This paper explores the motion planning problem for multiple moving objects. The approach taken consists of assigning priorities to the objects, then planning motions one object at a time. For each moving object, the planner constructs a configuration space-time that represents the time-varying constraints imposed on the moving object by the other moving and stationary objects. The planner represents this space-time approximately, using two-dimensional slices. The space-time is then searched for a collision-free path. The paper demonstrates this approach in two domains. One domain consists of translating planar objects; the other domain consists of two-link planar articulated arms.

This thesis presents methods for implementing robust hexpod locomotion on an autonomous robot with many sensors and actuators. The controller is based on the Subsumption Architecture and is fully distributed over approximately 1500 simple, concurrent processes. The robot, Hannibal, weighs approximately 6 pounds and is equipped with over 100 physical sensors, 19 degrees of freedom, and 8 on board computers. We investigate the following topics in depth: distributed control of a complex robot, insect-inspired locomotion control for gait generation and rough terrain mobility, and fault tolerance. The controller was implemented, debugged, and tested on Hannibal. Through a series of experiments, we examined Hannibal's gait generation, rough terrain locomotion, and fault tolerance performance. These results demonstrate that Hannibal exhibits robust, flexible, real-time locomotion over a variety of terrain and tolerates a multitude of hardware failures.

Modern applications of mobile robots require them to have the ability to safely and effectively navigate in human environments. New challenges arise when these robots must plan their motion in a human-aware fashion. Current methods addressing this problem have focused mainly on the activity forecasting aspect, aiming at improving predictions without considering the active nature of the interaction, i.e. the robot’s effect on the environment and consequent issues such as reciprocity. Furthermore, many methods rely on computationally expensive offline training of predictive models that may not be well suited to rapidly evolving dynamic environments. This thesis presents a novel approach for enabling autonomous robots to navigate socially in environments with humans. Following formulations of the inverse planning problem, agents reason about the intentions of other agents and make predictions about their future interactive motion. A technique is proposed to implement counterfactual reasoning over a parametrised set of light-weight reciprocal motion models, thus making it more tractable to maintain beliefs over the future trajectories of other agents towards plausible goals. The speed of inference and the effectiveness of the algorithms is demonstrated via physical robot experiments, where computationally constrained robots navigate amongst humans in a distributed multi-sensor setup, able to infer other agents’ intentions as fast as 100ms after the first observation. While intention inference is a key aspect of successful human-robot interaction, executing any task requires planning that takes into account the predicted goals and trajectories of other agents, e.g., pedestrians. It is well known that robots demonstrate unwanted behaviours, such as freezing or becoming sluggishly responsive, when placed in dynamic and cluttered environments, due to the way in which safety margins according to simple heuristics end up covering the entire feasible space of motion. The presented approach makes more refined predictions about future movement, which enables robots to find collision-free paths quickly and efficiently. This thesis describes a novel technique for generating "interactive costmaps", a representation of the planner’s costs and rewards across time and space, providing an autonomous robot with the information required to navigate socially given the estimate of other agents’ intentions. This multi-layered costmap deters the robot from obstructing while encouraging social navigation respectful of other agents’ activity. Results show that this approach minimises collisions and near-collisions, minimises travel times for agents, and importantly offers the same computational cost as the most common costmap alternatives for navigation. A key part of the practical deployment of such technologies is their ease of implementation and configuration. Since every use case and environment is different and distinct, the presented methods use online adaptation to learn parameters of the navigating agents during runtime. Furthermore, this thesis includes a novel technique for allocating tasks in distributed robotics systems, where a tool is provided to maximise the performance on any distributed setup by automatic parameter tuning. All of these methods are implemented in ROS and distributed as open-source. The ultimate aim is to provide an accessible and efficient framework that may be seamlessly deployed on modern robots, enabling widespread use of intention prediction for interactive navigation in distributed robotic systems.

