1,634 research outputs found
Human Motion Trajectory Prediction: A Survey
With growing numbers of intelligent autonomous systems in human environments,
the ability of such systems to perceive, understand and anticipate human
behavior becomes increasingly important. Specifically, predicting future
positions of dynamic agents and planning considering such predictions are key
tasks for self-driving vehicles, service robots and advanced surveillance
systems. This paper provides a survey of human motion trajectory prediction. We
review, analyze and structure a large selection of work from different
communities and propose a taxonomy that categorizes existing methods based on
the motion modeling approach and level of contextual information used. We
provide an overview of the existing datasets and performance metrics. We
discuss limitations of the state of the art and outline directions for further
research.Comment: Submitted to the International Journal of Robotics Research (IJRR),
37 page
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Autonomous mobility scooters as assistive tools for the elderly
The aim of this research is to investigate the development of an autonomous navigation system that could be used as an assistive tool for elderly and disabled people in their activities of daily living. The navigation environment is an urban environment and the platform is a Mobility Scooter (MoS). To achieve this aim, a differentially steered MoS was modifed to receive motion commands from a computer and outfitted with onboard sensors that included a Global Positioning System (GPS) receiver and two 2D planar laser range sensors. Perception methods were developed to detect the presence of an outdoor pedestrian walkway. These methods achieved this by processing the range data produced by the laser sensors to identify features that are typically found around walkways like curbs, low vegetation, walls and barriers. A method that utilises GPS localisation information to plan and navigate a route in an outdoor urban environment was also developed. Extensive experimental work was conducted to test the accuracy, repeatability and usefulness of the sensory devices. The developed perception methodologies were evaluated in real world environments while the navigation algorithms were predominantly tested in virtual environments. A navigation system that plans a route in an urban environment and follows it using behaviours arranged in a hierarchy is presented and shown to have the ability to safely navigate an MoS along an outdoor pedestrian path
Socially Compliant Navigation Dataset (SCAND): A Large-Scale Dataset of Demonstrations for Social Navigation
Social navigation is the capability of an autonomous agent, such as a robot,
to navigate in a 'socially compliant' manner in the presence of other
intelligent agents such as humans. With the emergence of autonomously
navigating mobile robots in human populated environments (e.g., domestic
service robots in homes and restaurants and food delivery robots on public
sidewalks), incorporating socially compliant navigation behaviors on these
robots becomes critical to ensuring safe and comfortable human robot
coexistence. To address this challenge, imitation learning is a promising
framework, since it is easier for humans to demonstrate the task of social
navigation rather than to formulate reward functions that accurately capture
the complex multi objective setting of social navigation. The use of imitation
learning and inverse reinforcement learning to social navigation for mobile
robots, however, is currently hindered by a lack of large scale datasets that
capture socially compliant robot navigation demonstrations in the wild. To fill
this gap, we introduce Socially CompliAnt Navigation Dataset (SCAND) a large
scale, first person view dataset of socially compliant navigation
demonstrations. Our dataset contains 8.7 hours, 138 trajectories, 25 miles of
socially compliant, human teleoperated driving demonstrations that comprises
multi modal data streams including 3D lidar, joystick commands, odometry,
visual and inertial information, collected on two morphologically different
mobile robots a Boston Dynamics Spot and a Clearpath Jackal by four different
human demonstrators in both indoor and outdoor environments. We additionally
perform preliminary analysis and validation through real world robot
experiments and show that navigation policies learned by imitation learning on
SCAND generate socially compliant behavior
Online Mapping-Based Navigation System for Wheeled Mobile Robot in Road Following and Roundabout
