Guaranteeing Motion Safety for Robots

International audienceSpecial Issue Guest Editoria

Navigation under Obstacle Motion Uncertainty using Markov Decision Processes

In terms of navigation, a central problem in the field of autonomous robotics is obstacle avoidance. This research explores how to navigate as well as avoid obstacles by leveraging what is known of the environment to determine decisions with new incoming information during execution. The algorithm presented in this work is divided into two procedures: an offline process that uses prior knowledge to navigate toward the goal; and an online execution strategy that leverages results obtained offline to drive safely towards the target when new information is encountered (e.g., obstacles). To take advantage of what is known offline, the navigation problem was formulated as a Markov Decision Process (MDP) where the environment is characterized as an occupancy grid. Baseline dynamic programming techniques were used to solve this, producing general behaviors that drive the robot (or agent) toward the goal and a value function which encodes the value of being in particular states. Then during online execution, the agent uses these offline results and surrounding local information of the environment to operate (e.g., data from a LIDAR sensor). This locally acquired information, which may contain new data not seen prior, is represented as a small occupancy grid and leverages the offline obtained value function to define local goals allowing the agent to make short term plans. When the agent encounters an obstacle locally, the problem becomes a Partially Observable Markov Decision Process (POMDP) since it is uncertain where these obstacles will be in the next state. This is solved by utilizing an approximate planner (QMDP) that uses uncertainty of the obstacle motion and considers all possible obstacle state combinations in the next time step to determine the best action. The approximate planner can quickly solve the POMDP, due to the small size of the local occupancy grid and by using the behaviors produced offline to help speed up convergence, which opens the possibility for this procedure to be executed in real time, on a physical robot. Two simulated environments were created, varying in complexity and dynamic obstacles. Simulation results under complex conditions with narrow operable spaces and many dynamic obstacles show the proposed algorithm has approximately an 85% success rate, in test cases with cluttered environments and multiple dynamic obstacles, and is shown to produce safer trajectories than the baseline approach, which had roughly a 37% success rate, under the assumptions that dynamic obstacles can only move a short distance by the next time step

Model-based robocentric planning and navigation for dynamic environments

This work addresses a new technique of motion planning and navigation for differential-drive robots in dynamic environments. Static and dynamic objects are represented directly on the control space of the robot, where decisions on the best motion are made. A new model representing the dynamism and the prediction of the future behavior of the environment is defined, the dynamic object velocity space (DOVS). A formal definition of this model is provided, establishing the properties for its characterization. An analysis of its complexity, compared with other methods, is performed. The model contains information about the future behavior of obstacles, mapped on the robot control space. It allows planning of near-time-optimal safe motions within the visibility space horizon, not only for the current sampling period. Navigation strategies are developed based on the identification of situations in the model. The planned strategy is applied and updated for each sampling time, adapting to changes occurring in the scenario. The technique is evaluated in randomly generated simulated scenarios, based on metrics defined using safety and time-to-goal criteria. An evaluation in real-world experiments is also presented

Policy-Based Planning for Robust Robot Navigation

This thesis proposes techniques for constructing and implementing an extensible navigation framework suitable for operating alongside or in place of traditional navigation systems. Robot navigation is only possible when many subsystems work in tandem such as localization and mapping, motion planning, control, and object tracking. Errors in any one of these subsystems can result in the robot failing to accomplish its task, oftentimes requiring human interventions that diminish the benefits theoretically provided by autonomous robotic systems. Our first contribution is Direction Approximation through Random Trials (DART), a method for generating human-followable navigation instructions optimized for followability instead of traditional metrics such as path length. We show how this strategy can be extended to robot navigation planning, allowing the robot to compute the sequence of control policies and switching conditions maximizing the likelihood with which the robot will reach its goal. This technique allows robots to select plans based on reliability in addition to efficiency, avoiding error-prone actions or areas of the environment. We also show how DART can be used to build compact, topological maps of its environments, offering opportunities to scale to larger environments. DART depends on the existence of a set of behaviors and switching conditions describing ways the robot can move through an environment. In the remainder of this thesis, we present methods for learning these behaviors and conditions in indoor environments. To support landmark-based navigation, we show how to train a Convolutional Neural Network (CNN) to distinguish between semantically labeled 2D occupancy grids generated from LIDAR data. By providing the robot the ability to recognize specific classes of places based on human labels, not only do we support transitioning between control laws, but also provide hooks for human-aided instruction and direction. Additionally, we suggest a subset of behaviors that provide DART with a sufficient set of actions to navigate in most indoor environments and introduce a method to learn these behaviors from teleloperated demonstrations. Our method learns a cost function suitable for integration into gradient-based control schemes. This enables the robot to execute behaviors in the absence of global knowledge. We present results demonstrating these behaviors working in several environments with varied structure, indicating that they generalize well to new environments. This work was motivated by the weaknesses and brittleness of many state-of-the-art navigation systems. Reliable navigation is the foundation of any mobile robotic system. It provides access to larger work spaces and enables a wide variety of tasks. Even though navigation systems have continued to improve, catastrophic failures can still occur (e.g. due to an incorrect loop closure) that limit their reliability. Furthermore, as work areas approach the scale of kilometers, constructing and operating on precise localization maps becomes expensive. These limitations prevent large scale deployments of robots outside of controlled settings and laboratory environments. The work presented in this thesis is intended to augment or replace traditional navigation systems to mitigate concerns about scalability and reliability by considering the effects of navigation failures for particular actions. By considering these effects when evaluating the actions to take, our framework can adapt navigation strategies to best take advantage of the capabilities of the robot in a given environment. A natural output of our framework is a topological network of actions and switching conditions, providing compact representations of work areas suitable for fast, scalable planning.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/144073/1/rgoeddel_1.pd

