Target Assignment in Robotic Networks: Distance Optimality Guarantees and Hierarchical Strategies

We study the problem of multi-robot target assignment to minimize the total distance traveled by the robots until they all reach an equal number of static targets. In the first half of the paper, we present a necessary and sufficient condition under which true distance optimality can be achieved for robots with limited communication and target-sensing ranges. Moreover, we provide an explicit, non-asymptotic formula for computing the number of robots needed to achieve distance optimality in terms of the robots' communication and target-sensing ranges with arbitrary guaranteed probabilities. The same bounds are also shown to be asymptotically tight. In the second half of the paper, we present suboptimal strategies for use when the number of robots cannot be chosen freely. Assuming first that all targets are known to all robots, we employ a hierarchical communication model in which robots communicate only with other robots in the same partitioned region. This hierarchical communication model leads to constant approximations of true distance-optimal solutions under mild assumptions. We then revisit the limited communication and sensing models. By combining simple rendezvous-based strategies with a hierarchical communication model, we obtain decentralized hierarchical strategies that achieve constant approximation ratios with respect to true distance optimality. Results of simulation show that the approximation ratio is as low as 1.4

Surgical Subtask Automation for Intraluminal Procedures using Deep Reinforcement Learning

Intraluminal procedures have opened up a new sub-field of minimally invasive surgery that use flexible instruments to navigate through complex luminal structures of the body, resulting in reduced invasiveness and improved patient benefits. One of the major challenges in this field is the accurate and precise control of the instrument inside the human body. Robotics has emerged as a promising solution to this problem. However, to achieve successful robotic intraluminal interventions, the control of the instrument needs to be automated to a large extent. The thesis first examines the state-of-the-art in intraluminal surgical robotics and identifies the key challenges in this field, which include the need for safe and effective tool manipulation, and the ability to adapt to unexpected changes in the luminal environment. To address these challenges, the thesis proposes several levels of autonomy that enable the robotic system to perform individual subtasks autonomously, while still allowing the surgeon to retain overall control of the procedure. The approach facilitates the development of specialized algorithms such as Deep Reinforcement Learning (DRL) for subtasks like navigation and tissue manipulation to produce robust surgical gestures. Additionally, the thesis proposes a safety framework that provides formal guarantees to prevent risky actions. The presented approaches are evaluated through a series of experiments using simulation and robotic platforms. The experiments demonstrate that subtask automation can improve the accuracy and efficiency of tool positioning and tissue manipulation, while also reducing the cognitive load on the surgeon. The results of this research have the potential to improve the reliability and safety of intraluminal surgical interventions, ultimately leading to better outcomes for patients and surgeons

Learning from Interventions using Hierarchical Policies for Safe Learning

Learning from Demonstrations (LfD) via Behavior Cloning (BC) works well on multiple complex tasks. However, a limitation of the typical LfD approach is that it requires expert demonstrations for all scenarios, including those in which the algorithm is already well-trained. The recently proposed Learning from Interventions (LfI) overcomes this limitation by using an expert overseer. The expert overseer only intervenes when it suspects that an unsafe action is about to be taken. Although LfI significantly improves over LfD, the state-of-the-art LfI fails to account for delay caused by the expert's reaction time and only learns short-term behavior. We address these limitations by 1) interpolating the expert's interventions back in time, and 2) by splitting the policy into two hierarchical levels, one that generates sub-goals for the future and another that generates actions to reach those desired sub-goals. This sub-goal prediction forces the algorithm to learn long-term behavior while also being robust to the expert's reaction time. Our experiments show that LfI using sub-goals in a hierarchical policy framework trains faster and achieves better asymptotic performance than typical LfD.Comment: Accepted for publication at the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20

