Long-range navigation in complex and dynamic environments with Full-Stack S-DOVS

Robotic autonomous navigation in dynamic environments is a complex problem, as traditional planners may fail to take dynamic obstacles and their variables into account. The Strategy-based Dynamic Object Velocity Space (S-DOVS) planner has been proposed as a solution to navigate in such scenarios. However, it has a number of limitations, such as inability to reach a goal in a large known map, avoid convex objects, or handle trap situations. In this article, we present a modified version of the S-DOVS planner that is integrated into a full navigation stack, which includes a localization system, obstacle tracker, and novel waypoint generator. The complete system takes into account robot kinodynamic constraints and is capable of navigating through large scenarios with known map information in the presence of dynamic obstacles. Extensive simulation and ground robot experiments demonstrate the effectiveness of our system even in environments with dynamic obstacles and replanning requirements, and show that our waypoint generator outperforms other approaches in terms of success rate and time to reach the goal when combined with the S-DOVS planner. Overall, our work represents a step forward in the development of robust and reliable autonomous navigation systems for real-world scenarios

Motion planning with dynamics awareness for long reach manipulation in aerial robotic systems with two arms

Human activities in maintenance of industrial plants pose elevated risks as well as significant costs due to the required shutdowns of the facility. An aerial robotic system with two arms for long reach manipulation in cluttered environments is presented to alleviate these constraints. The system consists of a multirotor with a long bar extension that incorporates a lightweight dual arm in the tip. This configuration allows aerial manipulation tasks even in hard-to-reach places. The objective of this work is the development of planning strategies to move the aerial robotic system with two arms for long reach manipulation in a safe and efficient way for both navigation and manipulation tasks. The motion planning problem is addressed considering jointly the aerial platform and the dual arm in order to achieve wider operating conditions. Since there exists a strong dynamical coupling between the multirotor and the dual arm, safety in obstacle avoidance will be assured by introducing dynamics awareness in the operation of the planner. On the other hand, the limited maneuverability of the system emphasizes the importance of energy and time efficiency in the generated trajectories. Accordingly, an adapted version of the optimal Rapidly-exploring Random Tree algorithm has been employed to guarantee their optimality. The resulting motion planning strategy has been evaluated through simulation in two realistic industrial scenarios, a riveting application and a chimney repairing task. To this end, the dynamics of the aerial robotic system with two arms for long reach manipulation has been properly modeled, and a distributed control scheme has been derived to complete the test bed. The satisfactory results of the simulations are presented as a first validation of the proposed approach.Unión Europea H2020-644271Ministerio de Ciencia, Innovación y Universidades DPI2014-59383-C2-1-

Simulation of Rapidly-Exploring Random Trees in Membrane Computing with P-Lingua and Automatic Programming

Methods based on Rapidly-exploring Random Trees (RRTs) have been widely used in robotics to solve motion planning problems. On the other hand, in the membrane computing framework, models based on Enzymatic Numerical P systems (ENPS) have been applied to robot controllers, but today there is a lack of planning algorithms based on membrane computing for robotics. With this motivation, we provide a variant of ENPS called Random Enzymatic Numerical P systems with Proteins and Shared Memory (RENPSM) addressed to implement RRT algorithms and we illustrate it by simulating the bidirectional RRT algorithm. This paper is an extension of [21]a. The software presented in [21] was an ad-hoc simulator, i.e, a tool for simulating computations of one and only one model that has been hard-coded. The main contribution of this paper with respect to [21] is the introduction of a novel solution for membrane computing simulators based on automatic programming. First, we have extended the P-Lingua syntax –a language to define membrane computing models– to write RENPSM models. Second, we have implemented a new parser based on Flex and Bison to read RENPSM models and produce source code in C language for multicore processors with OpenMP. Finally, additional experiments are presented.Ministerio de Economía, Industria y Competitividad TIN2017-89842-

Decentralized Multi-Robot Social Navigation in Constrained Environments via Game-Theoretic Control Barrier Functions

