AUV-Assisted Diver Navigation

Pelletier, Jesse R.; O’Neill, Brendan W.; Leonard, John J.; Freitag, Lee; Gallimore, Eric

doi:10.1109/lra.2022.3191164

Cited by 8 publications

(11 citation statements)

References 17 publications

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“…Furthermore, a semi-blind passive localization algorithm is developed in [40] with the aim of estimating the source's position for scenarios in which the line-of-sight (LoS) between the source and a subset of sensors might be abscent. Moreover, the authors in [41] have established a testbed for scuba diver navigation assisted by AUV and introduced location estimation algorithms and a communication architecture. Table I summarizes the distinctive characteristics of the literature surveyed in this subsection, where S/M denotes the assumption made on the number of targets, i.e., single/multiple targets deployed.…”

Section: A Related Literaturementioning

confidence: 99%

Penalized Maximum-Likelihood-Based Localization for Unknown Number of Targets Using WSNs: Terrestrial and Underwater Environments

Al-Jarrah,

Alsusa,

Al-Dweik

2024

IEEE Internet Things J.

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This paper proposes a multiple target localization scheme using a clustered wireless sensor network (WSN) for terrestrial and underwater environments. In the considered system, sensors measure the total energy emitted by the targets and transmit quantized versions of their measurements to a data central device (DCD) with the help of intermediate cluster-heads (CHDs), which employ decode-and-forward relaying (DFR). Upon data collection from sensors, the DCD performs the localization process, which involves estimating the number and positions of the targets. Data transmission from the sensors to CHDs takes place through an imperfect medium, which is characterized by a Rician fading model. The penalized maximum likelihood estimator (PMLE), also known as regularized maximum likelihood estimation (MLE), is applied at the DCD to provide optimal estimates of the number and locations of targets. Furthermore, a suboptimal estimator is derived from PMLE that offers comparable performance under certain operating conditions, but with significantly reduced computational complexity. Cramer-Rao lower bound (CRLB) is derived to serve as an asymptotic benchmark for the root mean square error (RMSE) of the estimators in addition to the centroid-based localization benchmark. Monte Carlo simulation is used to evaluate the performance of the proposed estimation techniques under various system conditions. The results show that PMLE can effectively estimate the number and locations of the targets. Furthermore, it is shown that the RMSE of the proposed estimators approaches the CRLB for a large number of sensors and a high signal-tonoise ratio.

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Section: A Related Literaturementioning

confidence: 99%

Penalized Maximum-Likelihood-Based Localization for Unknown Number of Targets Using WSNs: Terrestrial and Underwater Environments

Al-Jarrah,

Alsusa,

Al-Dweik

2024

IEEE Internet Things J.

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“…This methodology is akin to the three-ball intersection positioning technique used for satellites. It proves particularly useful in environments where a Global Navigation Satellite System (GNSS) is unavailable or unreliable, such as indoor spaces like rooms [3], underground coal mines [4], underwater environments [5,6], and other similar settings. In many research studies on ranging-based positioning systems, the principles of graph optimization [7][8][9] or Kalman filtering [10,11] are commonly employed to track the position of the agent.…”

Section: Introductionmentioning

confidence: 99%

A Single-Anchor Cooperative Positioning Method Based on Optimized Inertial Measurement for UAVs

Yang,

Guo,

Tang

2023

Drones

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Benefiting from its structural simplicity and low cost, the inertial/ranging integrated navigation system is widely utilized in multi-agent applications, particularly in unmanned aerial vehicles (UAVs). As the deployment of UAVs in complex environments becomes more prevalent, accurate positioning in sparse observation scenarios has become increasingly important. In satellite-denied environments with few anchors, traditional filtering methods for positioning suffer from poor effectiveness due to the lack of constraints. This article proposes a method to enhance positioning accuracy in such environments by optimizing the inertial outputs of each UAV. The optimization process is based on the range measurements between the UAVs and a single anchor. By solving the optimization function derived using Bayesian theory, the optimized inertial outputs of the UAVs can be obtained. These optimized inertial data are then used in place of the original measurements for position estimation in the filter, resulting in improved performance. Simulation and real-world experiments validate that the proposed method can enhance UAVs’ positioning accuracy in single-anchor environments, surpassing the performance of a single optimizer or filter. Furthermore, the positions estimated by cooperative agents demonstrate higher accuracy than those estimated by individual agents, as more ranging measurements are incorporated.

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“…After setting the prior, the system no longer needs GPS data, so the diver can submerge to begin the transit. Algorithm 1 shows the steps to add the initial prior factor [120]. The external measurement on which the diver relies for localization is their range to the AUV.…”

Section: Diver State Estimationmentioning

confidence: 99%

“…The external measurement on which the diver relies for localization is their range to the AUV. Algorithm 3 shows the steps to add a two-dimensional range factor [120]. Every time a factor is added to the graph, iSAM2 estimates the diver's current position.…”

Section: Diver State Estimationmentioning

confidence: 99%

“…In addition, even in scenarios where ocean current measurements are available, it is beneficial to improve the diver's motion model beyond the constant speed profile which is prone to error. To this end, we leverage the accuracy shown in [120] to propose an alternate method to derive impacts on the diver's motion without an additional sensor.…”

Section: Derived Drift Vectorsmentioning

confidence: 99%

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Adaptive AUV-assisted diver navigation for loosely-coupled teaming in undersea operations

O’Neill

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Human divers face immense challenges in the undersea domain due to constraints on life support, sensory input, and mobility. Due to these challenges, even simple tasks are difficult, and navigation between points of interest is key among them. However, humans have progressively utilized creativity, innovation, and research to explore the Earth’s oceans at greater depths and with increased spatial and temporal detail. Autonomous underwater vehicles often lack the tools, dexterity, or flexibility to manage specific tasks or unforeseen circumstances. However, advances in inertial navigation, computation, and acoustic communication enable autonomous underwater vehicles to perform tasks outside human capability. Acoustic modem technology allows for flexible and reliable communication over an acoustic link. We propose algorithms for cooperative navigation between a diver and an autonomous underwater vehicle as a pathway toward complex undersea human-robot teams. This thesis identifies the communication, software, and algorithmic tools to enable loosely-coupled cooperative navigation between an autonomous underwater vehicle and a diver without a surface presence. Divers present new challenges for cooperative navigation based on their unique motion profile and variable pace from diver to diver. By leveraging the vehicle’s sensor suite, acoustic modem technology, and nonlinear least-squares state estimation, we enable enhanced diver localization and navigation without a surface presence. Adaptation to environmental impacts is explored through measured ocean currents as well as updates to the diver’s motion model based on state estimation analysis. These adaptations produce more efficient diver transits with fewer heading changes. In addition, maneuvering strategies for autonomous underwater vehicles are explored to assess their impact on diver localization accuracy. Experimental validation is shown through surface platforms as proxies for the autonomous underwater vehicle and diver, demonstrating the localization accuracy within a few meters for experiments under various operating conditions. These contributions provide a foundation for undersea human-robot teams to engage in complex tasks with greater efficiency through their combined strengths.

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AUV-Assisted Diver Navigation

Cited by 8 publications

References 17 publications

Penalized Maximum-Likelihood-Based Localization for Unknown Number of Targets Using WSNs: Terrestrial and Underwater Environments

Penalized Maximum-Likelihood-Based Localization for Unknown Number of Targets Using WSNs: Terrestrial and Underwater Environments

A Single-Anchor Cooperative Positioning Method Based on Optimized Inertial Measurement for UAVs

Adaptive AUV-assisted diver navigation for loosely-coupled teaming in undersea operations

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