Zobrazeno 1 - 10
of 271
pro vyhledávání: '"Stilwell, Daniel"'
In this paper, we seek to evaluate the effectiveness of a novel forward-looking sonar system with a limited number of beams for collision avoidance for small autonomous underwater vehicles (AUVs). We present a collision avoidance strategy specificall
Externí odkaz:
http://arxiv.org/abs/2309.05785
Autor:
Kim, Mingyu, Yetkin, Harun, Stilwell, Daniel J., Jimenez, Jorge, Shrestha, Saurav, Stark, Nina
This paper addresses the challenges of optimally placing a finite number of sensors to detect Poisson-distributed targets in a bounded domain. We seek to rigorously account for uncertainty in the target arrival model throughout the problem. Sensor lo
Externí odkaz:
http://arxiv.org/abs/2307.04634
We provide theoretical bounds on the worst case performance of the greedy algorithm in seeking to maximize a normalized, monotone, but not necessarily submodular objective function under a simple partition matroid constraint. We also provide worst ca
Externí odkaz:
http://arxiv.org/abs/2210.09868
We seek to rigorously evaluate the benefit of using a few beams rather than a single beam for a low-cost obstacle avoidance sonar for small AUVs. For a small low-cost AUV, the complexity, cost, and volume required for a multi-beam forward looking son
Externí odkaz:
http://arxiv.org/abs/2210.06537
This paper describes a high-fidelity sonar model and a simulation environment that implements the model. The model and simulation environment have been developed to aid in the design of a forward looking sonar for autonomous underwater vehicles (AUVs
Externí odkaz:
http://arxiv.org/abs/2210.06535
In this work, we present an aided inertial navigation system for an autonomous underwater vehicle (AUV) using an unscented Kalman filter on manifolds (UKF-M). The inertial navigation estimate is aided by a Doppler velocity log (DVL), depth sensor, ac
Externí odkaz:
http://arxiv.org/abs/2210.06510
We present the results of experiments performed using a small autonomous underwater vehicle to determine the location of an isobath within a bounded area. The primary contribution of this work is to implement and integrate several recent developments
Externí odkaz:
http://arxiv.org/abs/2210.02524
In this paper, we propose decentralized and scalable algorithms for Gaussian process (GP) training and prediction in multi-agent systems. To decentralize the implementation of GP training optimization algorithms, we employ the alternating direction m
Externí odkaz:
http://arxiv.org/abs/2203.02865
Gaussian processes (GPs) are a well-known nonparametric Bayesian inference technique, but they suffer from scalability problems for large sample sizes, and their performance can degrade for non-stationary or spatially heterogeneous data. In this work
Externí odkaz:
http://arxiv.org/abs/2107.12797
Autor:
Guo, Jia, Kepler, Michael E., Paruchuri, Sai Tej, Wang, Haoran, Kurdila, Andrew J., Stilwell, Daniel J.
This paper describes an adaptive method in continuous time for the estimation of external fields by a team of $N$ agents. The agents $i$ each explore subdomains $\Omega^i$ of a bounded subset of interest $\Omega\subset X := \mathbb{R}^d$. Ideal adapt
Externí odkaz:
http://arxiv.org/abs/2103.12721