In autonomous robotics, there is still a trend to develop and tune controllers with highly explicit goals and environments in mind. However, this tuning means that these robotic models often lack the developmental and behavioral flexibility seen in biological organisms. The lack of flexibility in these controllers leaves the robot vulnerable to changes in environmental condition. Whereby any environmental change may lead to the behaviors of the robots becoming unsuitable or even dangerous. In this manuscript we look at a potential biologically plausible mechanism which may be used in robotic controllers in order to allow them to adapt to different environments. This mechanism consists of a hormone driven epigenetic mechanism which regulates a robot's internal environment in relation to its current environmental conditions. As we will show in our early chapters, this epigenetic mechanism allows an autonomous robot to rapidly adapt to a range of different environmental conditions. This adaption is achieved without the need for any explicit knowledge of the environment. Allowing a single architecture to adapt to a range of challenges and develop unique behaviors. In later chapters however, we find that this mechanism not only allows for regulation of short term behavior, but also long development. Here we show how this system permits a robot to develop in a way that is suitable for its current environment. Further during this developmental process we notice similarities to infant development, along with acquisition of unplanned skills and abilities. The unplanned developments appears to leads to the emergence of unplanned potential cognitive abilities such as object permanence, which we assess using a range of different real world tests.

O problema da localização e mapeamento simultâneos, conhecido como problema do SLAM, é um dos maiores desafios que a robótica móvel autônoma enfrenta atualmente. Esse problema surge devido à dificuldade que um robô apresenta ao navegar por um ambiente desconhecido, construindo um mapa das regiões por onde já passou ao mesmo tempo em que se localiza dentro dele. O acúmulo de erros gerados pela imprecisão dos sensores utilizados para estimar os estados de localização e mapeamento impede que sejam obtidos resultados confiáveis após períodos de navegação suficientemente longos. Algoritmos de SLAM procuram eliminar esses erros resolvendo ambos os problemas simultaneamente, utilizando as informações de uma etapa para aumentar a precisão dos resultados alcançados na outra e viceversa. Uma das maneiras de se alcançar isso se baseia no estabelecimento de marcos no ambiente que o robô pode utilizar como pontos de referência para se localizar conforme navega. Esse trabalho apresenta uma solução para o problema do SLAM que faz uso de um sensor de visão omnidirecional para estabelecer esses marcos. O uso de sistemas de visão permite a extração de marcos naturais ao ambiente que podem ser correspondidos de maneira robusta sob diferentes pontos de vista. A visão omnidirecional amplia o campo de visão do robô e com isso aumenta a quantidade de marcos observados a cada instante. Ao ser detectado o marco é adicionado ao mapa que robô possui do ambiente e, ao ser reconhecido, o robô pode utilizar essa informação para refinar suas estimativas de localização e mapeamento, eliminando os erros acumulados e conseguindo mantê-las precisas mesmo após longos períodos de navegação. Essa solução foi testada em situações reais de navegação, e os resultados mostram uma melhora significativa nos resultados alcançados em relação àqueles obtidos com a utilização direta das informações coletadas. / The problem of simultaneous localization and mapping, known as the problem of SLAM, is one of the greatest obstacles that the field of autonomous robotics faces nowadays. This problem is related to a robots ability to navigate through an unknown environment, constructing a map of the regions it has already visited at the same time as localizing itself on this map. The imprecision inherent to the sensors used to collect information generates errors that accumulate over time, not allowing for a precise estimation of localization and mapping when used directly. SLAM algorithms try to eliminate these errors by taking advantage of their mutual dependence and solving both problems simultaneously, using the results of one step to refine the estimatives of the other. One possible way to achieve this is the establishment of landmarks in the environment that the robot can use as points of reference to localize itself while it navigates. This work presents a solution to the problem of SLAM using an omnidirectional vision system to detect these landmarks. The choice of visual sensors allows for the extraction of natural landmarks and robust matching under different points of view, as the robot moves through the environment. The omnidirectional vision amplifies the field of vision of the robot, increasing the number of landmarks observed at each instant. The detected landmarks are added to the map, and when they are later recognized they generate information that the robot can use to refine its estimatives of localization and mapping, eliminating accumulated errors and keeping them precise even after long periods of navigation. This solution has been tested in real navigational situations and the results show a substantial improvement in the results compared to those obtained through the direct use of the information collected.

