utility? 4) How to address the inherent interpretability limitations of DL-based methods to enhance user trust in AVs and CAVs? 5) Is it possible to extend learning-based approaches to operational-level decisions in a way that overcomes the inherent disadvantage of low explainability and lack of safety guarantee?</p><p dir="ltr">In an effort to address these questions and expand the existing knowledge in this domain, this dissertation introduces several learning-based motion planning frameworks tailored towards different driving scenarios of AV and CAV. Technically, these efforts target on developing trustworthy AI systems with a focus on the information fusion, “explainable AI” or XAI and safety critical AI. From a computational perspective, these frameworks introduce new learning-based models with state-of-the-art (SOTA) structures, including Convolutional Neural Network (CNN). Recurrent Neural Networks (RNN), Graph Neural Networks (GNN), Attention networks, and Transformers. They also incorporate reinforcement learning (RL) agents, such as Deep Q Networks (DQN) and Model-based RL. From an application standpoint, these developed frameworks can be deployed directly in AVs and CAVs at Level 3 and above. This can enhance the AV/CAV performance in terms of individual and system performance metrics, including safety, mobility, efficiency, and driving comfort.</p>

[EN] The efficiency of free-collision motion planning results very sensible on robot and obstacle modelling technique selected. In this way, many works have been oriented to define models with proper throughput to speed up the collision detection proccess. This dissertation presents a new approach to the problem, whose complexity is reduced notably by means of using enveloping models of real objects, allowing security regions or distances. This objective is reached by means of the definition of a spherical model, composed of infinite spheres, generated from the application of linear or polynomial equations to a reduced number of control spheres, giving the so-called poly-spheres and spheroids respectively. These models, with evident simplicity, present a high modelling power, adapt easily to the requirements need in collision-detection and path planning applications for robotics systems. In order to represent a complete multi-robot cell, an extended hierarchical structure has been defined, in form of an AND-OR graph, with different degrees of accuracy, according to the different approximation model used. In order to generate automatically this structure, a procedure has been developed to compute the minimum volume enveloping spherical model in an off-line process with two levels based on Downhill Simplex method and Hough transform. This procedure can be greatly speed up by using clustering techniques to obtain appropiate initial conditions, allowing an on-line use. With a hierarchical structure computed in such a way, a fast procedure for collision detection in a multi-robot cell is introduced, based on several algorithms for distance computation including polyspheres and spheroids. This methodology presents a fast and anticipativa response, in the sense that every movement of a system has been validated before its execution, implying that not necessarily must be done in an off-line simulation. The use of spherical models, in addition to their fast distance computation, results suitable for the definition of artificial potential fields allowing a path planning in robotics systems with up to six degrees of freedom, including three for translation and three for rotation. The definition of these new potential fields and the study of new planning techniques based on classical optimisation methods allow their application straight forward in Cartesian space, with all their advantages. Last but not least, with the help of some systems for robot programming, simulation and control, the correctness of these contributions have been validated in a set of prototype applications, covering from robot-obstacle and multi-robot collision detection, to motion planning for a robot-arm or an auto-guided vehicle. / [ES] La eficiencia de la planificación de movimientos libres de colisión resulta muy sensible al modelado de los robots y obstáculos que se consideren, por lo que, frente al modelado tradicional con politopos, muchos trabajos en robótica han estado orientados a la definición de unos modelos que presenten buenas prestaciones de cara a acelerar el proceso de detección de colisiones. En esta Tesis se presenta una nueva perspectiva del problema, cuya complejidad queda reducida notablemente al utilizar envolventes de los objetos reales, lo que permite definir zonas o distancias de seguridad. Para ello se han definido unos modelos esféricos, compuestos de infinitas esferas generadas a partir de la aplicación de unas relaciones lineales o polinómicas a un número reducido de esferas de control, dando lugar a las llamadas poli-esferas y esferoides respectivamente. Estos modelos, de sencillez clara, presentan una potencia de modelado elevada, adaptándose fácilmente a los requisitos necesarios en