Behavioral Spherical Harmonics for Long-Range Agents’ Interaction

We introduce behavioral spherical harmonic (BSH), a novel approach to efficiently and compactly represent the directional-dependent behavior of agent. BSH is based on spherical harmonics to project the directional information of a group of multiple agents to a vector of few coefficients; thus, BSH drastically reduces the complexity of the directional evaluation, as it requires only few agent-group interactions instead of multiple agent-agent ones. We show how the BSH model can efficiently model intricate behaviors such as long-range collision avoidance, reaching interactive performance and avoiding agent congestion on challenging multi-groups scenarios. Furthermore, we demonstrate how both the innate parallelism and the compact coefficient representation of the BSH model are well suited for GPU architectures, showing performance analysis of our OpenCL implementation

Biomechanical Locomotion Heterogeneity in Synthetic Crowds

Synthetic crowd simulation combines rule sets at different conceptual layers to represent the dynamic nature of crowds while adhering to basic principles of human steering, such as collision avoidance and goal completion. In this dissertation, I explore synthetic crowd simulation at the steering layer using a critical approach to define the central theme of the work, the impact of model representation and agent diversity in crowds. At the steering layer, simulated agents make regular decisions, or actions, related to steering which are often responsible for the emergent behaviours found in the macro-scale crowd. Because of this bottom-up impact of a steering model's defining rule-set, I postulate that biomechanics and diverse biomechanics may alter the outcomes of dynamic synthetic-crowds-based outcomes. This would mean that an assumption of normativity and/or homogeneity among simulated agents and their mobility would provide an inaccurate representation of a scenario. If these results are then used to make real world decisions, say via policy or design, then those populations not represented in the simulated scenario may experience a lack of representation in the actualization of those decisions. A focused literature review shows that applications of both biomechanics and diverse locomotion representation at this layer of modelling are very narrow and often not present. I respond to the narrowness of this representation by addressing both biomechanics and heterogeneity separately. To address the question of performance and importance of locomotion biomechanics in crowd simulation, I use a large scale comparative approach. The industry standard synthetic crowd models are tested under a battery of benchmarks derived from prior work in comparative analysis of synthetic crowds as well as new scenarios derived from built environments. To address the question of the importance of heterogeneity in locomotion biomechanics, I define tiers of impact in the multi-agent crowds model at the steering layer--from the action space, to the agent space, to the crowds space. To this end, additional models and layers are developed to address the modelling and application of heterogeneous locomotion biomechanics in synthetic crowds. The results of both studies form a research arc which shows that the biomechanics in steering models provides important fidelity in several applications and that heterogeneity in the model of locomotion biomechanics directly impacts both qualitative and quantitative synthetic crowds outcomes. As well, systems, approaches, and pitfalls regarding the analysis of steering model and human mobility diversity are described

Safe navigation and motion coordination control strategies for unmanned aerial vehicles

Unmanned aerial vehicles (UAVs) have become very popular for many military and civilian applications including in agriculture, construction, mining, environmental monitoring, etc. A desirable feature for UAVs is the ability to navigate and perform tasks autonomously with least human interaction. This is a very challenging problem due to several factors such as the high complexity of UAV applications, operation in harsh environments, limited payload and onboard computing power and highly nonlinear dynamics. Therefore, more research is still needed towards developing advanced reliable control strategies for UAVs to enable safe navigation in unknown and dynamic environments. This problem is even more challenging for multi-UAV systems where it is more efficient to utilize information shared among the networked vehicles. Therefore, the work presented in this thesis contributes towards the state-of-the-art in UAV control for safe autonomous navigation and motion coordination of multi-UAV systems. The first part of this thesis deals with single-UAV systems. Initially, a hybrid navigation framework is developed for autonomous mobile robots using a general 2D nonholonomic unicycle model that can be applied to different types of UAVs, ground vehicles and underwater vehicles considering only lateral motion. Then, the more complex problem of three-dimensional (3D) collision-free navigation in unknown/dynamic environments is addressed. To that end, advanced 3D reactive control strategies are developed adopting the sense-and-avoid paradigm to produce quick reactions around obstacles. A special case of navigation in 3D unknown confined environments (i.e. tunnel-like) is also addressed. General 3D kinematic models are considered in the design which makes these methods applicable to different UAV types in addition to underwater vehicles. Moreover, different implementation methods for these strategies with quadrotor-type UAVs are also investigated considering UAV dynamics in the control design. Practical experiments and simulations were carried out to analyze the performance of the developed methods. The second part of this thesis addresses safe navigation for multi-UAV systems. Distributed motion coordination methods of multi-UAV systems for flocking and 3D area coverage are developed. These methods offer good computational cost for large-scale systems. Simulations were performed to verify the performance of these methods considering systems with different sizes

