2,403 research outputs found
Information Acquisition with Sensing Robots: Algorithms and Error Bounds
Utilizing the capabilities of configurable sensing systems requires
addressing difficult information gathering problems. Near-optimal approaches
exist for sensing systems without internal states. However, when it comes to
optimizing the trajectories of mobile sensors the solutions are often greedy
and rarely provide performance guarantees. Notably, under linear Gaussian
assumptions, the problem becomes deterministic and can be solved off-line.
Approaches based on submodularity have been applied by ignoring the sensor
dynamics and greedily selecting informative locations in the environment. This
paper presents a non-greedy algorithm with suboptimality guarantees, which does
not rely on submodularity and takes the sensor dynamics into account. Our
method performs provably better than the widely used greedy one. Coupled with
linearization and model predictive control, it can be used to generate adaptive
policies for mobile sensors with non-linear sensing models. Applications in gas
concentration mapping and target tracking are presented.Comment: 9 pages (two-column); 2 figures; Manuscript submitted to the 2014
IEEE International Conference on Robotics and Automatio
Towards enabling predictive optimal energy management systems for hybrid electric vehicles with real world considerations
2021 Spring.Includes bibliographical references.In the pursuit of greater vehicle fleet efficiency, Predictive Optimal Energy Management Systems (POEMS) enabled Plug-in Hybrid Electric Vehicles (PHEV) have shown promising theoretical results. In order to enable the practical development of POEMS enabled PHEV technology, if must first be determined what method and what data is needed is for providing optimal predictions. Research performed at Colorado State University and partner institutions in 2019 and 2020 pursued a novel course in considering the widest range of possible data and methods of prediction currently available including a survey of all feasible Vehicle to Infrastructure (V2I), Vehicle to Vehicle (V2V), Advance Driver Assistance Systems (ADAS), and Ego vehicle CAN data streams with classical and novel machine learning methods. Real world vehicle operation data was collected in Fort Collins Colorado, processed, and used in the development of optimal prediction methods. From the results of this research, concrete conclusions on the relative value of V2I, V2V, and ADAS information for prediction, and high fidelity predictions were obtained for 10 second horizons using specialized Artificial Neural Networks
Parallelized Particle and Gaussian Sum Particle Filters for Large Scale Freeway Traffic Systems
Large scale traffic systems require techniques able to: 1) deal with high amounts of data and heterogenous data coming from different types of sensors, 2) provide robustness in the presence of sparse sensor data, 3) incorporate different models that can deal with various traffic regimes, 4) cope with multimodal conditional probability density functions for the states. Often centralized architectures face challenges due to high communication demands. This paper develops new estimation techniques able to cope with these problems of large traffic network systems. These are Parallelized Particle Filters (PPFs) and a Parallelized Gaussian Sum Particle Filter (PGSPF) that are suitable for on-line traffic management. We show how complex probability density functions of the high dimensional trafc state can be decomposed into functions with simpler forms and the whole estimation problem solved in an efcient way. The proposed approach is general, with limited interactions which reduces the computational time and provides high estimation accuracy. The efciency of the PPFs and PGSPFs is evaluated in terms of accuracy, complexity and communication demands and compared with the case where all processing is centralized
Sequential Kernel Herding: Frank-Wolfe Optimization for Particle Filtering
Recently, the Frank-Wolfe optimization algorithm was suggested as a procedure
to obtain adaptive quadrature rules for integrals of functions in a reproducing
kernel Hilbert space (RKHS) with a potentially faster rate of convergence than
Monte Carlo integration (and "kernel herding" was shown to be a special case of
this procedure). In this paper, we propose to replace the random sampling step
in a particle filter by Frank-Wolfe optimization. By optimizing the position of
the particles, we can obtain better accuracy than random or quasi-Monte Carlo
sampling. In applications where the evaluation of the emission probabilities is
expensive (such as in robot localization), the additional computational cost to
generate the particles through optimization can be justified. Experiments on
standard synthetic examples as well as on a robot localization task indicate
indeed an improvement of accuracy over random and quasi-Monte Carlo sampling.Comment: in 18th International Conference on Artificial Intelligence and
Statistics (AISTATS), May 2015, San Diego, United States. 38, JMLR Workshop
and Conference Proceeding
Joint Localization Based on Split Covariance Intersection on the Lie Group
This paper presents a pose fusion method that
accounts for the possible correlations among measurements.
The proposed method can handle data fusion problems whose
uncertainty has both independent part and dependent part.
Different from the existing methods, the uncertainties of the
various states or measurements are modeled on the Lie algebra
and projected to the manifold through the exponential map,
which is more precise than that modeled in the vector space. The
dealing of the correlation is based on the theory of covariance
intersection, where the independent and dependent parts are split
to yield a more consistent result. In this paper, we provide a novel
method for correlated pose fusion algorithm on the manifold.
Theoretical derivation and analysis are detailed first, and then
the experimental results are presented to support the proposed
theory. The main contributions are threefold: (1) We provide a
theoretical foundation for the split covariance intersection filter
performed on the manifold, where the uncertainty is associated
on the Lie algebra. (2) The proposed method gives an explicit
fusion formalism on SE(3) and SE(2), which covers the most
use cases in the field of robotics. (3) We present a localization
framework that can work both for single robot and multi-robots
systems, where not only the fusion with possible correlation is
derived on the manifold, the state evolution and relative pose
computation are also performed on the manifold. Experimental
results validate its advantage over state-of-the-art methods
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