las aplicaciones de detección de colisiones y planificación de movimientos en sistemas robotizados. Para la representación de una célula multi-robot completa, se ha definido una estructura jerárquica extendida, en forma de grafo AND-OR, con diferentes grados de precisión, mediante diferentes modelos de aproximación. De cara a generar automáticamente esta estructura, se ha desarrollado un procedimiento para generar el modelo esférico envolvente de mínimo volumen en un proceso off-line con dos niveles, basados en el método de minimización Downhill Simplex y en la transformada de Hough. Este procedimiento se acelera enormemente al utilizar técnicas de agrupamiento para obtener condiciones iniciales apropiadas, permitiendo su uso on-line. Con una estructura jerárquica generada de esta forma, se introduce un procedimiento rápido de detección de colisiones aplicable a una célula multi-robot, basado en algoritmos básicos de cálculo de distancias que pueden considerar poli-esferas y esferoides. Esta metodología presenta una respuesta rápida y anticipativa, entendiendo por tal que todo movimiento de cualquier sistema ha sido validado antes de su ejecución, por lo que no necesariamente debe realizarse en una simulación off-line. La utilización de modelos esféricos, así como el rápido cálculo de distancias entre ellos, resulta idónea para la definición de campos potenciales artificiales que permitan una planificación de movimientos en sistemas robotizados con hasta seis grados de libertad, incluyendo tres de traslación y tres de rotación. La definición de estos nuevos campos potenciales y el estudio de nuevas técnicas de planificación basados en métodos clásicos de optimización permiten su aplicación directamente en el espacio cartesiano, con las claras ventajas que esto conlleva. Finalmente, con la ayuda de varios sistemas de programación, simulación y control de robots, se ha demostrado la validez de estas aportaciones en una serie de aplicaciones prototipo que van desde la detección de colisiones de un robot con un obstáculo o entre sistemas multi-robot, a la planificación de movimientos de un brazo-robot o un vehículo autoguiado. / Mellado Arteche, M. (1996). Modelado, detección de colisiones y planificación de movimientos en sistemas robotizados mediante volúmenes esféricos [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/56621

The motion planning problem can be formulated as a Markov decision process (MDP), if the uncertainties in the robot motion and environments can be modeled probabilistically. The complexity of solving these MDPs grow exponentially as the dimension of the problem increases and hence, it is nearly impossible to solve the problem even without constraints. Using hierarchical methods, these MDPs can be transformed into a semi-Markov decision process (SMDP) which only needs to be solved at certain landmark states. In the deterministic robotics motion planning community, sampling based algorithms like probabilistic roadmaps (PRM) and rapidly exploring random trees (RRTs) have been successful in solving very high dimensional deterministic problem. However they are not robust to system with uncertainties in the system dynamics and hence, one of the primary objective of this work is to generalize PRM/RRT to solve motion planning with uncertainty. We first present generalizations of randomized sampling based algorithms PRM and RRT, to incorporate the process uncertainty, and obstacle location uncertainty, termed as "generalized PRM" (GPRM) and "generalized RRT" (GRRT). The controllers used at the lower level of these planners are feedback controllers which ensure convergence of trajectories while mitigating the effects of process uncertainty. The results indicate that the algorithms solve the motion planning problem for a single agent in continuous state/control spaces in the presence of process uncertainty, and constraints such as obstacles and other state/input constraints. Secondly, a novel adaptive sampling technique, termed as "adaptive GPRM" (AGPRM), is proposed for these generalized planners to increase the efficiency and overall success probability of these planners. It was implemented on high-dimensional robot n-link manipulators, with up to 8 links, i.e. in a 16-dimensional state-space. The results demonstrate the ability of the proposed algorithm to handle the motion planning problem for highly non-linear systems in very high-dimensional state space. Finally, a solution methodology, termed the "multi-agent AGPRM" (MAGPRM), is proposed to solve the multi-agent motion planning problem under uncertainty. The technique uses a existing solution technique to the multiple traveling salesman problem (MTSP) in conjunction with GPRM. For real-time implementation, an ?inter-agent collision detection and avoidance? module was designed which ensures that no two agents collide at any time-step. Algorithm was tested on teams of homogeneous and heterogeneous agents in cluttered obstacle space and the algorithm demonstrate the ability to handle such problems in continuous state/control spaces in presence of process uncertainty.