Analyzing Human-Building Interactions in Virtual Environments Using Crowd Simulations

This research explores the relationship between human-occupancy and environment designs by means of human behavior simulations. Predicting and analyzing user-related factors during environment designing is of vital importance. Traditional Computer-Aided Design (CAD) and Building Information Modeling (BIM) tools mostly represent geometric and semantic aspects of environment components (e.g., walls, pillars, doors, ramps, and floors). They often ignore the impact that an environment layout produces on its occupants and their movements. In recent efforts to analyze human social and spatial behaviors in buildings, researchers have started using crowd simulation techniques for dynamic analysis of urban and indoor environments. These analyses assist the designers in analyzing crowd-related factors in their designs and generating human-aware environments. This dissertation focuses on developing interactive solutions to perform spatial analytics that can quantify the dynamics of human-building interactions using crowd simulations in the virtual and built-environments. Partially, this dissertation aims to make these dynamic crowd analytics solutions available to designers either directly within mainstream environment design pipelines or as cross-platform simulation services, enabling users to seamlessly simulate, analyze, and incorporate human-centric dynamics into their design workflows

Semantic-driven modeling and reasoning for enhanced safety of cyber-physical systems

This dissertation is concerned with the development of new methodologies and semantics for model-based systems engineering (MBSE) procedures for the behavior modeling of cyber-physical systems (CPS). Our main interest is to enhance system-level safety through effective reasoning capabilities embedded in procedures for CPS design. This class of systems is defined by a tight integration of software and physical processes, the need to satisfy stringent constraints on performance, safety and a reliance on automation for the management of system functionality. Our approach employs semantic–driven modeling and reasoning : (1) for the design of cyber that can understand the physical world and reason with physical quantities, time and space, (2) to improve synthesis of component-based CPS architectures, and (3) to prevent under-specification of system requirements (the main cause of safety failures in software). We investigate and understand metadomains, especially temporal and spatial theories, and the role ontologies play in deriving formal, precise models of CPS. Description logic-based semantics and metadomain ontologies for reasoning in CPS and an integrated approach to unify the semantic foundations for decision making in CPS are covered. The research agenda is driven by Civil Systems design and operation applications, especially the dilemma zone problem. Semantic models of time and space supported respectively by Allen’s Temporal Interval Calculus (ATIC) and Region Connectedness Calculus (RCC-8) are developed and demonstrated thanks to the capabilities of Semantic Web technologies. A modular, flexible, and reusable reasoning-enabled semantic-based platform for safety-critical CPS modeling and analysis is developed and demonstrated. The platform employs formal representations of domains (cyber, physical) and metadomains (temporal and spatial) entities using decidable web ontology language (OWL) formalisms. Decidable fragments of temporal and spatial calculus are found to play a central role in the development of spatio-temporal algorithms to assure system safety. They rely on formalized safety metrics developed in the context of cyber-physical transportation systems and collision avoidance for autonomous systems. The platform components are integrated together with Whistle, a small scripting language (under development) able to process complex datatypes including physical quantities and units. The language also enables the simulation, visualization and analysis of safety tubes for collision prediction and prevention at signalized and non-signalized traffic intersections

Future Transportation

Greenhouse gas (GHG) emissions associated with transportation activities account for approximately 20 percent of all carbon dioxide (co2) emissions globally, making the transportation sector a major contributor to the current global warming. This book focuses on the latest advances in technologies aiming at the sustainable future transportation of people and goods. A reduction in burning fossil fuel and technological transitions are the main approaches toward sustainable future transportation. Particular attention is given to automobile technological transitions, bike sharing systems, supply chain digitalization, and transport performance monitoring and optimization, among others