Scene understanding for autonomous robots operating in indoor environments

Mención Internacional en el título de doctorThe idea of having robots among us is not new. Great efforts are continually made to replicate human intelligence, with the vision of having robots performing different activities, including hazardous, repetitive, and tedious tasks. Research has demonstrated that robots are good at many tasks that are hard for us, mainly in terms of precision, efficiency, and speed. However, there are some tasks that humans do without much effort that are challenging for robots. Especially robots in domestic environments are far from satisfactorily fulfilling some tasks, mainly because these environments are unstructured, cluttered, and with a variety of environmental conditions to control. This thesis addresses the problem of scene understanding in the context of autonomous robots operating in everyday human environments. Furthermore, this thesis is developed under the HEROITEA research project that aims to develop a robot system to help elderly people in domestic environments as an assistant. Our main objective is to develop different methods that allow robots to acquire more information from the environment to progressively build knowledge that allows them to improve the performance on high-level robotic tasks. In this way, scene understanding is a broad research topic, and it is considered a complex task due to the multiple sub-tasks that are involved. In that context, in this thesis, we focus on three sub-tasks: object detection, scene recognition, and semantic segmentation of the environment. Firstly, we implement methods to recognize objects considering real indoor environments. We applied machine learning techniques incorporating uncertainties and more modern techniques based on deep learning. Besides, apart from detecting objects, it is essential to comprehend the scene where they can occur. For this reason, we propose an approach for scene recognition that considers the influence of the detected objects in the prediction process. We demonstrate that the exiting objects and their relationships can improve the inference about the scene class. We also consider that a scene recognition model can benefit from the advantages of other models. We propose a multi-classifier model for scene recognition based on weighted voting schemes. The experiments carried out in real-world indoor environments demonstrate that the adequate combination of independent classifiers allows obtaining a more robust and precise model for scene recognition. Moreover, to increase the understanding of a robot about its surroundings, we propose a new division of the environment based on regions to build a useful representation of the environment. Object and scene information is integrated into a probabilistic fashion generating a semantic map of the environment containing meaningful regions within each room. The proposed system has been assessed on simulated and real-world domestic scenarios, demonstrating its ability to generate consistent environment representations. Lastly, full knowledge of the environment can enhance more complex robotic tasks; that is why in this thesis, we try to study how a complete knowledge of the environment influences the robot’s performance in high-level tasks. To do so, we select an essential task, which is searching for objects. This mundane task can be considered a precondition to perform many complex robotic tasks such as fetching and carrying, manipulation, user requirements, among others. The execution of these activities by service robots needs full knowledge of the environment to perform each task efficiently. In this thesis, we propose two searching strategies that consider prior information, semantic representation of the environment, and the relationships between known objects and the type of scene. All our developments are evaluated in simulated and real-world environments, integrated with other systems, and operating in real platforms, demonstrating their feasibility to implement in real scenarios, and in some cases outperforming other approaches. We also demonstrate how our representation of the environment can boost the performance of more complex robotic tasks compared to more standard environmental representations.La idea de tener robots entre nosotros no es nueva. Continuamente se realizan grandes esfuerzos para replicar la inteligencia humana, con la visión de tener robots que realicen diferentes actividades, incluidas tareas peligrosas, repetitivas y tediosas. La