We present an approach to ensure safe and deadlock-free navigation for decentralized multi-robot systems operating in constrained environments, including doorways and intersections. Although many solutions have been proposed to ensure safety, preventing deadlocks in a decentralized fashion with global consensus remains an open problem. We first formalize the objective as a non-cooperative, non-communicative, partially observable multi-robot navigation problem in constrained spaces with multiple conflicting agents, which we term as social mini-games. Our approach to ensuring safety and liveness rests on two novel insights: (i) deadlock resolution is equivalent to deriving a mixed-Nash equilibrium solution to a social mini-game and (ii) this mixed-Nash strategy can be interpreted as an analogue to control barrier functions (CBFs), that can then be integrated with standard CBFs, inheriting their safety guarantees. Together, the standard CBF along with the mixed-Nash CBF analogue preserves both safety and liveness. We evaluate our proposed game-theoretic navigation algorithm in simulation as well on physical robots using F1/10 robots, a Clearpath Jackal, as well as a Boston Dynamics Spot in a doorway, corridor intersection, roundabout, and hallway scenario. We show that (i) our approach results in safer and more efficient navigation compared to local planners based on geometrical constraints, optimization, multi-agent reinforcement learning, and auctions, (ii) our deadlock resolution strategy is the smoothest in terms of smallest average change in velocity and path deviation, and most efficient in terms of makespan (iii) our approach yields a flow rate of 2.8 - 3.3 (ms)^{-1 which is comparable to flow rate in human navigation at 4 (ms)^{-1}.Comment: arXiv admin note: text overlap with arXiv:2306.0881

Towards autonomous robotic systems: seamless localization and trajectory planning in dynamic environments