The master’s thesis implements localization and navigation routines for mobile robot B2 in order to operate autonomously in an environment described by a road map only. The ROS framework was used for developing new software. The research part describes possible approaches to localization problem and summarizes ROS packages with localization and navigation software. The following part includes communication with the robot’s sensor modules and data processing from LIDAR, IMU and camera. The localization package robot_localization based on Kalman filter is implemented and setting of the navigation stack navigation is proposed, aiming to robot’s autonomous outdoor navigation. Implemented functions were tested in park environment and they are evaluated in this master's thesis too.

This master’s thesis deals with the navigation of a mobile robot that uses the ROS framework. The aim is to improve the ability of the existing B2 robot to move autonomously outdoors. The theoretical part contains a description of the navigation core, which consists of the move_base library and the packages used for planning. The practical part describe the aws of the existing solution, the design and implementation of changes and the results of subsequent testing in the urban park environment.

La navigation autonome est un axe de recherche principal dans le domaine de la robotique mobile. Dans ce contexte, le robot doit disposer des algorithmes qui lui permettent d’évoluer de manière autonome dans des environnements complexes et inconnus. Les algorithmes de SLAM permettent à un robot de cartographier son environnement tout en se localisant dans l’espace. Les algorithmes SLAM sont de plus en plus performants, mais aucune implémentation matérielle ou architecturale complète n’a eu. Une telle implantation d’architecture doit prendre en considération la consommation d’énergie, l’embarquabilité et la puissance de calcul. Ce travail scientifique vise à évaluer des systèmes embarqués impliquant de la localisation ou reconstruction de scène. La méthodologie adoptera une approche A3 (Adéquation Algorithme Architecture) pour améliorer l’efficacité de l’implantation des algorithmes plus particulièrement pour des systèmes à fortes contraintes. Le système SLAM embarqué doit disposer d’une architecture électronique et logicielle permettant d’assurer la production d’information pertinentes à partir de données capteurs, tout en assurant la localisation de l’embarquant dans son environnement. L’objectif est donc de définir, pour un algorithme choisi, un modèle d’architecture répondant aux contraintes de l’embarqué. Les premiers travaux de cette thèse ont consisté à explorer les différentes approches algorithmiques permettant la résolution du problème de SLAM. Une étude plus approfondie de ces algorithmes est réalisée. Ceci nous a permet d’évaluer quatre algorithmes de différente nature : FastSLAM2.0, ORB SLAM, RatSLAM et le SLAM linéaire. Ces algorithmes ont été ensuite évalués sur plusieurs architectures pour l’embarqué afin d’étudier leur portabilité sur des systèmes de faible consommation énergétique et de ressources limitées. La comparaison prend en compte les temps d’exécutions et la consistance des résultats. Après avoir analysé profondément les évaluations temporelles de chaque algorithme, le FastSLAM2.0 est finalement choisi, pour un compromis temps d’exécution-consistance de résultat de localisation, comme candidat pour une étude plus approfondie sur une architecture hétérogène embarquée. La second partie de cette thèse est consacré à l’étude d’un système embarqué implémentant le FastSLAM2.0 monoculaire dédié aux environnements larges. Une réécriture algorithmique du FastSLAM2.0 a été nécessaire afin de l’adapter au mieux aux contraintes imposées par les environnements de grande échelle. Dans une démarche A3, le FastSLAM2.0 a été implanté sur une architecture hétérogène CPU-GPU. Grâce à un partitionnement efficace, un facteur d’accélération global de l’ordre de 22 a été obtenu sur une architecture récente dédiée pour l’embarqué. La nature du traitement de l’algorithme FastSLAM2.0 pouvait bénéficier d’une architecture fortement parallèle. Une deuxième instance matérielle basée sur une architecture programmable FPGA est proposée. L’implantation a été réalisée en utilisant des outils de synthèse de haut-niveau afin de réduire le temps de développement. Une comparaison des résultats d’implantation sur cette architecture matérielle par rapport à des architectures à base de GPU a été réalisée. Les gains obtenus sont conséquent, même par rapport aux GPU