A road mapping and feature extraction for mobile robot navigation in road roundabout and road following environments is presented in this chapter. In this work, the online mapping of mobile robot employing the utilization of sensor fusion technique is used to extract the road characteristics that will be used with path planning algorithm to enable the robot to move from a certain start position to predetermined goal, such as road curbs, road borders, and roundabout. The sensor fusion is performed using many sensors, namely, laser range finder, camera, and odometry, which are combined on a new wheeled mobile robot prototype to determine the best optimum path of the robot and localize it within its environments. The local maps are developed using an imageâs preprocessing and processing algorithms and an artificial threshold of LRF signal processing to recognize the road environment parameters such as road curbs, width, and roundabout. The path planning in the road environments is accomplished using a novel approach so called Laser Simulator to find the trajectory in the local maps developed by sensor fusion. Results show the capability of the wheeled mobile robot to effectively recognize the road environments, build a local mapping, and find the path in both road following and roundabout
Towards autonomous robotic systems: seamless localization and trajectory planning in dynamic environments
Evolucionar hacia una sociedad mĂĄs automatizada y robotizada en la que podamos convivir con sistemas robĂłticos que desempeñen tareas poco atractivas o peligrosas para el ser humano, supone plantearnos, entre otras cuestiones, quĂ© soluciones existen actualmente y cuĂĄles son las mejoras a incorporar a las mismas. La mayorĂa de aplicaciones ya desarrolladas son soluciones robustas y adecuadas para el fin que se diseñan. Sin embargo, muchas de las tĂ©cnicas implantadas podrĂan funcionar de manera mĂĄs eficiente o bien adaptarse a otras necesidades. Asimismo, en la mayorĂa de aplicaciones robĂłticas adquiere importancia el contexto en el que desempeñan su funciĂłn. Hay entornos estructurados y fĂĄciles de modelar, mientras que otros apenas presentan caracterĂsticas utilizables para obtener informaciĂłn de los mismos.Esta tesis se centra en dos de las funciones bĂĄsicas que debe tener cualquier sistema robĂłtico autĂłnomo para desplazarse de forma robusta en cualquier tipo de entorno: la localizaciĂłn y el cĂĄlculo de trayectorias seguras. AdemĂĄs, los escenarios en los que se desea poner en prĂĄctica la investigaciĂłn son complejos: un parque industrial con zonas cuyas caracterĂsticas de entorno (usualmente geomĂ©tricas) son utilizadas para que un robot se localice, varĂan; y entornos altamente ocupados por otros agentes mĂłviles, como el vestĂbulo de un teatro, en los que se debe considerar las caracterĂsticas dinĂĄmicas de los demĂĄs para calcular un movimiento que sea seguro tanto para el robot como para los demĂĄs agentes.La informaciĂłn que se puede percibir de los escenarios con ambientes no homogĂ©neos, por ejemplo de interior y exterior, suele ser de caracterĂsticas diferentes. Cuando la informaciĂłn que se dispone del entorno proviene de sensores diferentes hay que definir un mĂ©todo que integre las medidas para tener una estimaciĂłn de la localizaciĂłn del robot en todo momento. El tema de la localizaciĂłn se ha investigado intensamente y existen soluciones robustas en interior y exterior, pero no tanto en zonas mixtas. En las zonas de transiciĂłn interior-exterior y viceversa es necesario utilizar sensores que funcionan correctamente en ambas zonas, realizando una integraciĂłn sensorial durante la transiciĂłn para evitar discontinuidades en la localizaciĂłn o incluso que el robot se pierda. De esta manera la navegaciĂłn autĂłnoma, dependiente de la correcta localizaciĂłn, funcionarĂĄ sin discontinuidades ni movimientos bruscos.En entornos dinĂĄmicos es esencial definir una forma de representar la informaciĂłn 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 planificaciĂłn 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 estĂĄticos no funcionan correctamente en escenarios dinĂĄmicos, ya que son puramente reactivos. Es necesario tener en cuenta la predicciĂłn del movimiento de los obstĂĄculos mĂłviles para planificar trayectorias seguras sin colisiĂłn. Los temas abordados y las contribuciones aportadas en esta tesis son:âą Diseño de un sistema de localizaciĂłn continua en entornos de interior y exterior, poniendo especial interĂ©s en la fusiĂłn de las medidas obtenidas de diferentes sensores durante las transiciones interior-exterior, aspecto poco abordado en la literatura. De esta manera se obtiene una estimaciĂłn acotada de la localizaciĂłn durante toda la navegaciĂłn del robot. AdemĂĄs, la localizaciĂłn se integra con una tĂ©cnica reactiva de navegaciĂłn, construyendo un sistema completo de navegaciĂłn. El sistema integrado se ha evaluado en un escenario real de un parque industrial, para una aplicaciĂłn logĂstica en la que las transiciones interior-exterior y viceversa suponĂan un problema fundamental a resolver.âą DefiniciĂłn de un modelo para representar el entorno dinĂĄmico 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 obstĂĄculos de alrededor. Este modelo puede ser utilizado por algoritmos de navegaciĂłn ya existentes, y sirve de base para las nuevas tĂ©cnicas de navegaciĂłn desarrolladas en la tesis y explicadas en los siguientes puntos. âą Desarrollo de una tĂ©cnica de planificaciĂłn y navegaciĂłn basada en el modelo DOVTS. En este modelo se identifica un conjunto de situaciones relativas entre el robot y los obstĂĄculos. A cada situaciĂłn se asocia una estrategia de navegaciĂłn, que considera la seguridad del robot para evitar colisiones, a la vez que intenta minimizar el tiempo al objetivo.