Using human-inspired models for guiding robot locomotion

Cette thèse a été effectuée dans le cadre du projet européen Koroibot dont l'objectif est le développement d'algorithmes de marche avancés pour les robots humanoïdes. Dans le but de contrôler les robots d'une manière sûre et efficace chez les humains, il est nécessaire de comprendre les règles, les principes et les stratégies de l'homme lors de la locomotion et de les transférer à des robots. L'objectif de cette thèse est d'étudier et d'identifier les stratégies de locomotion humaine et créer des algorithmes qui pourraient être utilisés pour améliorer les capacités du robot. La contribution principale est l'analyse sur les principes de piétons qui guident les stratégies d'évitement des collisions. En particulier, nous observons comment les humains adapter une tâche de locomotion objectif direct quand ils ont à interférer avec un obstacle en mouvement traversant leur chemin. Nous montrons les différences entre la stratégie définie par les humains pour éviter un obstacle non-collaboratif et la stratégie pour éviter un autre être humain, et la façon dont les humains interagissent avec un objet si se déplaçant en manier simil à l'humaine. Deuxièmement, nous présentons un travail effectué en collaboration avec les neuroscientifiques de calcul. Nous proposons une nouvelle approche pour synthétiser réalistes complexes mouvements du robot humanoïde avec des primitives de mouvement. Trajectoires humaines walking-to-grasp ont été enregistrés. L'ensemble des mouvements du corps sont reciblées et proportionnée afin de correspondre à la cinématique de robots humanoïdes. Sur la base de cette base de données des mouvements, nous extrayons les primitives de mouvement. Nous montrons que ces signaux sources peuvent être exprimées sous forme de solutions stables d'un système dynamique autonome, qui peut être considéré comme un système de central pattern generators (CPGs). Sur la base de cette approche, les stratégies réactives walking-to-grasp ont été développés et expérimenté avec succès sur le robot humanoïde HRP-2 au LAAS-CNRS. Dans la troisième partie de la thèse, nous présentons une nouvelle approche du problème de pilotage d'un robot soumis à des contraintes non holonomes par une porte en utilisant l'asservissement visuel. La porte est représentée par deux points de repère situés sur ses supports verticaux. La plan géométric qui a été construit autour de la porte est constituée de faisceaux de hyperboles, des ellipses et des cercles orthogonaux. Nous montrons que cette géométrie peut être mesurée directement dans le plan d'image de la caméra et que la stratégie basée sur la vision présentée peut également être lié à l'homme. Simulation et expériences réalistes sont présentés pour montrer l'efficacité de nos solutions.This thesis has been done within the framework of the European Project Koroibot which aims at developing advanced algorithms to improve the humanoid robots locomotion. It is organized in three parts. With the aim of steering robots in a safe and efficient manner among humans it is required to understand the rules, principles and strategies of human during locomotion and transfer them to robots. The goal of this thesis is to investigate and identify the human locomotion strategies and create algorithms that could be used to improve robot capabilities. A first contribution is the analysis on pedestrian principles which guide collision avoidance strategies. In particular, we observe how humans adapt a goal-direct locomotion task when they have to interfere with a moving obstacle crossing their way. We show differences both in the strategy set by humans to avoid a non-collaborative obstacle with respect to avoid another human, and the way humans interact with an object moving in human-like way. Secondly, we present a work done in collaboration with computational neuroscientists. We propose a new approach to synthetize realistic complex humanoid robot movements with motion primitives. Human walking-to-grasp trajectories have been recorded. The whole body movements are retargeted and scaled in order to match the humanoid robot kinematics. Based on this database of movements, we extract the motion primitives. We prove that these sources signals can be expressed as stable solutions of an autonomous dynamical system, which can be regarded as a system of coupled central pattern generators (CPGs). Based on this approach, reactive walking-to-grasp strategies have been developed and successfully experimented on the humanoid robot HRP at LAAS-CNRS. In the third part of the thesis, we present a new approach to the problem of vision-based steering of robot subject to non-holonomic constrained to pass through a door. The door is represented by two landmarks located on its vertical supports. The planar geometry that has been built around the door consists of bundles of hyperbolae, ellipses, and orthogonal circles. We prove that this geometry can be directly measured in the camera image plane and that the proposed vision-based control strategy can also be related to human. Realistic simulation and experiments are reported to show the effectiveness of our solutions

