arXiv subjects

Christopher Leet

Publications and source records attributed to Christopher Leet.

9 recordsLinked to original sources

arXiv2026

Eventually Optimal and Scalable Multi-Agent Planning for Block Cave Mining

Automation in underground mining has the potential to significantly enhance safety, operational efficiency, and sustainability. However, effectively coordinating fleets of autonomous vehicles in dynamic mine environments introduces substantial challenges in both optimization and motion planning. To address these challenges, we introduce and formalize the \emph{Block Cave Mining (BCM)} problem, which focuses on computing a transport plan that maximizes ore throughput while satisfying draw ratio constraints. To solve this problem, we propose SAMM, an eventually optimal anytime solver that jointly integrates task assignment, scheduling, and path planning via a mixed-integer linear programming formulation. To improve scalability, we also introduce SAMMS, a variant of SAMM that trades optimality guarantees for efficiency by decomposing the problem into shorter planning subcycles. Experimental evaluations using realistic industrial mine scenarios demonstrate that SAMMS achieves near-optimal throughput and scales effectively to larger fleets and mine layouts.

cs.RO↗

arXiv2025

An Anytime, Scalable and Complete Algorithm for Embedding a Manufacturing Procedure in a Smart Factory

Modern automated factories increasingly run manufacturing procedures using a matrix of programmable machines, such as 3D printers, interconnected by a programmable transport system, such as a fleet of tabletop robots. To embed a manufacturing procedure into a smart factory, an operator must: (a) assign each of its processes to a machine and (b) specify how agents should transport parts between machines. The problem of embedding a manufacturing process into a smart factory is termed the Smart Factory Embedding (SFE) problem. State-of-the-art SFE solvers can only scale to factories containing a couple dozen machines. Modern smart factories, however, may contain hundreds of machines. We fill this hole by introducing the first highly scalable solution to the SFE, TS-ACES, the Traffic System based Anytime Cyclic Embedding Solver. We show that TS-ACES is complete and can scale to SFE instances based on real industrial scenarios with more than a hundred machines.

cs.RO↗

arXiv2025

Jointly Assigning Processes to Machines and Generating Plans for Autonomous Mobile Robots in a Smart Factory

A modern smart factory runs a manufacturing procedure using a collection of programmable machines. Typically, materials are ferried between these machines using a team of mobile robots. To embed a manufacturing procedure in a smart factory, a factory operator must a) assign its processes to the smart factory's machines and b) determine how agents should carry materials between machines. A good embedding maximizes the smart factory's throughput; the rate at which it outputs products. Existing smart factory management systems solve the aforementioned problems sequentially, limiting the throughput that they can achieve. In this paper we introduce ACES, the Anytime Cyclic Embedding Solver, the first solver which jointly optimizes the assignment of processes to machines and the assignment of paths to agents. We evaluate ACES and show that it can scale to real industrial scenarios.

cs.RO↗

arXiv2023

Co-Design of Topology, Scheduling, and Path Planning in Automated Warehouses

We address the warehouse servicing problem (WSP) in automated warehouses, which use teams of mobile agents to bring products from shelves to packing stations. Given a list of products, the WSP amounts to finding a plan for a team of agents which brings every product on the list to a station within a given timeframe. The WSP consists of four subproblems, concerning what tasks to perform (task formulation), who will perform them (task allocation), and when (scheduling) and how (path planning) to perform them. These subproblems are NP-hard individually and become more challenging in combination. The difficulty of the WSP is compounded by the scale of automated warehouses, which frequently use teams of hundreds of agents. In this paper, we present a methodology that can solve the WSP at such scales. We introduce a novel, contract-based design framework which decomposes an automated warehouse into traffic system components. By assigning each of these components a contract describing the traffic flows it can support, we can synthesize a traffic flow satisfying a given WSP instance. Component-wise search-based path planning is then used to transform this traffic flow into a plan for discrete agents in a modular way. Evaluation shows that this methodology can solve WSP instances on real automated warehouses.

eess.SY↗

arXiv2020

Identifying Exoplanets with Deep Learning. IV. Removing Stellar Activity Signals from Radial Velocity Measurements Using Neural Networks

Exoplanet detection with precise radial velocity (RV) observations is currently limited by spurious RV signals introduced by stellar activity. We show that machine learning techniques such as linear regression and neural networks can effectively remove the activity signals (due to starspots/faculae) from RV observations. Previous efforts focused on carefully filtering out activity signals in time using modeling techniques like Gaussian Process regression (e.g. Haywood et al. 2014). Instead, we systematically remove activity signals using only changes to the average shape of spectral lines, and no information about when the observations were collected. We trained our machine learning models on both simulated data (generated with the SOAP 2.0 software; Dumusque et al. 2014) and observations of the Sun from the HARPS-N Solar Telescope (Dumusque et al. 2015; Phillips et al. 2016; Collier Cameron et al. 2019). We find that these techniques can predict and remove stellar activity from both simulated data (improving RV scatter from 82 cm/s to 3 cm/s) and from more than 600 real observations taken nearly daily over three years with the HARPS-N Solar Telescope (improving the RV scatter from 1.753 m/s to 1.039 m/s, a factor of ~ 1.7 improvement). In the future, these or similar techniques could remove activity signals from observations of stars outside our solar system and eventually help detect habitable-zone Earth-mass exoplanets around Sun-like stars.

