44,714 research outputs found
Comparative analysis of different approaches to target differentiation and localization with sonar
Cataloged from PDF version of article.This study compares the performances of di erent methods for the di erentiation and localization of commonly encountered
features in indoor environments. Di erentiation of such features is of interest for intelligent systems in a variety of applications
such as system control based on acoustic signal detection and identi/cation, map building, navigation, obstacle avoidance,
and target tracking. Di erent representations of amplitude and time-of-2ight measurement patterns experimentally acquired
from a real sonar system are processed. The approaches compared in this study include the target di erentiation algorithm,
Dempster–Shafer evidential reasoning, di erent kinds of voting schemes, statistical pattern recognition techniques (k-nearest
neighbor classi/er, kernel estimator, parameterized density estimator, linear discriminant analysis, and fuzzy c-means clustering
algorithm), and arti/cial neural networks. The neural networks are trained with di erent input signal representations obtained
using pre-processing techniques such as discrete ordinary and fractional Fourier, Hartley and wavelet transforms, and Kohonen’s
self-organizing feature map. The use of neural networks trained with the back-propagation algorithm, usually with fractional
Fourier transform or wavelet pre-processing results in near perfect di erentiation, around 85% correct range estimation and
around 95% correct azimuth estimation, which would be satisfactory in a wide range of applications.
(C) 2002 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserve
Computational intelligence approaches to robotics, automation, and control [Volume guest editors]
No abstract available
A Neural Model of Visually Guided Steering, Obstacle Avoidance, and Route Selection
A neural model is developed to explain how humans can approach a goal object on foot while steering around obstacles to avoid collisions in a cluttered environment. The model uses optic flow from a 3D virtual reality environment to determine the position of objects based on motion discotinuities, and computes heading direction, or the direction of self-motion, from global optic flow. The cortical representation of heading interacts with the representations of a goal and obstacles such that the goal acts as an attractor of heading, while obstacles act as repellers. In addition the model maintains fixation on the goal object by generating smooth pursuit eye movements. Eye rotations can distort the optic flow field, complicating heading perception, and the model uses extraretinal signals to correct for this distortion and accurately represent heading. The model explains how motion processing mechanisms in cortical areas MT, MST, and VIP can be used to guide steering. The model quantitatively simulates human psychophysical data about visually-guided steering, obstacle avoidance, and route selection.Air Force Office of Scientific Research (F4960-01-1-0397); National Geospatial-Intelligence Agency (NMA201-01-1-2016); National Science Foundation (NSF SBE-0354378); Office of Naval Research (N00014-01-1-0624
Scalable Approach to Uncertainty Quantification and Robust Design of Interconnected Dynamical Systems
Development of robust dynamical systems and networks such as autonomous
aircraft systems capable of accomplishing complex missions faces challenges due
to the dynamically evolving uncertainties coming from model uncertainties,
necessity to operate in a hostile cluttered urban environment, and the
distributed and dynamic nature of the communication and computation resources.
Model-based robust design is difficult because of the complexity of the hybrid
dynamic models including continuous vehicle dynamics, the discrete models of
computations and communications, and the size of the problem. We will overview
recent advances in methodology and tools to model, analyze, and design robust
autonomous aerospace systems operating in uncertain environment, with stress on
efficient uncertainty quantification and robust design using the case studies
of the mission including model-based target tracking and search, and trajectory
planning in uncertain urban environment. To show that the methodology is
generally applicable to uncertain dynamical systems, we will also show examples
of application of the new methods to efficient uncertainty quantification of
energy usage in buildings, and stability assessment of interconnected power
networks
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
Safe Local Exploration for Replanning in Cluttered Unknown Environments for Micro-Aerial Vehicles
In order to enable Micro-Aerial Vehicles (MAVs) to assist in complex,
unknown, unstructured environments, they must be able to navigate with
guaranteed safety, even when faced with a cluttered environment they have no
prior knowledge of. While trajectory optimization-based local planners have
been shown to perform well in these cases, prior work either does not address
how to deal with local minima in the optimization problem, or solves it by
using an optimistic global planner.
We present a conservative trajectory optimization-based local planner,
coupled with a local exploration strategy that selects intermediate goals. We
perform extensive simulations to show that this system performs better than the
standard approach of using an optimistic global planner, and also outperforms
doing a single exploration step when the local planner is stuck. The method is
validated through experiments in a variety of highly cluttered environments
including a dense forest. These experiments show the complete system running in
real time fully onboard an MAV, mapping and replanning at 4 Hz.Comment: Accepted to ICRA 2018 and RA-L 201
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