Adaptive Aerial Localization Using Lissajous Search Patterns

Steckenrider, J. Josiah

doi:10.1109/tro.2021.3126225

Cited by 4 publications

(2 citation statements)

References 51 publications

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“…Search methods based on Lissajous curves can be both cyclical and comprehensive as demonstrated in Fig. 2 , and they show significant promise as a potential alternative search method to those in current use 20 . In particular, LPs have been shown to more quickly reach an 80% confidence threshold of mission completion when optimized for assumed target size 21 and are especially effective against canonical patterns when the target is modeled as adversarial 22 .…”

Section: Introductionmentioning

confidence: 99%

Lissajous curves as aerial search patterns

Steckenrider,

Miller,

Blankenship

et al. 2024

Sci Rep

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Manned and unmanned systems are prevalent in a wide range of aerial searching applications. For aircraft whose trajectory is not or cannot be planned on-the-fly, optimal deterministic search pattern generation is a critical area of research. Lissajous curves have recently caught attention as excellent candidates for all kinds of aerial search applications, but little fundamental research has been done to understand how best to design Lissajous pattern (LP)s for this use. This paper examines the optimization of these search patterns from analytical, numerical, and data-driven perspectives to establish the state of the field in Lissajous curves for aerial search. From an analytical perspective, it was found that the average expected distance between a Lissajous searcher and a random target on a unit square approaches 0.586 as search time increases. Furthermore, an analytical approximation for the average searcher speed was found to guarantee error of no more than 22.1%. Important outcomes from the numerical optimization of Lissajous search patterns include the development of an intuitive evaluation criterion and the conclusion that irrational frequency ratios near 0.8 typically yield highest performance. Finally, while a robust predictive model for fast pattern optimization is yet out of reach, initial results indicate that such an approach shows promise.

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Section: Introductionmentioning

confidence: 99%

Lissajous curves as aerial search patterns

Steckenrider,

Miller,

Blankenship

et al. 2024

Sci Rep

Self Cite

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“…Human localization is an active research topic with many applications, from the accurate determination of human position in teams of humans and mobile robots to the study of human biomechanics. The variety of fields implementing human–robot teams includes agriculture [ 1 ], mining [ 2 ], search-and-rescue [ 3 ], and exploration [ 4 ]. Each sector has its own requirements for safe and effective human–robot teams due to the different environments and levels of human involvement.…”

Section: Introductionmentioning

confidence: 99%

Walking Trajectory Estimation Using Multi-Sensor Fusion and a Probabilistic Step Model

Rabb

Steckenrider

2023

Sensors

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This paper presents a framework for accurately and efficiently estimating a walking human’s trajectory using a computationally inexpensive non-Gaussian recursive Bayesian estimator. The proposed framework fuses global and inertial measurements with predictions from a kinematically driven step model to provide robustness in localization. A maximum a posteriori-type filter is trained on typical human kinematic parameters and updated based on live measurements. Local step size estimates are generated from inertial measurement units using the zero-velocity update (ZUPT) algorithm, while global measurements come from a wearable GPS. After each fusion event, a gradient ascent optimizer efficiently locates the highest likelihood of the individual’s location which then triggers the next estimator iteration.The proposed estimator was compared to a state-of-the-art particle filter in several Monte Carlo simulation scenarios, and the original framework was found to be comparable in accuracy and more efficient at higher resolutions. It is anticipated that the methods proposed in this work could be more useful in general real-time estimation (beyond just personal navigation) than the traditional particle filter, especially if the state is many-dimensional. Applications of this research include but are not limited to: in natura biomechanics measurement, human safety in manual fieldwork environments, and human/robot teaming.

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Motion Tracking of a High-Speed Multilink System Using Dynamic Measurements Fusion

Zhang,

Steckenrider,

Furukawa

2023

Journal of Dynamic Systems, Measurement, and Control

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This paper presents a technique for tracking the high-speed motion of a multi-link system using Inertial Measurement Units (IMUs) in a new sensor arrangement, an approach which is referred to as dynamic measurements fusion. The proposed technique incorporates accelerometers with traditional gyroscopes to measure joint angular velocities, while joint angles are measured with magnetometers. Comparative studies with conventional techniques show that the proposed technique tracks the motion of a multi-link system accurately at both low (0.5 m/s) and high (5 m/s) speeds. Further analysis with different levels of measurement noise demonstrates the robustness of the proposed technique and its overall capability for tracking joint angular velocities and angles.

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Adaptive Aerial Localization Using Lissajous Search Patterns

Cited by 4 publications

References 51 publications

Lissajous curves as aerial search patterns

Lissajous curves as aerial search patterns

Walking Trajectory Estimation Using Multi-Sensor Fusion and a Probabilistic Step Model

Motion Tracking of a High-Speed Multilink System Using Dynamic Measurements Fusion

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