The multi-agent flood algorithm as an autonomous system for search and rescue applications

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Multi-Agent Path Planning for Locating a Radiating Source in an Unknown Environment

A situation is addressed in which multiple autonomous agents are used to search an unknown environment for a target, the position and orientation of which is known with respect to each agent. A controlling framework is proposed to inform and coordinate the agents’ movements in order to reduce the time required to locate the target. Four primary variables are considered: the cost function used to select the agents’ paths, the number of agents in a given scenario, the distance over which the agents are assumed to communicate, and the size of the environment in which the agents are operating. It was found that a cost function that balances progress toward the target with exploration of the environment is generally most effective for all combinations of the other variables. More agents and greater communication are beneficial, to a point, in larger environments, although these may be less effective in smaller ones

Adoption of vehicular ad hoc networking protocols by networked robots

This paper focuses on the utilization of wireless networking in the robotics domain. Many researchers have already equipped their robots with wireless communication capabilities, stimulated by the observation that multi-robot systems tend to have several advantages over their single-robot counterparts. Typically, this integration of wireless communication is tackled in a quite pragmatic manner, only a few authors presented novel Robotic Ad Hoc Network (RANET) protocols that were designed specifically with robotic use cases in mind. This is in sharp contrast with the domain of vehicular ad hoc networks (VANET). This observation is the starting point of this paper. If the results of previous efforts focusing on VANET protocols could be reused in the RANET domain, this could lead to rapid progress in the field of networked robots. To investigate this possibility, this paper provides a thorough overview of the related work in the domain of robotic and vehicular ad hoc networks. Based on this information, an exhaustive list of requirements is defined for both types. It is concluded that the most significant difference lies in the fact that VANET protocols are oriented towards low throughput messaging, while RANET protocols have to support high throughput media streaming as well. Although not always with equal importance, all other defined requirements are valid for both protocols. This leads to the conclusion that cross-fertilization between them is an appealing approach for future RANET research. To support such developments, this paper concludes with the definition of an appropriate working plan

Control of heterogeneous robot networks for assistance in search and rescue tasks