Motion planning for robot operations is concerned with path planning and trajectory generation. In multi-robot systems, i.e. with multiple robots operating simultaneously in a shared workspace, the motion planning also needs to coordinate the robots' motions to avoid collisions between them. The multi-robot coordination decides the cycle-time for the planned paths and trajectories since it determines to which extend the operations can take place simultaneously without colliding. To obtain the quickest cycle-time, there needs to bean optimal balance between, on the one hand short paths and fast trajectories, and on the other hand possibly longer paths and slower trajectories to allow that the operations take place simultaneously in the shared workspace. Due to the inter-dependencies, it becomes necessary to consider the path planning, trajectory generation and multi-robot coordination together as one optimisation problem in order to find this optimal balance.This thesis focusses on optimising the motion planning for multi-robot material handling systems of sheet metal parts. A methodology to model the relevant aspects of this motion planning problem together as one multi-disciplinary optimisation problem for Simulation based Optimisation (SBO) is proposed. The identified relevant aspects include path planning,trajectory generation, multi-robot coordination, collision-avoidance, motion smoothness, end-effectors' holding force, cycle-time, robot wear, energy efficiency, part deformations, induced stresses in the part, and end-effectors' design. The cycle-time is not always the (only) objective since it is sometimes equally/more important to minimise robot wear, energy consumption, and/or part deformations. Different scenarios for these other objectives are therefore also investigated. Specialised single- and multi-objective algorithms are proposed for optimising the motion planning of these multi-robot systems. This thesis also investigates how to optimise the velocity and acceleration profiles of the coordinated trajectories for multi-robot material handling of sheet metal parts. Another modelling methodology is proposed that is based on a novel mathematical model that parametrises the velocity and acceleration profiles of the trajectories, while including the relevant aspects of the motion planning problem excluding the path planning since the paths are now predefined.This enables generating optimised trajectories that have tailored velocity and acceleration profiles for the specific material handling operations in order to minimise the cycle-time,energy consumption, or deformations of the handled parts.The proposed methodologies are evaluated in different scenarios. This is done for real world industrial case studies that consider the multi-robot material handling of a multi-stage tandem sheet metal press line, which is used in the automotive industry to produce the cars' body panels. The optimisation results show that significant improvements can be obtained compared to the current industrial practice.

La compréhension et la prédiction des relations structure-fonction de protéines par des approches in sillico représentent aujourd'hui un challenge. Malgré le développement récent de méthodes algorithmiques pour l'étude du mouvement et des interactions moléculaires, la flexibilité de macromolécules reste largement hors de portée des outils actuels de modélisation moléculaire. L'objectif de cette thèse est de développer une nouvelle approche basée sur des algorithmes de planification de mouvement issus de la robotique pour mieux traiter la flexibilité moléculaire dans l'étude des interactions protéiques. Nous avons étendu un algorithme récent d'exploration par échantillonnage aléatoire, ML-RRT pour le désassemblage d'objets articulés complexes. Cet algorithme repose sur la décomposition des paramètres de configuration en deux sous-ensembles actifs et passifs, qui sont traités de manière découplée. Les extensions proposées permettent de considérer plusieurs degrés de mobilité pour la partie passive, qui peut être poussée ou attirée par la partie active. Cet outil algorithmique a été appliqué avec succès pour l'étude des changements conformationnels de protéines induits lors de la diffusion d'un ligand. A partir de cette extension, nous avons développé une nouvelle méthode pour la résolution simultanée du séquençage et des mouvements de désassemblage entre plusieurs objets. La méthode, nommée Iterative-ML-RRT, calcule non seulement les trajectoires permettant d'extraire toutes les pièces d'un objet complexe assemblé, mais également l'ordre permettant le désassemblage. L'approche est générale et a été appliquée pour l'étude du processus de dissociation de complexes macromoléculaires en introduisant une fonction d'évaluation basée sur l'énergie d'interaction. Les résultats présentés dans cette thèse montrent non seulement l'efficacité mais aussi la généralité des algorithmes proposés. / Understanding and predicting structure-function relationships in