Evolucionar hacia una sociedad más automatizada y robotizada en la que podamos convivir con sistemas robóticos que desempeñen tareas poco atractivas o peligrosas para el ser humano, supone plantearnos, entre otras cuestiones, qué soluciones existen actualmente y cuáles son las mejoras a incorporar a las mismas. La mayoría de aplicaciones ya desarrolladas son soluciones robustas y adecuadas para el fin que se diseñan. Sin embargo, muchas de las técnicas implantadas podrían funcionar de manera más eficiente o bien adaptarse a otras necesidades. Asimismo, en la mayoría de aplicaciones robóticas adquiere importancia el contexto en el que desempeñan su función. Hay entornos estructurados y fáciles de modelar, mientras que otros apenas presentan características utilizables para obtener información de los mismos.Esta tesis se centra en dos de las funciones básicas que debe tener cualquier sistema robótico autónomo para desplazarse de forma robusta en cualquier tipo de entorno: la localización y el cálculo de trayectorias seguras. Además, los escenarios en los que se desea poner en práctica la investigación son complejos: un parque industrial con zonas cuyas características de entorno (usualmente geométricas) son utilizadas para que un robot se localice, varían; y entornos altamente ocupados por otros agentes móviles, como el vestíbulo de un teatro, en los que se debe considerar las características dinámicas de los demás para calcular un movimiento que sea seguro tanto para el robot como para los demás agentes.La información que se puede percibir de los escenarios con ambientes no homogéneos, por ejemplo de interior y exterior, suele ser de características diferentes. Cuando la información que se dispone del entorno proviene de sensores diferentes hay que definir un método que integre las medidas para tener una estimación de la localización del robot en todo momento. El tema de la localización se ha investigado intensamente y existen soluciones robustas en interior y exterior, pero no tanto en zonas mixtas. En las zonas de transición interior-exterior y viceversa es necesario utilizar sensores que funcionan correctamente en ambas zonas, realizando una integración sensorial durante la transición para evitar discontinuidades en la localización o incluso que el robot se pierda. De esta manera la navegación autónoma, dependiente de la correcta localización, funcionará sin discontinuidades ni movimientos bruscos.En entornos dinámicos es esencial definir una forma de representar la información que refleje su naturaleza cambiante. Por ello, se han definido en la literatura diferentes modelos que representan el dinamismo del entorno, y que permiten desarrollar una planificación de trayectorias directamente sobre las variables que controlan el movimiento del robot, en nuestro caso, las velocidades angular y lineal para un robot diferencial. Los planificadores de trayectorias y navegadores diseñados para entornos estáticos no funcionan correctamente en escenarios dinámicos, ya que son puramente reactivos. Es necesario tener en cuenta la predicción del movimiento de los obstáculos móviles para planificar trayectorias seguras sin colisión. Los temas abordados y las contribuciones aportadas en esta tesis son:• Diseño de un sistema de localización continua en entornos de interior y exterior, poniendo especial interés en la fusión de las medidas obtenidas de diferentes sensores durante las transiciones interior-exterior, aspecto poco abordado en la literatura. De esta manera se obtiene una estimación acotada de la localización durante toda la navegación del robot. Además, la localización se integra con una técnica reactiva de navegación, construyendo un sistema completo de navegación. El sistema integrado se ha evaluado en un escenario real de un parque industrial, para una aplicación logística en la que las transiciones interior-exterior y viceversa suponían un problema fundamental a resolver.• Definición de un modelo para representar el entorno dinámico del robot, llamado Dynamic Obstacle Velocity-Time Space (DOVTS). En este modelo aparecen representadas las velocidades permitidas y prohibidas para que el robot evite las colisiones con los obstáculos de alrededor. Este modelo puede ser utilizado por algoritmos de navegación ya existentes, y sirve de base para las nuevas técnicas de navegación desarrolladas en la tesis y explicadas en los siguientes puntos. • Desarrollo de una técnica de planificación y navegación basada en el modelo DOVTS. En este modelo se identifica un conjunto de situaciones relativas entre el robot y los obstáculos. A cada situación se asocia una estrategia de navegación, que considera la seguridad del robot para evitar colisiones, a la vez que intenta minimizar el tiempo al objetivo.• Implementación de una técnica de planificación y navegación basada en el modelo DOVTS, que utiliza explícitamente la información del tiempo para la planificación del movimiento. Se desarrolla un algoritmo A*-like que planifica los movimientos de los siguientes instantes, incrementando la maniobrabilidad del robot para la evitación de obstáculos respecto al método del anterior punto, a costa de un mayor tiempo de cómputo. Se analizan las diferencias en el comportamiento global del robot con respecto a la técnica anterior.Los diferentes aspectos que se han investigado en esta tesis tratan de avanzar en el objetivo de conseguir robots autónomos que puedan adaptarse a nuestra vida cotidiana en escenarios que son típicamente dinámicos de una forma natural y segura.<br /

Autonomous Navigation for Unmanned Aerial Systems - Visual Perception and Motion Planning