investigación ha demostrado que los robots son buenos en muchas tareas que resultan difíciles para nosotros, principalmente en términos de precisión, eficiencia y velocidad. Sin embargo, existen tareas que los humanos realizamos sin mucho esfuerzo y que son un desafío para los robots. Especialmente, los robots en entornos domésticos están lejos de cumplir satisfactoriamente algunas tareas, principalmente porque estos entornos no son estructurados, pueden estar desordenados y cuentan con una gran variedad de condiciones ambientales que controlar. Esta tesis aborda el problema de la comprensión de la escena en el contexto de robots autónomos que operan en entornos humanos cotidianos. Asimismo, esta tesis se desarrolla en el marco del proyecto de investigación HEROITEA que tiene como objetivo desarrollar un sistema robótico que funcione como asistente para ayudar a personas mayores en entornos domésticos. Nuestro principal objetivo es desarrollar diferentes métodos que permitan a los robots adquirir más información del entorno a fin de construir progresivamente un conocimiento que les permita mejorar su desempeño en tareas robóticas más complejas. En este sentido, la comprensión de escenas es un tema de investigación amplio, y se considera una tarea compleja debido a las múltiples subtareas involucradas. En esta tesis nos enfocamos específicamente en tres subtareas: detección de objetos, reconocimiento de escenas y etiquetado semántico del entorno. Por un lado, implementamos métodos para el reconocimiento de objectos considerando entornos interiores reales. Aplicamos técnicas de aprendizaje automático incorporando incertidumbres y técnicas más modernas basadas en aprendizaje profundo. Además, aparte de detectar objetos, es fundamental comprender la escena donde estos se encuentran. Por esta razón, proponemos un modelo para el reconocimiento de escenas que considera la influencia de los objetos detectados en el proceso de predicción. Demostramos que los objetos existentes y sus relaciones pueden mejorar el proceso de inferencia de la categoría de la escena. También consideramos que un modelo de reconocimiento de escenas puede beneficiarse de las ventajas de otros modelos. Por ello, proponemos un multiclasificador para el reconocimiento de escenas basado en esquemas de votación ponderados. Los experimentos llevados a cabo en entornos interiores reales demuestran que la combinación adecuada de clasificadores independientes permite obtener un modelo más robusto y preciso para el reconocimiento de escenas. Adicionalmente, para aumentar la comprensión de un robot acerca de su entorno, proponemos una nueva división del entorno basada en regiones a fin de construir una representación útil del entorno. La información de objetos y de la escena se integra de forma probabilística generando un mapa semántico que contiene regiones significativas dentro de cada habitación. El sistema propuesto ha sido evaluado en entornos domésticos simulados y reales, demostrando su capacidad para generar representaciones consistentes del entorno. Por otro lado, el conocimiento integral del entorno puede mejorar tareas robóticas más complejas; es por ello que en esta tesis analizamos cómo el conocimiento completo del entorno influye en el desempeño del robot en tareas de alto nivel. Para ello, seleccionamos una tarea fundamental, que es la búsqueda de objetos. Esta tarea mundana puede considerarse una condición previa para realizar diversas tareas robóticas complejas, como transportar objetos, tareas de manipulación, atender requerimientos del usuario, entre otras. La ejecución de estas actividades por parte de robots de servicio requiere un conocimiento profundo del entorno para realizar cada tarea de manera eficiente. En esta tesis proponemos dos estrategias de búsqueda de objetos que consideran información previa, la representación semántica del entorno, las relaciones entre los objetos conocidos y el tipo de escena. Todos nuestros desarrollos son evaluados en entornos simulados y reales, integrados con otros sistemas y operando en plataformas reales, demostrando su viabilidad de ser implementados en escenarios reales y, en algunos casos, superando a otros enfoques. También demostramos cómo nuestra representación del entorno puede mejorar el desempeño de tareas robóticas más complejas en comparación con representaciones del entorno más tradicionales.Programa de Doctorado en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidente: Carlos Balaguer Bernaldo de Quirós.- Secretario: Fernando Matía Espada.- Vocal: Klaus Strob

