35,433 research outputs found
Formation Control for Moving Target Enclosing via Relative Localization
In this paper, we investigate the problem of controlling multiple unmanned
aerial vehicles (UAVs) to enclose a moving target in a distributed fashion
based on a relative distance and self-displacement measurements. A relative
localization technique is developed based on the recursive least square
estimation (RLSE) technique with a forgetting factor to estimates both the
``UAV-UAV'' and ``UAV-target'' relative positions. The formation enclosing
motion is planned using a coupled oscillator model, which generates desired
motion for UAVs to distribute evenly on a circle. The coupled-oscillator-based
motion can also facilitate the exponential convergence of relative localization
due to its persistent excitation nature. Based on the generation strategy of
desired formation pattern and relative localization estimates, a cooperative
formation tracking control scheme is proposed, which enables the formation
geometric center to asymptotically converge to the moving target. The
asymptotic convergence performance is analyzed theoretically for both the
relative localization technique and the formation control algorithm. Numerical
simulations are provided to show the efficiency of the proposed algorithm.
Experiments with three quadrotors tracking one target are conducted to evaluate
the proposed target enclosing method in real platforms.Comment: 8 Pages, accepted by IEEE CDC 202
Cooperative and Distributed Localization for Wireless Sensor Networks in Multipath Environments
We consider the problem of sensor localization in a wireless network in a
multipath environment, where time and angle of arrival information are
available at each sensor. We propose a distributed algorithm based on belief
propagation, which allows sensors to cooperatively self-localize with respect
to one single anchor in a multihop network. The algorithm has low overhead and
is scalable. Simulations show that although the network is loopy, the proposed
algorithm converges, and achieves good localization accuracy
A robust extended H-infinity filtering approach to multi-robot cooperative localization in dynamic indoor environments
Multi-robot cooperative localization serves as an essential task for a team of mobile robots to work within an unknown environment. Based on the real-time laser scanning data interaction, a robust approach is proposed to obtain optimal multi-robot relative observations using the Metric-based Iterative Closest Point (MbICP) algorithm, which makes it possible to utilize the surrounding environment information directly instead of placing a localization-mark on the robots. To meet the demand of dealing with the inherent non-linearities existing in the multi-robot kinematic models and the relative observations, a robust extended H∞ filtering (REHF) approach is developed for the multi-robot cooperative localization system, which could handle non-Gaussian process and measurement noises with respect to robot navigation in unknown dynamic scenes. Compared with the conventional multi-robot localization system using extended Kalman filtering (EKF) approach, the proposed filtering algorithm is capable of providing superior performance in a dynamic indoor environment with outlier disturbances. Both numerical experiments and experiments conducted for the Pioneer3-DX robots show that the proposed localization scheme is effective in improving both the accuracy and reliability of the performance within a complex environment.This work was supported inpart by the National Natural Science Foundation of China under grants 61075094, 61035005 and 61134009
RSSI-Based Self-Localization with Perturbed Anchor Positions
We consider the problem of self-localization by a resource-constrained mobile
node given perturbed anchor position information and distance estimates from
the anchor nodes. We consider normally-distributed noise in anchor position
information. The distance estimates are based on the log-normal shadowing
path-loss model for the RSSI measurements. The available solutions to this
problem are based on complex and iterative optimization techniques such as
semidefinite programming or second-order cone programming, which are not
suitable for resource-constrained environments. In this paper, we propose a
closed-form weighted least-squares solution. We calculate the weights by taking
into account the statistical properties of the perturbations in both RSSI and
anchor position information. We also estimate the bias of the proposed solution
and subtract it from the proposed solution. We evaluate the performance of the
proposed algorithm considering a set of arbitrary network topologies in
comparison to an existing algorithm that is based on a similar approach but
only accounts for perturbations in the RSSI measurements. We also compare the
results with the corresponding Cramer-Rao lower bound. Our experimental
evaluation shows that the proposed algorithm can substantially improve the
localization performance in terms of both root mean square error and bias.Comment: Accepted for publication in 28th Annual IEEE International Symposium
on Personal, Indoor and Mobile Radio Communications (IEEE PIMRC 2017
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