Nonlinear observability of unicycle multi-robot teams subject to nonuniform environmental disturbances

Heintzman, Larkin; Williams, Ryan K.

doi:10.1007/s10514-020-09923-y

Cited by 11 publications

(6 citation statements)

References 33 publications

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“…Since we simulate an ungrazed pasture, we disable APSIM's grazing module, and an average pasture height is generated through the above parameters as shown in Figure 1a. To generate a 2D map of pasture environments, an evolving process of pastures is simulated through a Gaussian Mixture Model (GMM) (inspired by works in our eventual application domain of multi-robot systems, such as [6,7,9,[43][44][45][46]). The dynamic GMM process is defined as,…”

Section: Simulated Spatiotemporal Datasetmentioning

confidence: 99%

DeepPaSTL: Spatio-Temporal Deep Learning Methods for Predicting Long-Term Pasture Terrains Using Synthetic Datasets

Rangwala

Ahluwalia

et al. 2021

Agronomy

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Effective management of dairy farms requires an accurate prediction of pasture biomass. Generally, estimation of pasture biomass requires site-specific data, or often perfect world assumptions to model prediction systems when field measurements or other sensory inputs are unavailable. However, for small enterprises, regular measurements of site-specific data are often inconceivable. In this study, we approach the estimation of pasture biomass by predicting sward heights across the field. A convolution based sequential architecture is proposed for pasture height predictions using deep learning. We develop a process to create synthetic datasets that simulate the evolution of pasture growth over a period of 30 years. The deep learning based pasture prediction model (DeepPaSTL) is trained on this dataset while learning the spatiotemporal characteristics of pasture growth. The architecture purely learns from the trends in pasture growth through available spatial measurements and is agnostic to any site-specific data, or climatic conditions, such as temperature, precipitation, or soil condition. Our model performs within a 12% error margin even during the periods with the largest pasture growth dynamics. The study demonstrates the potential scalability of the architecture to predict any pasture size through a quantization approach during prediction. Results suggest that the DeepPaSTL model represents a useful tool for predicting pasture growth both for short and long horizon predictions, even with missing or irregular historical measurements.

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Section: Simulated Spatiotemporal Datasetmentioning

confidence: 99%

DeepPaSTL: Spatio-Temporal Deep Learning Methods for Predicting Long-Term Pasture Terrains Using Synthetic Datasets

Rangwala

Ahluwalia

et al. 2021

Agronomy

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“…D. Fontanelli is with the Department of Industrial Engineering, University of Trento, Italy, e-mail: daniele.fontanelli@unitn.it by Heintzman et al [6], who exploit the observability matrix to analyse a team of robots using mutual measurements.…”

Section: Introductionmentioning

confidence: 99%

On Local/Global Constructibility for Mobile Robots Using Bounded Range Measurements

Riz

Palopoli

Fontanelli

2022

IEEE Control Syst. Lett.

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We propose a state-constructibility analysis for a nonholonomic vehicle moving across an environment instrumented with fixed-frame range sensors (anchors). We consider a sparse deployment in which anchors are in a small number, and have finite and non-overlapping sensing ranges. Under the most extreme conditions (i.e., the robot being in sight of two anchors at different times), we provide a sufficient condition on the manoeuvres that the robot is required to execute within the range of each of the anchors in order to achieve global constructibility. When the robot travels along straight lines, these conditions are not met, but we can still have "local" constructibility to a degree quantifiable through the smallest eigenvalue of the Constructibility Gramian (CG). Our second contribution is to show how this metric changes according to the geometric parameters of the linear trajectory with respect to the position of the anchors and their sensing range.

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“…All these single range-based localization approaches usually require persistent relative motion between the anchor and the agent [4]- [6] or the two agents [7]- [9], as outlined in [10]. In the presence of many agents, [11] and [12] show how the nonlinear observability matrix associated with a two-dimensional relative localization problem is dependent on both the rigidity matrix and the relative motion of the agents. Alternative approaches include the implementation of a particle filter when only an IMU and range measurements are available, as in [13], which is computationally expensive.…”

Section: Introductionmentioning

confidence: 99%

Relative Position Estimation in Multi-Agent Systems Using Attitude-Coupled Range Measurements

Shalaby

Cossette

Forbes

et al. 2021

IEEE Robot. Autom. Lett.

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The ability to accurately estimate the position of robotic agents relative to one another, in possibly GPS-denied environments, is crucial to execute collaborative tasks. Interagent range measurements are available at a low cost, due to technologies such as ultra-wideband radio. However, the task of three-dimensional relative position estimation using range measurements in multi-agent systems suffers from unobservabilities. This letter presents a sufficient condition for the observability of the relative positions, and satisfies the condition using a simple framework with only range measurements, an accelerometer, a rate gyro, and a magnetometer. The framework has been tested in simulation and in experiments, where 40-50 cm positioning accuracy is achieved using inexpensive off-the-shelf hardware.

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Nonlinear observability of unicycle multi-robot teams subject to nonuniform environmental disturbances

Cited by 11 publications

References 33 publications

DeepPaSTL: Spatio-Temporal Deep Learning Methods for Predicting Long-Term Pasture Terrains Using Synthetic Datasets

DeepPaSTL: Spatio-Temporal Deep Learning Methods for Predicting Long-Term Pasture Terrains Using Synthetic Datasets

On Local/Global Constructibility for Mobile Robots Using Bounded Range Measurements

Relative Position Estimation in Multi-Agent Systems Using Attitude-Coupled Range Measurements

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