16,043 research outputs found
Fuzzy based obstacle avoidance system for autonomous mobile robot
The goal of this research was to develop a fuzzy obstacle avoidance system for an autonomous mobile robot using IR detection sensors. This paper presents implemented control architecture for behavior-based mobile robot. The mobile robot is able to interact with an unknown environment using a reactive strategy determined by sensory information. Current research in robotics aims to build autonomous and intelligent robots, which can plan its motion in a dynamic environment. Autonomous mobile robots are increasingly used in well structured environment such as warehouses, offices and industries. Fuzzy behavior able to make inferences is well suited for mobile robot navigation because of the uncertainty of the environment. A rule-based fuzzy controller with reactive behavior was implemented and tested on a two wheels mobile robot equipped with infrared sensors to perform collision-free navigation. The experimental results have shown that the proposed architecture provides an efficient and flexible solution for small wheeled mobile robots
Experiments in cooperative human multi-robot navigation
In this paper, we consider the problem of a
group of autonomous mobile robots and a human moving
coordinately in a real-world implementation. The group
moves throughout a dynamic and unstructured environment.
The key problem to be solved is the inclusion of a human in a
real multi-robot system and consequently the multiple robot
motion coordination. We present a set of performance metrics
(system efficiency and percentage of time in formation) and a
novel flexible formation definition whereby a formation
control strategy both in simulation and in real-world
experiments of a human multi-robot system is presented. The
formation control proposed is stable and effective by means of
its uniform dispersion, cohesion and flexibility
New hybrid control architecture for intelligent mobile robot navigation in a manufacturing environment
U radu je prikazana nova hibridna upravljačka arhitektura namenjena za eksploataciju i navigaciju inteligentnih mobilnih robota u tehnološkom okruženju. Arhitektura je bazirana na empirijskom upravljanju i implementaciji koncepta mašinskog učenja u vidu razvoja sistema veštačkih neuronskih mreža za potrebe generisanja inteligentnog ponašanja mobilnog robota. Za razliku od konvencionalne metodologije razvoja inteligentnih mobilnih robota, predložena arhitektura je razvijena na temeljima eksperimentalnog procesa i implementacije sistema veštačkih neuronskih mreža za potrebe generisanja inteligentnog ponašanja. Predložena metodologija razvoja i implementacije inteligentnih mobilnih robota treba da omogući nesmetanu i pouzdanu eksploataciju ali i robustnost u pogledu generisane upravljačke komande, kao odgovora robota na trenutno stanje tehnološkog okruženja.This paper presents a new hybrid control architecture for Intelligent Mobile Robot navigation based on implementation of Artificial Neural Networks for behavior generation. The architecture is founded on the use of Artificial Neural Networks for assemblage of fast reacting behaviors, obstacle detection and module for action selection based on environment classification. In contrast to standard formulation of robot behaviors, in proposed architecture there will be no explicit modeling of robot behaviors. Instead, the use of empirical data gathered in experimental process and Artificial Neural Networks should insure proper generation of particular behavior. In this way, the overall architectural response should be flexible and robust to failures, and consequently provide reliableness in exploitation. These issues are important especially if one takes under consideration that this particular architecture is being developed for mobile robot operating in manufacturing environment as a component of Intelligent Manufacturing System
Lifelong Federated Reinforcement Learning: A Learning Architecture for Navigation in Cloud Robotic Systems
This paper was motivated by the problem of how to make robots fuse and
transfer their experience so that they can effectively use prior knowledge and
quickly adapt to new environments. To address the problem, we present a
learning architecture for navigation in cloud robotic systems: Lifelong
Federated Reinforcement Learning (LFRL). In the work, We propose a knowledge
fusion algorithm for upgrading a shared model deployed on the cloud. Then,
effective transfer learning methods in LFRL are introduced. LFRL is consistent
with human cognitive science and fits well in cloud robotic systems.