L'abstract è presente nell'allegato / the abstract is in the attachmen

Human-aware space sharing and navigation for an interactive robot

Les méthodes de planification de mouvements robotiques se sont développées à un rythme accéléré ces dernières années. L'accent a principalement été mis sur le fait de rendre les robots plus efficaces, plus sécurisés et plus rapides à réagir à des situations imprévisibles. En conséquence, nous assistons de plus en plus à l'introduction des robots de service dans notre vie quotidienne, en particulier dans les lieux publics tels que les musées, les centres commerciaux et les aéroports. Tandis qu'un robot de service mobile se déplace dans l'environnement humain, il est important de prendre en compte l'effet de son comportement sur les personnes qu'il croise ou avec lesquelles il interagit. Nous ne les voyons pas comme de simples machines, mais comme des agents sociaux et nous nous attendons à ce qu'ils se comportent de manière similaire à l'homme en suivant les normes sociétales comme des règles. Ceci a créé de nouveaux défis et a ouvert de nouvelles directions de recherche pour concevoir des algorithmes de commande de robot, qui fournissent des comportements de robot acceptables, lisibles et proactifs. Cette thèse propose une méthode coopérative basée sur l'optimisation pour la planification de trajectoire et la navigation du robot avec des contraintes sociales intégrées pour assurer des mouvements de robots prudents, conscients de la présence de l'être humain et prévisibles. La trajectoire du robot est ajustée dynamiquement et continuellement pour satisfaire ces contraintes sociales. Pour ce faire, nous traitons la trajectoire du robot comme une bande élastique (une construction mathématique représentant la trajectoire du robot comme une série de positions et une différence de temps entre ces positions) qui peut être déformée (dans l'espace et dans le temps) par le processus d'optimisation pour respecter les contraintes données. De plus, le robot prédit aussi les trajectoires humaines plausibles dans la même zone d'exploitation en traitant les chemins humains aussi comme des bandes élastiques. Ce système nous permet d'optimiser les trajectoires des robots non seulement pour le moment présent, mais aussi pour l'interaction entière qui se produit lorsque les humains et les robots se croisent les uns les autres. Nous avons réalisé un ensemble d'expériences avec des situations interactives humains-robots qui se produisent dans la vie de tous les jours telles que traverser un couloir, passer par une porte et se croiser sur de grands espaces ouverts. La méthode de planification coopérative proposée se compare favorablement à d'autres schémas de planification de la navigation à la pointe de la technique. Nous avons augmenté le comportement de navigation du robot avec un mouvement synchronisé et réactif de sa tête. Cela permet au robot de regarder où il va et occasionnellement de détourner son regard vers les personnes voisines pour montrer que le robot va éviter toute collision possible avec eux comme prévu par le planificateur. À tout moment, le robot pondère les multiples critères selon le contexte social et décide de ce vers quoi il devrait porter le regard. Grâce à une étude utilisateur en ligne, nous avons montré que ce mécanisme de regard complète efficacement le comportement de navigation ce qui améliore la lisibilité des actions du robot. Enfin, nous avons intégré notre schéma de navigation avec un système de supervision plus large qui peut générer conjointement des comportements du robot standard tel que l'approche d'une personne et l'adaptation de la vitesse du robot selon le groupe de personnes que le robot guide dans des scénarios d'aéroport ou de musée.The methods of robotic movement planning have grown at an accelerated pace in recent years. The emphasis has mainly been on making robots more efficient, safer and react faster to unpredictable situations. As a result we are witnessing more and more service robots introduced in our everyday lives, especially in public places such as museums, shopping malls and airports. While a mobile service robot moves in a human environment, it leaves an innate effect on people about its demeanor. We do not see them as mere machines but as social agents and expect them to behave humanly by following societal norms and rules. This has created new challenges and opened new research avenues for designing robot control algorithms that deliver human-acceptable, legible and proactive robot behaviors. This thesis proposes a optimization-based cooperative method for trajectoryplanning and navigation with in-built social constraints for keeping robot motions safe, human-aware and predictable. The robot trajectory is dynamically and continuously adjusted to satisfy these social constraints. To do so, we treat the robot trajectory as an elastic band (a mathematical construct representing the robot path as a series of poses and time-difference between those poses) which can be deformed (both in space and time) by the optimization process to respect given constraints. Moreover, we also predict plausible human trajectories in the same operating area by treating human paths also as elastic bands. This scheme allows us to optimize the robot trajectories not only for the current moment but for the entire interaction that happens when humans and robot cross each other's paths. We carried out a set of experiments with canonical human-robot interactive situations that happen in our everyday lives such as crossing a hallway, passing through a door and intersecting paths on wide open spaces. The proposed cooperative planning method compares favorably against other stat-of-the-art human-aware navigation planning schemes. We have augmented robot navigation behavior with synchronized and responsive movements of the robot head, making the robot look where it is going and occasionally diverting its gaze towards nearby people to acknowledge that robot will avoid any possible collision with them. At any given moment the robot weighs multiple criteria according to the social context and decides where it should turn its gaze. Through an online user study we have shown that such gazing mechanism effectively complements the navigation behavior and it improves legibility of the robot actions. Finally, we have integrated our navigation scheme with a broader supervision system which can jointly generate normative robot behaviors such as approaching a person and adapting the robot speed according to a group of people who the robot guides in airports or museums