This project develops a decentralized control strategy for multiple heterogeneous robots oriented to the assistance in search and rescue situations from two complementary perspectives, the discrete tasks allocation and the real-time control. For the discrete task allocation through the mission, we present an optimized algorithm based on events, oriented to the minimization of the time required to attend all the victims in the mission environment. This algorithm allows assign to each robot an appropriate task considering that the robots may vary in their capacity for completing each task and also may vary in their moving capabilities. The considered tasks are the mission environment exploration, the victims’ search and identification, the medical supplies delivery to victims unable to move and the evacuation of victims capable to move. It is worth to mention that, through the development of each task and the estimation of its durations, the robots consider optimized routes considering a distance metric based in the breath first search algorithm called flooding distance with 8 neighbors (8NF) which considers only orthogonal and 45 degrees diagonal movements allowing an estimation of the geodesic distance to each point in the map. Regarding to the real-time control laws, they oversee the proper execution of the tasks assigned by the reallocation algorithm respecting the restrictions in the connectivity range, the obstacles avoidance and the fulfillment of each task. The exploration task is made employing an adaptation of the algorithm DisCoverage presented by [16] which employing a Voronoi cells based tessellation considering the arrival time to each point as reference, allows the determination of the map of non-convex spaces as those that may be found in search and rescue situations. The evasion of obstacles and the preservation of the robots’ links is achieved employing an approach of artificial potentials based in the work of [37]. The interest points related to each task tracking is made employing proportional control loops for each agent identifying the route points within the line of sight and considering optimized routes given by the 8NF flooding distance metric. Additionally, there is presented a heuristic reconfiguration algorithm that allows to change the network topology preserving its connectivity for each instant of time. This complete framework allows a team of autonomous robots to bring valuable assistance in certain search and rescue situations where the human teams may be insufficient, and/or the mission conditions may be harmful for the people considering that even if the robots cannot realize paramedical tasks yet, they can complete multiple useful tasks for reducing the effort and risks of the human teams in that kind of situations. The functioning of those algorithms is presented in non-trivial simulations intended to show the behaviors that emerge in the robots.Resumen: Este proyecto desarrolló una estrategia de control descentralizado para múltiples robots heterogéneos orientada a la asistencia en situaciones de búsqueda y rescate desde dos perspectivas complementarias, la asignación discreta de tareas y el control en tiempo real. Para la asignación discreta de las tareas a los robots a lo largo de la misión, presentamos un algoritmo optimizado de reasignación de tareas basado en eventos, orientado a la minimización del tiempo requerido para atender a todas las víctimas en el ambiente de misión. Este algoritmo permite asignar a cada robot una tarea apropiada considerando que los robots pueden diferir en su capacidad para completar cada tarea, así como también en sus capacidades de movimiento. Las tareas consideradas son la exploración del ambiente de misión, la búsqueda e identificación de víctimas, la entrega de suministros médicos a las víctimas incapaces de moverse y la evacuación de las víctimas capaces de moverse. Cabe destacar que, durante el desarrollo de cada tarea y la estimación de los tiempos de las mismas, los robots consideran rutas optimizadas considerando una métrica de distancia basada en el algoritmo de búsqueda en anchura (Breath first Search) llamada distancia por inundaci´on con 8 vecinos (8NF) la cual considera movimientos netamente ortogonales y diagonales a 45 grados permitiendo una estimación de la distancia geodésica a cada punto en el mapa. Con respecto a las leyes de control en tiempo real, estas están a cargo de la correcta ejecución de las tareas asignadas por el algoritmo de reasignación de tareas respetando las restricciones en el rango de conectividad, la evasión de colisiones y la completa ejecución de cada tarea. La exploración es desarrollada empleando una adaptación del algoritmo DisCoverage presentado por [16] el cuál empleando una teselación basada en celdas de Voronoi con el tiempo de llegada a cada punto como referencia, permite la determinaci´on del mapa de espacios no convexos como los que se pueden encontrar en algunas situaciones de búsqueda y rescate. La evasión de obstáculos y la preservación de los enlaces se realiza a través de un enfoque de potenciales artificiales basándose en el trabajo de [37]. El seguimiento de los puntos de interés relacionados a cada tarea se realiza empleando lazos de control proporcional para cada agente identificando los puntos de ruta dentro de la línea de visión y considerando rutas optimizadas tomando la estimaciói brindada por la métrica de distancia por inundación 8NF. Adicionalmente se presentó un algoritmo de reconfiguración de la red heurístico que permite cambiar la topología de la red manteniendo la conectividad de la misma para cada instante de tiempo. Este marco de trabajo completo permite a un equipo de robots autónomos brindar asistencia valiosa en ciertas situaciones de búsqueda y rescate d´onde los equipos humanos sean insuficientes y/o las condiciones de la misión pueden ser peligrosas para las personas teniendo en cuenta que si bien los robots actualmente no son capaces de realizar tareas paramédicas si son capaces de realizar múltiples tareas útiles para aligerar el trabajo y el riesgo para equipos humanos en estas situaciones. El funcionamiento de estos algoritmos es presentado en simulaciones no triviales en Matlab R buscando presentar los comportamientos que emergen en los robots y adicionalmente fue implementado en una versión simplificada con robots móviles tipo turtlebot y configuraciones simples de robots BioloidMaestrí

NeBula: TEAM CoSTAR’s robotic autonomy solution that won phase II of DARPA subterranean challenge