A cooperative navigation system with distributed architecture for multiple unmanned aerial vehicles

Unmanned aerial vehicles (UAVs) have been widely used in many applications due to, among other features, their versatility, reduced operating cost, and small size. These applications increasingly demand that features related to autonomous navigation be employed, such as mapping. However, the reduced capacity of resources such as, for example, battery and hardware (memory and processing units) can hinder the development of these applications in UAVs. Thus, the collaborative use of multiple UAVs for mapping can be used as an alternative to solve this problem, with a cooperative navigation system. This system requires that individual local maps be transmitted and merged into a global map in a distributed manner. In this scenario, there are two main problems to be addressed: the transmission of maps among the UAVs and the merging of the local maps in each UAV. In this context, this work describes the design, development, and evaluation of a cooperative navigation system with distributed architecture to be used by multiple UAVs. This system uses proposed structures to store the 3D occupancy grid maps. Furthermore, maps are compressed and transmitted between UAVs using algorithms specially proposed for these purposes. Then the local 3D maps are merged in each UAV. In this map merging system, maps are processed before and merged in pairs using suitable algorithms to make them compatible with the 3D occupancy grid map data. In addition, keypoints orientation properties are obtained from potential field gradients. Some proposed filters are used to improve the parameters of the transformations among maps. To validate the proposed solution, simulations were performed in six different environments, outdoors and indoors, and with different layout characteristics. The obtained results demonstrate the effectiveness of thesystemin the construction, sharing, and merging of maps. Still, from the obtained results, the extreme complexity of map merging systems is highlighted.Os veículos aéreos não tripulados (VANTs) têm sidoamplamenteutilizados em muitas aplicações devido, entre outrosrecursos,à sua versatilidade, custo de operação e tamanho reduzidos. Essas aplicações exigem cadavez mais que recursos relacionados à navegaçãoautônoma sejam empregados,como o mapeamento. No entanto, acapacidade reduzida de recursos como, por exemplo, bateria e hardware (memória e capacidade de processamento) podem atrapalhar o desenvolvimento dessas aplicações em VANTs.Assim, o uso colaborativo de múltiplosVANTs para mapeamento pode ser utilizado como uma alternativa para resolvereste problema, criando um sistema de navegaçãocooperativo. Estesistema requer que mapas locais individuais sejam transmitidos efundidos em um mapa global de forma distribuída.Nesse cenário, há doisproblemas principais aserem abordados:a transmissão dosmapas entre os VANTs e afusão dos mapas locais em cada VANT. Nestecontexto, estatese apresentao projeto, desenvolvimento e avaliaçãode um sistema de navegação cooperativo com arquitetura distribuída para ser utilizado pormúltiplos VANTs. Este sistemausa estruturas propostas para armazenaros mapasdegradedeocupação 3D. Além disso, os mapas são compactados e transmitidos entre os VANTs usando os algoritmos propostos. Em seguida, os mapas 3D locais são fundidos em cada VANT. Neste sistemade fusão de mapas, os mapas são processados antes e juntados em pares usando algunsalgoritmos adequados para torná-los compatíveiscom os dados dos mapas da grade de ocupação 3D. Além disso, as propriedadesde orientação dos pontoschave são obtidas a partir de gradientes de campos potenciais. Alguns filtros propostos são utilizadospara melhorar as indicações dos parâmetros dastransformações entre mapas. Paravalidar a aplicação proposta, foram realizadas simulações em seis ambientes distintos, externos e internos, e com características construtivas distintas. Os resultados apresentados demonstram a efetividade do sistema na construção, compartilhamento e fusão dos mapas. Ainda, a partir dos resultados obtidos, destaca-se a extrema complexidade dos sistemas de fusão de mapas