astro-ph.EP↗

arXiv2020

EXPRES I. HD~3651 an Ideal RV Benchmark

The next generation of exoplanet-hunting spectrographs should deliver up to an order of magnitude improvement in radial velocity precision over the standard 1 m/s state of the art. This advance is critical for enabling the detection of Earth-mass planets around Sun-like stars. New calibration techniques such as laser frequency combs and stabilized etalons ensure that the instrumental stability is well characterized. However, additional sources of error include stellar noise, undetected short-period planets, and telluric contamination. To understand and ultimately mitigate error sources, the contributing terms in the error budget must be isolated to the greatest extent possible. Here, we introduce a new high cadence radial velocity program, the EXPRES 100 Earths program, which aims to identify rocky planets around bright, nearby G and K dwarfs. We also present a benchmark case: the 62-d orbit of a Saturn-mass planet orbiting the chromospherically quiet star, HD 3651. The combination of high eccentricity (0.6) and a moderately long orbital period, ensures significant dynamical clearing of any inner planets. Our Keplerian model for this planetary orbit has a residual RMS of 58 cm/s over a $\sim 6$ month time baseline. By eliminating significant contributors to the radial velocity error budget, HD 3651 serves as a standard for evaluating the long term precision of extreme precision radial velocity (EPRV) programs.

astro-ph.EP↗

arXiv2020

An Extreme Precision Radial Velocity Pipeline: First Radial Velocities from EXPRES

The EXtreme PREcision Spectrograph (EXPRES) is an environmentally stabilized, fiber-fed, $R=137,500$, optical spectrograph. It was recently commissioned at the 4.3-m Lowell Discovery Telescope (LDT) near Flagstaff, Arizona. The spectrograph was designed with a target radial-velocity (RV) precision of 30$\mathrm{~cm~s^{-1}}$. In addition to instrumental innovations, the EXPRES pipeline, presented here, is the first for an on-sky, optical, fiber-fed spectrograph to employ many novel techniques---including an "extended flat" fiber used for wavelength-dependent quantum efficiency characterization of the CCD, a flat-relative optimal extraction algorithm, chromatic barycentric corrections, chromatic calibration offsets, and an ultra-precise laser frequency comb for wavelength calibration. We describe the reduction, calibration, and radial-velocity analysis pipeline used for EXPRES and present an example of our current sub-meter-per-second RV measurement precision, which reaches a formal, single-measurement error of 0.3$\mathrm{~m~s^{-1}}$ for an observation with a per-pixel signal-to-noise ratio of 250. These velocities yield an orbital solution on the known exoplanet host 51 Peg that matches literature values with a residual RMS of 0.895$\mathrm{~m~s^{-1}}$.

astro-ph.IM↗

arXiv2020

Performance Verification of the EXtreme PREcision Spectrograph

The EXtreme PREcision Spectrograph (EXPRES) is a new Doppler spectrograph designed to reach a radial velocity measurement precision sufficient to detect Earth-like exoplanets orbiting nearby, bright stars. We report on extensive laboratory testing and on-sky observations to quantitatively assess the instrumental radial velocity measurement precision of EXPRES, with a focused discussion of individual terms in the instrument error budget. We find that EXPRES can reach a single-measurement instrument calibration precision better than 10 cm/s, not including photon noise from stellar observations. We also report on the performance of the various environmental, mechanical, and optical subsystems of EXPRES, assessing any contributions to radial velocity error. For atmospheric and telescope related effects, this includes the fast tip-tilt guiding system, atmospheric dispersion compensation, and the chromatic exposure meter. For instrument calibration, this includes the laser frequency comb (LFC), flat-field light source, CCD detector, and effects in the optical fibers. Modal noise is mitigated to a negligible level via a chaotic fiber agitator, which is especially important for wavelength calibration with the LFC. Regarding detector effects, we empirically assess the impact on radial velocity precision due to pixel-position non-uniformities (PPNU) and charge transfer inefficiency (CTI). EXPRES has begun its science survey to discover exoplanets orbiting G-dwarf and K-dwarf stars, in addition to transit spectroscopy and measurements of the Rossiter-McLaughlin effect.

astro-ph.IM↗

arXiv2019

Towards a Self-calibrating, Empirical, Light-Weight Model for Tellurics in High-Resolution Spectra

To discover Earth analogs around other stars, next generation spectrographs must measure radial velocity (RV) with 10 cm/s precision. To achieve 10cm/s precision, however, the effects of telluric contamination must be accounted for. The standard approaches to telluric removal are: (a) observing a standard star and (b) using a radiative transfer code. Observing standard stars, however, takes valuable observing time away from science targets. Radiative transfer codes, meanwhile, rely on imprecise line data in the HITRAN database (typical line position uncertainties range from a few to several hundred m/s) and require difficult-to-obtain measurements of water vapor column density for best performance. To address these issues, we present SELENITE: a SELf-calibrating, Empricial, Light-Weight liNear regressIon TElluric model for high-resolution spectra. The model exploits two simple observations: (a) water tellurics grow proportionally to precipitable water vapor and therefore proportionally to each other and (b) non-water tellurics grow proportionally to airmass. Water tellurics can be identified by looking for pixels whose growth correlates with a known calibration water telluric and modelled by regression against it, and likewise non-water tellurics with airmass. The model doesn't require line data, water vapor measurements and additional observations (beyond one-time calibration observations), achieves fits with a reduced chi squared of 1.17 on B stars and 2.95 on K dwarfs, and leaves residuals of 1% (B stars) and 1.1% (K dwarfs) of continuum. Fitting takes seconds on laptop PCs: SELENITE is light-weight enough to guide observing runs.

astro-ph.IM↗