4,386 research outputs found
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
A Bayesian framework for optimal motion planning with uncertainty
Modeling robot motion planning with uncertainty in a Bayesian framework leads to a computationally intractable stochastic control problem. We seek hypotheses that can justify a separate implementation of control, localization and planning. In the end, we reduce the stochastic control problem to path- planning in the extended space of poses x covariances; the transitions between states are modeled through the use of the Fisher information matrix. In this framework, we consider two problems: minimizing the execution time, and minimizing the final covariance, with an upper bound on the execution time. Two correct and complete algorithms are presented. The first is the direct extension of classical graph-search algorithms in the extended space. The second one is a back-projection algorithm: uncertainty constraints are propagated backward from the goal towards the start state
Highly efficient Localisation utilising Weightless neural systems
Efficient localisation is a highly desirable property for an autonomous navigation system. Weightless neural networks offer a real-time approach to robotics applications by reducing hardware and software requirements for pattern recognition techniques. Such networks offer the potential for objects, structures, routes and locations to be easily identified and maps constructed from fused limited sensor data as information becomes available. We show that in the absence of concise and complex information, localisation can be obtained using simple algorithms from data with inherent uncertainties using a combination of Genetic Algorithm techniques applied to a Weightless Neural Architecture
Deep Network Uncertainty Maps for Indoor Navigation
Most mobile robots for indoor use rely on 2D laser scanners for localization,
mapping and navigation. These sensors, however, cannot detect transparent
surfaces or measure the full occupancy of complex objects such as tables. Deep
Neural Networks have recently been proposed to overcome this limitation by
learning to estimate object occupancy. These estimates are nevertheless subject
to uncertainty, making the evaluation of their confidence an important issue
for these measures to be useful for autonomous navigation and mapping. In this
work we approach the problem from two sides. First we discuss uncertainty
estimation in deep models, proposing a solution based on a fully convolutional
neural network. The proposed architecture is not restricted by the assumption
that the uncertainty follows a Gaussian model, as in the case of many popular
solutions for deep model uncertainty estimation, such as Monte-Carlo Dropout.
We present results showing that uncertainty over obstacle distances is actually
better modeled with a Laplace distribution. Then, we propose a novel approach
to build maps based on Deep Neural Network uncertainty models. In particular,
we present an algorithm to build a map that includes information over obstacle
distance estimates while taking into account the level of uncertainty in each
estimate. We show how the constructed map can be used to increase global
navigation safety by planning trajectories which avoid areas of high
uncertainty, enabling higher autonomy for mobile robots in indoor settings.Comment: Accepted for publication in "2019 IEEE-RAS International Conference
on Humanoid Robots (Humanoids)
Simultaneous localization and map-building using active vision
An active approach to sensing can provide the focused measurement capability over a wide field of view which allows correctly formulated Simultaneous Localization and Map-Building (SLAM) to be implemented with vision, permitting repeatable long-term localization using only naturally occurring, automatically-detected features. In this paper, we present the first example of a general system for autonomous localization using active vision, enabled here by a high-performance stereo head, addressing such issues as uncertainty-based measurement selection, automatic map-maintenance, and goal-directed steering. We present varied real-time experiments in a complex environment.Published versio
Stabilization Control of the Differential Mobile Robot Using Lyapunov Function and Extended Kalman Filter
This paper presents the design of a control model to navigate the
differential mobile robot to reach the desired destination from an arbitrary
initial pose. The designed model is divided into two stages: the state
estimation and the stabilization control. In the state estimation, an extended
Kalman filter is employed to optimally combine the information from the system
dynamics and measurements. Two Lyapunov functions are constructed that allow a
hybrid feedback control law to execute the robot movements. The asymptotical
stability and robustness of the closed loop system are assured. Simulations and
experiments are carried out to validate the effectiveness and applicability of
the proposed approach.Comment: arXiv admin note: text overlap with arXiv:1611.07112,
arXiv:1611.0711
Artificial Intelligence and Systems Theory: Applied to Cooperative Robots
This paper describes an approach to the design of a population of cooperative
robots based on concepts borrowed from Systems Theory and Artificial
Intelligence. The research has been developed under the SocRob project, carried
out by the Intelligent Systems Laboratory at the Institute for Systems and
Robotics - Instituto Superior Tecnico (ISR/IST) in Lisbon. The acronym of the
project stands both for "Society of Robots" and "Soccer Robots", the case study
where we are testing our population of robots. Designing soccer robots is a
very challenging problem, where the robots must act not only to shoot a ball
towards the goal, but also to detect and avoid static (walls, stopped robots)
and dynamic (moving robots) obstacles. Furthermore, they must cooperate to
defeat an opposing team. Our past and current research in soccer robotics
includes cooperative sensor fusion for world modeling, object recognition and
tracking, robot navigation, multi-robot distributed task planning and
coordination, including cooperative reinforcement learning in cooperative and
adversarial environments, and behavior-based architectures for real time task
execution of cooperating robot teams
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