proteins with fully in silico approaches remain today a great challenge. Despite recent developments of computational methods for studying molecular motions and interactions, dealing with macromolecular flexibility largely remains out of reach of the existing molecular modeling tools. The aim of this thesis is to develop a novel approach based on motion planning algorithms originating from robotics to better deal with macromolecular flexibility in protein interaction studies. We have extended a recent sampling-based algorithm, ML-RRT, for (dis)-assembly path planning of complex articulated objects. This algorithm is based on a partition of the configuration parameters into active and passive subsets, which are then treated in a decoupled manner. The presented extensions permit to consider different levels of mobility for the passive parts that can be pushed or pulled by the motion of active parts. This algorithmic tool is successfully applied to study protein conformational changes induced by the diffusion of a ligand inside it. Building on the extension of ML-RRT, we have developed a novel method for simultaneously (dis)assembly sequencing and path planning. The new method, called Iterative-ML-RRT, computes not only the paths for extracting all the parts from a complex assembled object, but also the preferred order that the disassembly process has to follow. We have applied this general approach for studying disassembly pathways of macromolecular complexes considering a scoring function based on the interaction energy. The results described in this thesis prove not only the efficacy but also the generality of the proposed algorithms

Dans cette thèse nous nous intéressons à deux types d'approches pour la planification de pas pour robots humanoïdes : d'une part les approches discrètes où le robot n'a qu'un nombre fini de pas possibles, et d'autre part les approches où le robot se base sur des zones de faisabilité continues. Nous étudions ces problèmes à la fois du point de vue théorique et pratique. En particulier nous décrivons deux méthodes originales, cohérentes et efficaces pour la planification de pas, l'une dans le cas discret (chapitre 5) et l'autre dans le cas continu (chapitre 6). Nous validons ces méthodes en simulation ainsi qu'avec plusieurs expériences sur le robot HRP-2. / In this thesis we investigate two types of approaches for footstep planning for humanoid robots: on one hand the discrete approaches where the robot has only a finite set of possible steps, and on the other hand the approaches where the robot uses continuous feasibility regions. We study these problems both on a theoretical and practical level. In particular, we describe two original, coherent and efficient methods for footstep planning, one in the discrete case (chapter 5), and one in the continuous case (chapter 6). We validate these methods in simulation and with several experiments on the robot HRP-2.

This thesis work contains a stabilization and a motion planning strategy for a truck and trailer system. A dynamical model for a general 2-trailer with two rigid free joints and a kingpin hitching has been derived based on previous work. The model holds under the assumption of rolling without slipping of the wheels and has been used for control design and as a steering function in a probabilistic motion planning algorithm. A gain scheduled Linear Quadratic (LQ) controller with a Pure pursuit path following algorithm has been designed to stabilize the system around a given reference path. The LQ controller is only used in backward motion and the Pure pursuit controller is split into two parts which are chosen depending on the direction of motion. A motion planning algorithm called Closed-Loop Rapidly-exploring Random Tree (CL-RRT) has then been used to plan suitable reference paths for the system from an initial state configuration to a desired goal configuration with obstacle-imposed constraints. The motion planning algorithm solves a non-convex optimal control problem by randomly exploring the input space to the closed-loop system by performing forward simulations of the closed-loop system. Evaluations of performance is partly done in simulations and partly on a Lego platform consisting of a small-scale system. The controllers have been used on the Lego platform with successful results. When the reference path is chosen as a smooth function the closed-loop system is able to follow the desired path in forward and backward motion with a small control error. In the work, it is shown how the CL-RRT algorithm is able to plan non-trivial maneuvers in simulations by combining forward and backward motion. Beyond simulations, the algorithm has also been used for planning in open-loop for the Lego platform. / <p>Links to movies:</p><p>Reference tracking on Lego platform:</p><p>https://www.dropbox.com/s/ebtfgfo7aq9ij8w/reference_tracking.m4v?dl=0</p><p></p><p>Motion planning simulation with CL-RRT:</p><p>https://www.dropbox.com/s/z9kk27cxdxc1llp/CL_RRT_motion_planning.wmv?dl=0</p>