This paper presents and discusses algorithms, hardware, and software architecture developed by the TEAM CoSTAR (Collaborative SubTerranean Autonomous Robots), competing in the DARPA Subterranean Challenge. Specifically, it presents the techniques utilized within the Tunnel (2019) and Urban (2020) competitions, where CoSTAR achieved second and first place, respectively. We also discuss CoSTAR’s demonstrations in Martian-analog surface and subsurface (lava tubes) exploration. The paper introduces our autonomy solution, referred to as NeBula (Networked Belief-aware Perceptual Autonomy). NeBula is an uncertainty-aware framework that aims at enabling resilient and modular autonomy solutions by performing reasoning and decision making in the belief space (space of probability distributions over the robot and world states). We discuss various components of the NeBula framework, including (i) geometric and semantic environment mapping, (ii) a multi-modal positioning system, (iii) traversability analysis and local planning, (iv) global motion planning and exploration behavior, (v) risk-aware mission planning, (vi) networking and decentralized reasoning, and (vii) learning-enabled adaptation. We discuss the performance of NeBula on several robot types (e.g., wheeled, legged, flying), in various environments. We discuss the specific results and lessons learned from fielding this solution in the challenging courses of the DARPA Subterranean Challenge competition.Peer ReviewedAgha, A., Otsu, K., Morrell, B., Fan, D. D., Thakker, R., Santamaria-Navarro, A., Kim, S.-K., Bouman, A., Lei, X., Edlund, J., Ginting, M. F., Ebadi, K., Anderson, M., Pailevanian, T., Terry, E., Wolf, M., Tagliabue, A., Vaquero, T. S., Palieri, M., Tepsuporn, S., Chang, Y., Kalantari, A., Chavez, F., Lopez, B., Funabiki, N., Miles, G., Touma, T., Buscicchio, A., Tordesillas, J., Alatur, N., Nash, J., Walsh, W., Jung, S., Lee, H., Kanellakis, C., Mayo, J., Harper, S., Kaufmann, M., Dixit, A., Correa, G. J., Lee, C., Gao, J., Merewether, G., Maldonado-Contreras, J., Salhotra, G., Da Silva, M. S., Ramtoula, B., Fakoorian, S., Hatteland, A., Kim, T., Bartlett, T., Stephens, A., Kim, L., Bergh, C., Heiden, E., Lew, T., Cauligi, A., Heywood, T., Kramer, A., Leopold, H. A., Melikyan, H., Choi, H. C., Daftry, S., Toupet, O., Wee, I., Thakur, A., Feras, M., Beltrame, G., Nikolakopoulos, G., Shim, D., Carlone, L., & Burdick, JPostprint (published version

NeBula: Team CoSTAR's robotic autonomy solution that won phase II of DARPA Subterranean Challenge

This paper presents and discusses algorithms, hardware, and software architecture developed by the TEAM CoSTAR (Collaborative SubTerranean Autonomous Robots), competing in the DARPA Subterranean Challenge. Specifically, it presents the techniques utilized within the Tunnel (2019) and Urban (2020) competitions, where CoSTAR achieved second and first place, respectively. We also discuss CoSTAR¿s demonstrations in Martian-analog surface and subsurface (lava tubes) exploration. The paper introduces our autonomy solution, referred to as NeBula (Networked Belief-aware Perceptual Autonomy). NeBula is an uncertainty-aware framework that aims at enabling resilient and modular autonomy solutions by performing reasoning and decision making in the belief space (space of probability distributions over the robot and world states). We discuss various components of the NeBula framework, including (i) geometric and semantic environment mapping, (ii) a multi-modal positioning system, (iii) traversability analysis and local planning, (iv) global motion planning and exploration behavior, (v) risk-aware mission planning, (vi) networking and decentralized reasoning, and (vii) learning-enabled adaptation. We discuss the performance of NeBula on several robot types (e.g., wheeled, legged, flying), in various environments. We discuss the specific results and lessons learned from fielding this solution in the challenging courses of the DARPA Subterranean Challenge competition.The work is partially supported by the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (80NM0018D0004), and Defense Advanced Research Projects Agency (DARPA)