Experiments show that LFRL greatly improves the efficiency of reinforcement
learning for robot navigation. The cloud robotic system deployment also shows
that LFRL is capable of fusing prior knowledge. In addition, we release a cloud
robotic navigation-learning website based on LFRL
Reusable Software Components for Robots Using Fuzzy Abstractions
Mobile robots today, while varying greatly in design, often have a large number of similarities in terms of their tasks and goals. Navigation, obstacle avoidance, and vision are all examples. In turn, robots of similar design, but with varying configurations, should be able to share the bulk of their controlling software. Any changes required should be minimal and ideally only to specify new hardware configurations. However, it is difficult to achieve such flexibility, mainly due to the enormous variety of robot hardware available and the huge number of possible configurations. Monolithic controllers that can handle such variety are impossible to build. This paper will investigate these portability problems, as well as techniques to manage common abstractions for user-designed components. The challenge is in creating new methods for robot software to support a diverse variety of robots, while also being easily upgraded and extended. These methods can then provide new ways to support the operational and functional reuse of the same high-level components across a variety of robots
A mosaic of eyes
Autonomous navigation is a traditional research topic in intelligent robotics and vehicles, which requires a robot to perceive its environment through onboard sensors such as cameras or laser scanners, to enable it to drive to its goal. Most research to date has focused on the development of a large and smart brain to gain autonomous capability for robots. There are three fundamental questions to be answered by an autonomous mobile robot: 1) Where am I going? 2) Where am I? and 3) How do I get there? To answer these basic questions, a robot requires a massive spatial memory and considerable computational resources to accomplish perception, localization, path planning, and control. It is not yet possible to deliver the centralized intelligence required for our real-life applications, such as autonomous ground vehicles and wheelchairs in care centers. In fact, most autonomous robots try to mimic how humans navigate, interpreting images taken by cameras and then taking decisions accordingly. They may encounter the following difficulties
An Idiotypic Immune Network as a Short Term Learning Architecture for Mobile Robots
A combined Short-Term Learning (STL) and Long-Term Learning (LTL) approach to
solving mobile robot navigation problems is presented and tested in both real
and simulated environments. The LTL consists of rapid simulations that use a
Genetic Algorithm to derive diverse sets of behaviours. These sets are then
transferred to an idiotypic Artificial Immune System (AIS), which forms the STL
phase, and the system is said to be seeded. The combined LTL-STL approach is
compared with using STL only, and with using a handdesigned controller. In
addition, the STL phase is tested when the idiotypic mechanism is turned off.
The results provide substantial evidence that the best option is the seeded
idiotypic system, i.e. the architecture that merges LTL with an idiotypic AIS
for the STL. They also show that structurally different environments can be
used for the two phases without compromising transferabilityComment: 13 pages, 5 tables, 4 figures, 7th International Conference on
Artificial Immune Systems (ICARIS2008), Phuket, Thailan
A short curriculum of the robotics and technology of computer lab
Our research Lab is directed by Prof. Anton Civit. It is an interdisciplinary group of 23
researchers that carry out their teaching and researching labor at the Escuela
Politécnica Superior (Higher Polytechnic School) and the Escuela de Ingeniería
Informática (Computer Engineering School). The main research fields are: a)
Industrial and mobile Robotics, b) Neuro-inspired processing using electronic spikes,
c) Embedded and real-time systems, d) Parallel and massive processing computer
architecture, d) Information Technologies for rehabilitation, handicapped and elder
people, e) Web accessibility and usability
In this paper, the Lab history is presented and its main publications and research
projects over the last few years are summarized.Nuestro grupo de investigación está liderado por el profesor Civit. Somos un grupo
multidisciplinar de 23 investigadores que realizan su labor docente e investigadora
en la Escuela Politécnica Superior y en Escuela de Ingeniería Informática. Las
principales líneas de investigaciones son: a) Robótica industrial y móvil. b)
Procesamiento neuro-inspirado basado en pulsos electrónicos. c) Sistemas
empotrados y de tiempo real. d) Arquitecturas paralelas y de procesamiento masivo.
e) Tecnología de la información aplicada a la discapacidad, rehabilitación y a las
personas mayores. f) Usabilidad y accesibilidad Web.
En este artículo se reseña la historia del grupo y se resumen las principales
publicaciones y proyectos que ha conseguido en los últimos años
- …