haut-de-gamme avec un grand nombre de cœurs. Le système résultant peut cartographier des environnements larges tout en garantissant le compromis entre la consistance des résultats de localisation et le temps réel. L’utilisation de plusieurs calculateurs implique d’utiliser des moyens d’échanges de données entre ces derniers. Cela passe par des couplages forts. Ces travaux de thèse ont permis de mettre en avant l’intérêt des architectures hétérogènes parallèles pour le portage des algorithmes SLAM. Les architectures hétérogènes à base de FPGA peuvent particulièrement devenir des candidats potentiels pour porter des algorithmes complexes traitant des données massives. / Autonomous navigation is a main axis of research in the field of mobile robotics. In this context, the robot must have an algorithm that allow the robot to move autonomously in a complex and unfamiliar environments. Mapping in advance by a human operator is a tedious and time consuming task. On the other hand, it is not always reliable, especially when the structure of the environment changes. SLAM algorithms allow a robot to map its environment while localizing it in the space.SLAM algorithms are becoming more efficient, but there is no full hardware or architectural implementation that has taken place . Such implantation of architecture must take into account the energy consumption, the embeddability and computing power. This scientific work aims to evaluate the embedded systems implementing locatization and scene reconstruction (SLAM). The methodology will adopt an approach AAM ( Algorithm Architecture Matching) to improve the efficiency of the implementation of algorithms especially for systems with high constaints. SLAM embedded system must have an electronic and software architecture to ensure the production of relevant data from sensor information, while ensuring the localization of the robot in its environment. Therefore, the objective is to define, for a chosen algorithm, an architecture model that meets the constraints of embedded systems. The first work of this thesis was to explore the different algorithmic approaches for solving the SLAM problem. Further study of these algorithms is performed. This allows us to evaluate four different kinds of algorithms: FastSLAM2.0, ORB SLAM, SLAM RatSLAM and linear. These algorithms were then evaluated on multiple architectures for embedded systems to study their portability on energy low consumption systems and limited resources. The comparison takes into account the time of execution and consistency of results. After having deeply analyzed the temporal evaluations for each algorithm, the FastSLAM2.0 was finally chosen for its compromise performance-consistency of localization result and execution time, as a candidate for further study on an embedded heterogeneous architecture. The second part of this thesis is devoted to the study of an embedded implementing of the monocular FastSLAM2.0 which is dedicated to large scale environments. An algorithmic modification of the FastSLAM2.0 was necessary in order to better adapt it to the constraints imposed by the largescale environments. The resulting system is designed around a parallel multi-core architecture. Using an algorithm architecture matching approach, the FastSLAM2.0 was implemeted on a heterogeneous CPU-GPU architecture. Uisng an effective algorithme partitioning, an overall acceleration factor o about 22 was obtained on a recent dedicated architecture for embedded systems. The nature of the execution of FastSLAM2.0 algorithm could benefit from a highly parallel architecture. A second instance hardware based on programmable FPGA architecture is proposed. The implantation was performed using high-level synthesis tools to reduce development time. A comparison of the results of implementation on the hardware architecture compared to GPU-based architectures was realized. The gains obtained are promising, even compared to a high-end GPU that currently have a large number of cores. The resulting system can map a large environments while maintainingthe balance between the consistency of the localization results and real time performance. Using multiple calculators involves the use of a means of data exchange between them. This requires strong coupling (communication bus and shared memory). This thesis work has put forward the interests of parallel heterogeneous architectures (multicore, GPU) for embedding the SLAM algorithms. The FPGA-based heterogeneous architectures can particularly become potential candidatesto bring complex algorithms dealing with massive data.

