arXiv subjects

John Jackson

Publications and source records attributed to John Jackson.

5 recordsLinked to original sources

arXiv2024

Watching Grass Grow: Long-term Visual Navigation and Mission Planning for Autonomous Biodiversity Monitoring

We describe a challenging robotics deployment in a complex ecosystem to monitor a rich plant community. The study site is dominated by dynamic grassland vegetation and is thus visually ambiguous and liable to drastic appearance change over the course of a day and especially through the growing season. This dynamism and complexity in appearance seriously impact the stability of the robotics platform, as localisation is a foundational part of that control loop, and so routes must be carefully taught and retaught until autonomy is robust and repeatable. Our system is demonstrated over a 6-week period monitoring the response of grass species to experimental climate change manipulations. We also discuss the applicability of our pipeline to monitor biodiversity in other complex natural settings.

cs.RO↗

arXiv2021

Synergistic Offline-Online Control Synthesis via Local Gaussian Process Regression

Autonomous systems often have complex and possibly unknown dynamics due to, e.g., black-box components. This leads to unpredictable behaviors and makes control design with performance guarantees a major challenge. This paper presents a data-driven control synthesis framework for such systems subject to linear temporal logic on finite traces (LTLf) specifications. The framework combines a baseline (offline) controller with a novel online controller and refinement procedure that improves the baseline guarantees as new data is collected. The baseline controller is computed offline on an uncertain abstraction constructed using Gaussian process (GP) regression on a given dataset. The offline controller provides a lower bound on the probability of satisfying the LTLf specification, which may be far from optimal due to both discretization and regression errors. The synergy arises from the online controller using the offline abstraction along with the current state and new data to choose the next best action. The online controller may improve the baseline guarantees since it avoids the discretization error and reduces regression error as new data is collected. The new data are also used to refine the abstraction and offline controller using local GP regression, which significantly reduces the computation overhead. Evaluations show the efficacy of the proposed offline-online framework, especially when compared against the offline controller.

eess.SY↗

arXiv2021

Strategy Synthesis for Partially-known Switched Stochastic Systems

We present a data-driven framework for strategy synthesis for partially-known switched stochastic systems. The properties of the system are specified using linear temporal logic (LTL) over finite traces (LTLf), which is as expressive as LTL and enables interpretations over finite behaviors. The framework first learns the unknown dynamics via Gaussian process regression. Then, it builds a formal abstraction of the switched system in terms of an uncertain Markov model, namely an Interval Markov Decision Process (IMDP), by accounting for both the stochastic behavior of the system and the uncertainty in the learning step. Then, we synthesize a strategy on the resulting IMDP that maximizes the satisfaction probability of the LTLf specification and is robust against all the uncertainties in the abstraction. This strategy is then refined into a switching strategy for the original stochastic system. We show that this strategy is near-optimal and provide a bound on its distance (error) to the optimal strategy. We experimentally validate our framework on various case studies, including both linear and non-linear switched stochastic systems.

eess.SY↗

arXiv2020

Safety Verification of Unknown Dynamical Systems via Gaussian Process Regression

The deployment of autonomous systems that operate in unstructured environments necessitates algorithms to verify their safety. This can be challenging due to, e.g., black-box components in the control software, or undermodelled dynamics that prevent model-based verification. We present a novel verification framework for an unknown dynamical system from a given set of noisy observations of the dynamics. Using Gaussian processes trained on this data set, the framework abstracts the system as an uncertain Markov process with discrete states defined over the safe set. The transition bounds of the abstraction are derived from the probabilistic error bounds between the regression and underlying system. An existing approach for verifying safety properties over uncertain Markov processes then generates safety guarantees. We demonstrate the versatility of the framework on several examples, including switched and nonlinear systems.

eess.SY↗

arXiv2016

Measuring the speed of light with ultra-compact radio quasars

In this paper, based on a 2.29 GHz VLBI all-sky survey of 613 milliarcsecond ultra-compact radio sources with $0.0035<z<3.787$, we describe a method of identifying the sub-sample which can serve as individual standard rulers in cosmology. If the linear size of the compact structure is assumed to depend on source luminosity and redshift as $l_m=l L^\beta (1+z)^n$, only intermediate-luminosity quasars ($10^{27}$ W/Hz$<L<$ $10^{28}$ W/Hz) show negligible dependence ($|n|\simeq 10^{-3}$, $|\beta|\simeq 10^{-4}$), and thus represent a population of such rulers with fixed characteristic length $l=11.42$ pc. With a sample of 120 such sources covering the redshift range $0.46<z<2.80$, we confirm the existence of dark energy in the Universe with high significance under the assumption of a flat universe, and obtain stringent constraints on both the matter density $\Omega_m=0.323^{+0.245}_{-0.145}$ and the Hubble constant $H_0=66.30^{+7.00}_{-8.50}$ km sec$^{-1}$ Mpc$^{-1}$. Finally, with the angular diameter distances $D_A$ measured for quasars extending to high redshifts ($z\sim 3.0$), we reconstruct the $D_A(z)$ function using the technique of Gaussian processes. This allows us to identify the redshift corresponding to the maximum of the $D_A(z)$ function: $z_m=1.70$ and the corresponding angular diameter distance $D_A(z_m)=1719.01\pm43.46$ Mpc. Similar reconstruction of the expansion rate function $H(z)$ based on the data from cosmic chronometers and BAO gives us $H(z_m)=176.77\pm6.11$ km sec$^{-1}$ Mpc$^{-1}$. These measurements are used to estimate the speed of light: $c=3.039(\pm0.180)\times 10^5$ km/s. This is the first measurement of the speed of light in a cosmological setting referring to the distant past.

astro-ph.CO↗