âą ImplementaciĂłn de una tĂ©cnica de planificaciĂłn y navegaciĂłn basada en el modelo DOVTS, que utiliza explĂcitamente la informaciĂłn del tiempo para la planificaciĂłn del movimiento. Se desarrolla un algoritmo A*-like que planifica los movimientos de los siguientes instantes, incrementando la maniobrabilidad del robot para la evitaciĂłn de obstĂĄculos respecto al mĂ©todo del anterior punto, a costa de un mayor tiempo de cĂłmputo. Se analizan las diferencias en el comportamiento global del robot con respecto a la tĂ©cnica anterior.Los diferentes aspectos que se han investigado en esta tesis tratan de avanzar en el objetivo de conseguir robots autĂłnomos que puedan adaptarse a nuestra vida cotidiana en escenarios que son tĂpicamente dinĂĄmicos de una forma natural y segura.<br /
2D laser-based probabilistic motion tracking in urban-like environments
All over the world traffic injuries and fatality rates are increasing every year. The combination of negligent and imprudent drivers, adverse road and weather conditions produces tragic results with dramatic loss of life. In this scenario, the use of mobile robotics technology onboard vehicles could reduce casualties. Obstacle motion tracking is an essential ability for car-like mobile robots. However, this task is not trivial in urban environments where a great quantity and variety of obstacles may induce the vehicle to take erroneous decisions. Unfortunately, obstacles close to its sensors frequently cause blind zones behind them where other obstacles could be hidden. In this situation, the robot may lose vital information about these obstructed obstacles that can provoke collisions. In order to overcome this problem, an obstacle motion tracking module based only on 2D laser scan data was developed. Its main parts consist of obstacle detection, obstacle classification, and obstacle tracking algorithms. A motion detection module using scan matching was developed aiming to improve the data quality for navigation purposes; a probabilistic grid representation of the environment was also implemented. The research was initially conducted using a MatLab simulator that reproduces a simple 2D urban-like environment. Then the algorithms were validated using data samplings in real urban environments. On average, the results proved the usefulness of considering obstacle paths and velocities while navigating at reasonable computational costs. This, undoubtedly, will allow future controllers to obtain a better performance in highly dynamic environments.Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES
Outdoor navigation of mobile robots
AGVs in the manufacturing industry currently constitute the largest application area for mobile robots. Other applications have been gradually emerging, including various transporting tasks in demanding environments, such as mines or harbours. Most of the new potential applications require a free-ranging navigation system, which means that the path of a robot is no longer bound to follow a buried inductive cable. Moreover, changing the route of a robot or taking a new working area into use must be as effective as possible. These requirements set new challenges for the navigation systems of mobile robots. One of the basic methods of building a free ranging navigation system is to combine dead reckoning navigation with the detection of beacons at known locations. This approach is the backbone of the navigation systems in this study.
The study describes research and development work in the area of mobile robotics including the applications in forestry, agriculture, mining, and transportation in a factory yard. The focus is on describing navigation sensors and methods for position and heading estimation by fusing dead reckoning and beacon detection information. A Kalman filter is typically used here for sensor fusion.
Both cases of using either artificial or natural beacons have been covered. Artificial beacons used in the research and development projects include specially designed flat objects to be detected using a camera as the detection sensor, GPS satellite positioning system, and passive transponders buried in the ground along the route of a robot. The walls in a mine tunnel have been used as natural beacons. In this case, special attention has been paid to map building and using the map for positioning.
The main contribution of the study is in describing the structure of a working navigation system, including positioning and position control. The navigation system for mining application, in particular, contains some unique features that provide an easy-to-use procedure for taking new production areas into use and making it possible to drive a heavy mining machine autonomously at speed comparable to an experienced human driver.reviewe
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