Exploration autonome et efficiente de chantiers miniers souterrains inconnus avec un drone filaire

Abstract: Underground mining stopes are often mapped using a sensor located at the end of a pole that the operator introduces into the stope from a secure area. The sensor emits laser beams that provide the distance to a detected wall, thus creating a 3D map. This produces shadow zones and a low point density on the distant walls. To address these challenges, a research team from the Université de Sherbrooke is designing a tethered drone equipped with a rotating LiDAR for this mission, thus benefiting from several points of view. The wired transmission allows for unlimited flight time, shared computing, and real-time communication. For compatibility with the movement of the drone after tether entanglements, the excess length is integrated into an onboard spool, contributing to the drone payload. During manual piloting, the human factor causes problems in the perception and comprehension of a virtual 3D environment, as well as the execution of an optimal mission. This thesis focuses on autonomous navigation in two aspects: path planning and exploration. The system must compute a trajectory that maps the entire environment, minimizing the mission time and respecting the maximum onboard tether length. Path planning using a Rapidly-exploring Random Tree (RRT) quickly finds a feasible path, but the optimization is computationally expensive and the performance is variable and unpredictable. Exploration by the frontier method is representative of the space to be explored and the path can be optimized by solving a Traveling Salesman Problem (TSP) but existing techniques for a tethered drone only consider the 2D case and do not optimize the global path. To meet these challenges, this thesis presents two new algorithms. The first one, RRT-Rope, produces an equal or shorter path than existing algorithms in a significantly shorter computation time, up to 70% faster than the next best algorithm in a representative environment. A modified version of RRT-connect computes a feasible path, shortened with a deterministic technique that takes advantage of previously added intermediate nodes. The second algorithm, TAPE, is the first 3D cavity exploration method that focuses on minimizing mission time and unwound tether length. On average, the overall path is 4% longer than the method that solves the TSP, but the tether remains under the allowed length in 100% of the simulated cases, compared to 53% with the initial method. The approach uses a 2-level hierarchical architecture: global planning solves a TSP after frontier extraction, and local planning minimizes the path cost and tether length via a decision function. The integration of these two tools in the NetherDrone produces an intelligent system for autonomous exploration, with semi-autonomous features for operator interaction. This work opens the door to new navigation approaches in the field of inspection, mapping, and Search and Rescue missions.La cartographie des chantiers miniers souterrains est souvent réalisée à l’aide d’un capteur situé au bout d’une perche que l’opérateur introduit dans le chantier, depuis une zone sécurisée. Le capteur émet des faisceaux laser qui fournissent la distance à un mur détecté, créant ainsi une carte en 3D. Ceci produit des zones d’ombres et une faible densité de points sur les parois éloignées. Pour relever ces défis, une équipe de recherche de l’Université de Sherbrooke conçoit un drone filaire équipé d’un LiDAR rotatif pour cette mission, bénéficiant ainsi de plusieurs points de vue. La transmission filaire permet un temps de vol illimité, un partage de calcul et une communication en temps réel. Pour une compatibilité avec le mouvement du drone lors des coincements du fil, la longueur excédante est intégrée dans une bobine embarquée, qui contribue à la charge utile du drone. Lors d’un pilotage manuel, le facteur humain entraîne des problèmes de perception et compréhension d’un environnement 3D virtuel, et d’exécution d’une mission optimale. Cette thèse se concentre sur la navigation autonome sous deux aspects : la planification de trajectoire et l’exploration. Le système doit calculer une trajectoire qui cartographie l’environnement complet, en minimisant le temps de mission et en respectant la longueur maximale de fil embarquée. La planification de trajectoire à l’aide d’un Rapidly-exploring Random Tree (RRT) trouve rapidement un chemin réalisable, mais l’optimisation est coûteuse en calcul et la performance est variable et imprévisible. L’exploration par la méthode des frontières est représentative de l’espace à explorer et le chemin peut être optimisé en résolvant un Traveling Salesman Problem (TSP), mais les techniques existantes pour un drone filaire ne considèrent que le cas 2D et n’optimisent pas le chemin global. Pour relever ces défis, cette thèse présente deux nouveaux algorithmes. Le premier, RRT-Rope, produit un chemin égal ou plus court que les algorithmes existants en un temps de calcul jusqu’à 70% plus court que le deuxième meilleur algorithme dans un environnement représentatif. Une version modifiée de RRT-connect calcule un chemin réalisable, raccourci avec une technique déterministe qui tire profit des noeuds intermédiaires préalablement ajoutés. Le deuxième algorithme, TAPE, est la première méthode d’exploration de cavités en 3D qui minimise le temps de mission et la longueur du fil déroulé. En moyenne, le trajet global est 4% plus long que la méthode qui résout le TSP, mais le fil reste sous la longueur autorisée dans 100% des cas simulés, contre 53% avec la méthode initiale. L’approche utilise une architecture hiérarchique à 2 niveaux : la planification globale résout un TSP après extraction des frontières, et la planification locale minimise le coût du chemin et la longueur de fil via une fonction de décision. L’intégration de ces deux outils dans le NetherDrone produit un système intelligent pour l’exploration autonome, doté de fonctionnalités semi-autonomes pour une interaction avec l’opérateur. Les travaux réalisés ouvrent la porte à de nouvelles approches de navigation dans le domaine des missions d’inspection, de cartographie et de recherche et sauvetage

Censored deep reinforcement patrolling with information criterion for monitoring large water resources using Autonomous Surface Vehicles

© 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)Monitoring and patrolling large water resources is a major challenge for nature conservation. The problem of acquiring data of an underlying environment that usually changes within time involves a proper formulation of the information. The use of Autonomous Surface Vehicles equipped with water quality sensor modules can serve as an early-warning system for contamination peak-detection, algae blooms monitoring, or oil-spill scenarios. In addition to information gathering, the vehicle must plan routes that are free of obstacles on non-convex static and dynamics maps. This work proposes a novel framework to obtain a collision-free policy using deterministic knowledge of the environment by means of a censoring operator and noisy networks that addresses the informative path planning with emphasis in temporal patrolling. Using information gain as a measure of the uncertainty reduction over data, it is proposed a Deep Q-Learning algorithm improved by a Q-Censoring mechanism for model-based obstacle avoidance. The obtained results demonstrate the effectiveness of the proposed algorithm for both cases in the Ypacaraí monitorization task. Simulations showed that the use of noisy-networks are a good choice for enhanced exploration, with 3 times less redundancy in the paths with respect to — greedy policy. Previous coverage strategies are also outperformed both in the accuracy of the obtained contamination model by a 13% on average and by a 37% in the detection of dangerous contamination peaks. Finally, the achieved results indicate the appropriateness of the proposed framework for monitoring scenarios with autonomous vehicles