Cognitive Task Planning for Smart Industrial Robots

This research work presents a novel Cognitive Task Planning framework for Smart Industrial Robots. The framework makes an industrial mobile manipulator robot Cognitive by applying Semantic Web Technologies. It also introduces a novel Navigation Among Movable Obstacles algorithm for robots navigating and manipulating inside a firm. The objective of Industrie 4.0 is the creation of Smart Factories: modular firms provided with cyber-physical systems able to strong customize products under the condition of highly flexible mass-production. Such systems should real-time communicate and cooperate with each other and with humans via the Internet of Things. They should intelligently adapt to the changing surroundings and autonomously navigate inside a firm while moving obstacles that occlude free paths, even if seen for the first time. At the end, in order to accomplish all these tasks while being efficient, they should learn from their actions and from that of other agents. Most of existing industrial mobile robots navigate along pre-generated trajectories. They follow ectrified wires embedded in the ground or lines painted on th efloor. When there is no expectation of environment changes and cycle times are critical, this planning is functional. When workspaces and tasks change frequently, it is better to plan dynamically: robots should autonomously navigate without relying on modifications of their environments. Consider the human behavior: humans reason about the environment and consider the possibility of moving obstacles if a certain goal cannot be reached or if moving objects may significantly shorten the path to it. This problem is named Navigation Among Movable Obstacles and is mostly known in rescue robotics. This work transposes the problem on an industrial scenario and tries to deal with its two challenges: the high dimensionality of the state space and the treatment of uncertainty. The proposed NAMO algorithm aims to focus exploration on less explored areas. For this reason it extends the Kinodynamic Motion Planning by Interior-Exterior Cell Exploration algorithm. The extension does not impose obstacles avoidance: it assigns an importance to each cell by combining the efforts necessary to reach it and that needed to free it from obstacles. The obtained algorithm is scalable because of its independence from the size of the map and from the number, shape, and pose of obstacles. It does not impose restrictions on actions to be performed: the robot can both push and grasp every object. Currently, the algorithm assumes full world knowledge but the environment is reconfigurable and the algorithm can be easily extended in order to solve NAMO problems in unknown environments. The algorithm handles sensor feedbacks and corrects uncertainties. Usually Robotics separates Motion Planning and Manipulation problems. NAMO forces their combined processing by introducing the need of manipulating multiple objects, often unknown, while navigating. Adopting standard precomputed grasps is not sufficient to deal with the big amount of existing different objects. A Semantic Knowledge Framework is proposed in support of the proposed algorithm by giving robots the ability to learn to manipulate objects and disseminate the information gained during the fulfillment of tasks. The Framework is composed by an Ontology and an Engine. The Ontology extends the IEEE Standard Ontologies for Robotics and Automation and contains descriptions of learned manipulation tasks and detected objects. It is accessible from any robot connected to the Cloud. It can be considered a data store for the efficient and reliable execution of repetitive tasks; and a Web-based repository for the exchange of information between robots and for the speed up of the learning phase. No other manipulation ontology exists respecting the IEEE Standard and, regardless the standard, the proposed ontology differs from the existing ones because of the type of features saved and the efficient way in which they can be accessed: through a super fast Cascade Hashing algorithm. The Engine lets compute and store the manipulation actions when not present in the Ontology. It is based on Reinforcement Learning techniques that avoid massive trainings on large-scale databases and favors human-robot interactions. The overall system is flexible and easily adaptable to different robots operating in different industrial environments. It is characterized by a modular structure where each software block is completely reusable. Every block is based on the open-source Robot Operating System. Not all industrial robot controllers are designed to be ROS-compliant. This thesis presents the method adopted during this research in order to Open Industrial Robot Controllers and create a ROS-Industrial interface for them