Numerous optimization problems in production systems can be considered as decision-making processes that determine the best allocation of resources to tasks over time to optimize one or more objectives in concert with big data. Among the optimization problems, production scheduling and routing of robots for material handling are becoming more important due to their impacts on system performance. However, the development of efficient algorithms for scheduling or routing faces several challenges. While the scheduling and vehicle routing problems can be solved by mathematical models such as mixed-integer linear programming to find optimal solutions to smallsized problems, they are not applicable to larger problems due to the nature of NP-hard problems. Thus, further research on machine learning applications to those problems is a significant step towards increasing the possibilities and potentialities of field application. In order to create truly intelligent systems, new frameworks for scheduling and routing are proposed to utilize machine learning (ML) techniques. First, the dynamic single-machine scheduling problem for minimization of total weighted tardiness is addressed. In order to solve the problem more efficiently, a decisiontree-based approach called Generation of Rules Automatically with Feature construction and Treebased learning (GRAFT) is designed to extract dispatching rules from existing or good schedules. In addition to the single-machine scheduling problem, the flexible job-shop scheduling problem with release times for minimizing the total weighted tardiness is analyzed. As a ML-based solution approach, a random-forest-based approach called Random Forest for Obtaining Rules for Scheduling (RANFORS) is developed to solve the problem by generating dispatching rules automatically. Finally, an optimization problem for routing of autonomous robots for minimizing total tardiness of transportation requests is analyzed by decomposing it into three sub-problems. In order to solve the sub-problems, a comprehensive framework with consideration of conflicts between routes is proposed. Especially to the sub-problem for vehicle routing, a new local search algorithm called COntextual-Bandit-based Adaptive Local search with Tree-based regression (COBALT) that incorporates the contextual bandit into operator selection is developed. The findings from my research contribute to suggesting a guidance to practitioners for the applications of ML to scheduling and control problems, and ultimately to lead the implementation of smart factories.

The field of robotics has always been one of fascination right from the day of Terminator. Even though we still do not have robots that can actually replicate human action and intelligence, progress is being made in the right direction. Robotic applications range from defense to civilian, in public safety and fire fighting. With the increase in urban-warfare robot tracking inside buildings and in cities form a very important application. The numerous applications range from munitions tracking to replacing soldiers for reconnaissance information. Fire fighters use robots for survey of the affected area. Tracking robots has been limited to the local area under consideration. Decision making is inhibited due to limited local knowledge and approximations have to be made. An effective decision making would involve tracking the robot in earth co-ordinates such as latitude and longitude. GPS signal provides us sufficient and reliable data for such decision making. The main drawback of using GPS is that it is unavailable indoors and also there is signal attenuation outdoors. Indoor geolocation forms the basis of tracking robots inside buildings and other places where GPS signals are unavailable. Indoor geolocation has traditionally been the field of wireless networks using techniques such as low frequency RF signals and ultra-wideband antennas. In this thesis we propose a novel method for achieving geolocation and enable tracking. Geolocation and tracking are achieved by a combination of Gyroscope and encoders together referred to as the Inertial Navigation System (INS). Gyroscopes have been widely used in aerospace applications for stabilizing aircrafts. In our case we use gyroscope as means of determining the heading of the robot. Further, commands can be sent to the robot when it is off balance or off-track. Sensors are inherently error prone; hence the process of geolocation is complicated and limited by the imperfect mathematical modeling of input noise. We make use of Kalman Filter for processing erroneous sensor data, as it provides us a robust and stable algorithm. The error characteristics of the sensors are input to the Kalman Filter and filtered data is obtained. We have performed a large set of experiments, both indoors and outdoors to test the reliability of the system. In outdoors we have used the GPS signal to aid the INS measurements. When indoors we utilize the last known position and extrapolate to obtain the GPS co-ordinates.