Robot Navigation in Human Environments

For the near future, we envision service robots that will help us with everyday chores in home, office, and urban environments. These robots need to work in environments that were designed for humans and they have to collaborate with humans to fulfill their tasks. In this thesis, we propose new methods for communicating, transferring knowledge, and collaborating between humans and robots in four different navigation tasks. In the first application, we investigate how automated services for giving wayfinding directions can be improved to better address the needs of the human recipients. We propose a novel method based on inverse reinforcement learning that learns from a corpus of human-written route descriptions what amount and type of information a route description should contain. By imitating the human teachers' description style, our algorithm produces new route descriptions that sound similarly natural and convey similar information content, as we show in a user study. In the second application, we investigate how robots can leverage background information provided by humans for exploring an unknown environment more efficiently. We propose an algorithm for exploiting user-provided information such as sketches or floor plans by combining a global exploration strategy based on the solution of a traveling salesman problem with a local nearest-frontier-first exploration scheme. Our experiments show that the exploration tours are significantly shorter and that our system allows the user to effectively select the areas that the robot should explore. In the second part of this thesis, we focus on humanoid robots in home and office environments. The human-like body plan allows humanoid robots to navigate in environments and operate tools that were designed for humans, making humanoid robots suitable for a wide range of applications. As localization and mapping are prerequisites for all navigation tasks, we first introduce a novel feature descriptor for RGB-D sensor data and integrate this building block into an appearance-based simultaneous localization and mapping system that we adapt and optimize for the usage on humanoid robots. Our optimized system is able to track a real Nao humanoid robot more accurately and more robustly than existing approaches. As the third application, we investigate how humanoid robots can cover known environments efficiently with their camera, for example for inspection or search tasks. We extend an existing next-best-view approach by integrating inverse reachability maps, allowing us to efficiently sample and check collision-free full-body poses. Our approach enables the robot to inspect as much of the environment as possible. In our fourth application, we extend the coverage scenario to environments that also include articulated objects that the robot has to actively manipulate to uncover obstructed regions. We introduce algorithms for navigation subtasks that run highly parallelized on graphics processing units for embedded devices. Together with a novel heuristic for estimating utility maps, our system allows to find high-utility camera poses for efficiently covering environments with articulated objects. All techniques presented in this thesis were implemented in software and thoroughly evaluated in user studies, simulations, and experiments in both artificial and real-world environments. Our approaches advance the state of the art towards universally usable robots in everyday environments.Roboternavigation in menschlichen Umgebungen In naher Zukunft erwarten wir Serviceroboter, die uns im Haushalt, im Büro und in der Stadt alltägliche Arbeiten abnehmen. Diese Roboter müssen in für Menschen gebauten Umgebungen zurechtkommen und sie müssen mit Menschen zusammenarbeiten um ihre Aufgaben zu erledigen. In dieser Arbeit schlagen wir neue Methoden für die Kommunikation, Wissenstransfer und Zusammenarbeit zwischen Menschen und Robotern bei Navigationsaufgaben in vier Anwendungen vor. In der ersten Anwendung untersuchen wir, wie automatisierte Dienste zur Generierung von Wegbeschreibungen verbessert werden können, um die Beschreibungen besser an die Bedürfnisse der Empfänger anzupassen. Wir schlagen eine neue Methode vor, die inverses bestärkendes Lernen nutzt, um aus einem Korpus von von Menschen geschriebenen Wegbeschreibungen zu lernen, wie viel und welche Art von