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 /

Safety of Autonomous Cognitive-oriented Robots

Service robots shall very soon autonomously provide services in all spheres of life by executing demanding and complex tasks in dynamic, complex environments and by collaborating with human users. In order to push forward the understanding of the safety problem a novel classification of robot hazards is provided. The so-called object interaction hazards are derived which arise when environment objects interact with objects that are manipulated by a robot. Taking into account the current state-of-the-art, it can be stated that this denotes a novel problem area. However, it is already proposed the so-called dynamic risk assessment approach, which shall enable the robot to perceive the risk of current and upcoming situations. In order to realize such a risk-aware planning system for the first time, dynamic risk assessment is integrated within a cognitive architecture serving cognitive functions like anticipation, planning and learning. In this connection, action spaces (sets of possible upcoming situations) are dynamically anticipated assessed with regard to comprised risks. Though, (initial) knowledge about hazards is required in order to realize this. Therefore, a novel procedural model is developed for systematically generating a safety knowledge base. However, it can be assumed that the safety knowledge potentially lacks completeness. The application of AI-based approaches constitutes a noteworthy opportunity. For this reason, light is shed on strategically influential learning methods in safety-critical contexts. Finally, this work describes the generation, integration, utilization, and maintenance of a system-internal safety knowledge base for dynamic risk assessment. It denotes an overall concept toward solving the advanced safety problem and confirms in principle the realization of a safe behavior of autonomous and intelligent systems.Sicherheit autonomer kognitivorientierter Roboter Autonome mobile Serviceroboter sollen zukünftig selbstständig Dienstleistungen in allen Lebensbereichen erbringen, auch in direkter Nähe zum Menschen. Um das Verständnis für Sicherheit in der Robotik zu erwei-tern, wird zunächst eine neue Klassifizierung der möglichen Gefahren vorgenommen. Hiervon wird die Klasse der Objektinteraktionsgefahren abgeleitet. Diese Gefahren entstehen, wenn Objekte der Umgebung mit denen interagieren, die der Roboter greift und transportiert. In Anbetracht des aktuellen Standes der Sicherheits-technik in der Robotik wird klar, dass sich hier ein neues Problemfeld auftut. Grundsätzlich wurde bereits ein dynamischer Risikountersuchungsansatz vorgeschlagen, welcher den Roboter selbst befähigen soll, Situatio-nen hinsichtlich möglicher Gefahren zu untersuchen. Um dadurch eine risikobewusste Handlungsplanung erstmals zu realisieren, wird dieser in eine kognitive Architektur integriert, um kognitive Funktionen, wie Anti-zipation, Planen und Lernen zu nutzen. Hierbei werden mögliche Handlungsräume dynamisch antizipiert und mittels dynamischer Risikoanalyse auf mögliche Gefahren untersucht. Um (Objektinteraktions-) Gefahren mit Hilfe der dynamischer Risikountersuchung bestimmen zu können, bedarf es eines (initialen) Wissens über mögliche Gefahren. Aus diesem Grund wird ein Vorgehensmodell zur systematischen Erzeugung einer solchen Sicherheitswissensbasis entwickelt. Dieses Sicherheitswissen ist jedoch potentiell unvollständig. Daher stellt die Erweiterung und Verfeinerung desselben eine Notwendigkeit dar. Hierbei können die Ansätze aus dem Bereich der künstlichen Intelligenz als nützliche Möglichkeit wahrgenommen werden. Daher werden strate-gisch wichtige Lernmethoden hinsichtlich der Anwendung in einem sicherheitskritischen Kontext untersucht. Die vorliegende Arbeit beschreibt die Erzeugung, die Integration, die Verwendung und die Aufrechterhaltung einer systeminternen Sicherheitswissensbasis zum Zwecke der dynamischen Risikountersuchung. Sie stellt hierbei ein Gesamtkonzept dar, dass zur Lösung des erweiterten Sicherheitsproblems beiträgt und somit die prinzipielle Realisierung des sicheren Betriebs von autonomen und intelligenten bestätigt