Information eine Wegbeschreibung enthalten sollte. Indem unser Algorithmus den Stil der Wegbeschreibungen der menschlichen Lehrer imitiert, kann der Algorithmus neue Wegbeschreibungen erzeugen, die sich ähnlich natürlich anhören und einen ähnlichen Informationsgehalt vermitteln, was wir in einer Benutzerstudie zeigen. In der zweiten Anwendung untersuchen wir, wie Roboter von Menschen bereitgestellte Hintergrundinformationen nutzen können, um eine bisher unbekannte Umgebung schneller zu erkunden. Wir schlagen einen Algorithmus vor, der Hintergrundinformationen wie Gebäudegrundrisse oder Skizzen nutzt, indem er eine globale Explorationsstrategie basierend auf der Lösung eines Problems des Handlungsreisenden kombiniert mit einer lokalen Explorationsstrategie. Unsere Experimente zeigen, dass die Erkundungstouren signifikant kürzer werden und dass der Benutzer mit unserem System effektiv die zu erkundenden Regionen spezifizieren kann. Der zweite Teil dieser Arbeit konzentriert sich auf humanoide Roboter in Umgebungen zu Hause und im Büro. Der menschenähnliche Körperbau ermöglicht es humanoiden Robotern, in Umgebungen zu navigieren und Werkzeuge zu benutzen, die für Menschen gebaut wurden, wodurch humanoide Roboter für vielfältige Aufgaben einsetzbar sind. Da Lokalisierung und Kartierung Grundvoraussetzungen für alle Navigationsaufgaben sind, führen wir zunächst einen neuen Merkmalsdeskriptor für RGB-D-Sensordaten ein und integrieren diesen Baustein in ein erscheinungsbasiertes simultanes Lokalisierungs- und Kartierungsverfahren, das wir an die Besonderheiten von humanoiden Robotern anpassen und optimieren. Unser System kann die Position eines realen humanoiden Roboters genauer und robuster verfolgen, als es mit existierenden Ansätzen möglich ist. Als dritte Anwendung untersuchen wir, wie humanoide Roboter bekannte Umgebungen effizient mit ihrer Kamera abdecken können, beispielsweise zu Inspektionszwecken oder zum Suchen eines Gegenstands. Wir erweitern ein bestehendes Verfahren, das die nächstbeste Beobachtungsposition berechnet, durch inverse Erreichbarkeitskarten, wodurch wir kollisionsfreie Ganzkörperposen effizient generieren und prüfen können. Unser Ansatz ermöglicht es dem Roboter, so viel wie möglich von der Umgebung zu untersuchen. In unserer vierten Anwendung erweitern wir dieses Szenario um Umgebungen, die auch bewegbare Gegenstände enthalten, die der Roboter aktiv bewegen muss um verdeckte Regionen zu sehen. Wir führen Algorithmen für Teilprobleme ein, die hoch parallelisiert auf Grafikkarten von eingebetteten Systemen ausgeführt werden. Zusammen mit einer neuen Heuristik zur Schätzung von Nutzenkarten ermöglicht dies unserem System Beobachtungspunkte mit hohem Nutzen zu finden, um Umgebungen mit bewegbaren Objekten effizient zu inspizieren. Alle vorgestellten Techniken wurden in Software implementiert und sorgfältig evaluiert in Benutzerstudien, Simulationen und Experimenten in künstlichen und realen Umgebungen. Unsere Verfahren bringen den Stand der Forschung voran in Richtung universell einsetzbarer Roboter in alltäglichen Umgebungen

Exploration and Coverage with Swarms of Settling Agents

We consider several algorithms for exploring and filling an unknown, connected region, by simple, airborne agents. The agents are assumed to be identical, autonomous, anonymous and to have a finite amount of memory. The region is modeled as a connected sub-set of a regular grid composed of square cells. The algorithms described herein are suited for Micro Air Vehicles (MAV) since these air vehicles enable unobstructed views of the ground below and can move freely in space at various heights. The agents explore the region by applying various action-rules based on locally acquired information Some of them may settle in unoccupied cells as the exploration progresses. Settled agents become virtual pheromones for the exploration and coverage process, beacons that subsequently aid the remaining, and still exploring, mobile agents. We introduce a backward propagating information diffusion process as a way to implement a deterministic indicator of process termination and guide the mobile agents. For the proposed algorithms, complete covering of the graph in finite time is guaranteed when the size of the region is fixed. Bounds on the coverage times are also derived. Extensive simulation results exhibit good agreement with the theoretical predictions