We consider the problem of multi-robot sensor coverage, which deals with deploying a multi-robot team in an environment and optimizing the sensing quality of the overall environment. As real-world environments involve a variety of sensory information, and individual robots are limited in their available number of sensors, successful multi-robot sensor coverage requires the deployment of robots in such a way that each individual team member’s sensing quality is maximized. Additionally, because individual robots have varying complements of sensors and both robots and sensors can fail, robots must be able to adapt and adjust how they value each sensing capability in order to obtain the most complete view of the environment, even through changes in team composition. We introduce a novel formulation for sensor coverage by multi-robot teams with heterogeneous sensing capabilities that maximizes each robot's sensing quality, balancing the varying sensing capabilities of individual robots based on the overall team composition. We propose a solution based on regularized optimization that uses sparsity-inducing terms to ensure a robot team focuses on all possible event types, and which we show is proven to converge to the optimal solution. Through extensive simulation, we show that our approach is able to effectively deploy a multi-robot team to maximize the sensing quality of an environment, responding to failures in the multi-robot team more robustly than non-adaptive approaches.
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Sensor Placement for Globally Optimal Coverage of 3D-Embedded Surfaces
We carry out a structural and algorithmic study
of a mobile sensor coverage optimization problem targeting 2D
surfaces embedded in a 3D workspace. The investigated settings
model multiple important applications including camera net-
work deployment for surveillance, geological monitoring/survey
of 3D terrains, and UVC-based surface disinfection for the
prevention of the spread of disease agents (e.g., SARS-CoV-
2). Under a unified general “sensor coverage” problem, three
concrete formulations are examined, focusing on optimizing
visibility, single-best coverage quality, and cumulative quality,
respectively. After demonstrating the computational intractabil-
ity of all these formulations, we describe approximation schemes
and mathematical programming models for near-optimally
solving them. The effectiveness of our methods is thoroughly
evaluated under realistic and practical scenarios.
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- NSF-PAR ID:
- 10219063
- Date Published:
- Journal Name:
- IEEE International Conference on Robotics and Automation
- ISSN:
- 1049-3492
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Many solid-dose oral drug products are engineered to release their active ingredients into the body at a certain rate. Techniques for measuring the dissolution or degradation of a drug product in vitro play a crucial role in predicting how a drug product will perform in vivo. However, existing techniques are often labor-intensive, time-consuming, irreproducible, require specialized analytical equipment, and provide only “snapshots” of drug dissolution every few minutes. These limitations make it difficult for pharmaceutical companies to obtain full dissolution profiles for drug products in a variety of different conditions, as recommended by the US Food and Drug Administration. Additionally, for drug dosage forms containing multiple controlled-release pellets, particles, beads, granules, etc. in a single capsule or tablet, measurements of the dissolution of the entire multi-particle capsule or tablet are incapable of detecting pellet-to-pellet variations in controlled release behavior. In this work, we demonstrate a simple and fully-automated technique for obtaining dissolution profiles from single controlled-release pellets. We accomplished this by inverting the drug dissolution problem: instead of measuring the increase in the concentration of drug compounds in the solution during dissolution (as is commonly done), we monitor the decrease in the buoyant mass of the solid controlled-release pellet as it dissolves. We weigh single controlled-release pellets in fluid using a vibrating tube sensor, a piece of glass tubing bent into a tuning-fork shape and filled with any desired fluid. An electronic circuit keeps the glass tube vibrating at its resonance frequency, which is inversely proportional to the mass of the tube and its contents. When a pellet flows through the tube, the resonance frequency briefly changes by an amount that is inversely proportional to the buoyant mass of the pellet. By passing the pellet back-and-forth through the vibrating tube sensor, we can monitor its mass as it degrades or dissolves, with high temporal resolution (measurements every few seconds) and mass resolution (700 nanogram resolution). As a proof-of-concept, we used this technique to measure the single-pellet dissolution profiles of several commercial controlled-release proton pump inhibitors in simulated stomach and intestinal contents, as well as comparing name-brand and generic formulations of the same drug. In each case, vibrating tube sensor data revealed significantly different dissolution profiles for the different drugs, and in some cases our method also revealed differences between different pellets from the same drug product. By measuring any controlled-release pellets, particles, beads, or granules in any physiologically-relevant environment in a fully-automated fashion, this method can augment and potentially replace current dissolution tests and support product development and quality assurance in the pharmaceutical industry.
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Neural networks can represent and accurately reconstruct radiance fields for static 3D scenes (e.g., NeRF). Several works extend these to dynamic scenes captured with monocular video, with promising performance. However, the monocular setting is known to be an under-constrained problem, and so methods rely on data-driven priors for reconstructing dynamic content. We replace these priors with measurements from a time-of-flight (ToF) camera, and introduce a neural representation based on an image formation model for continuous-wave ToF cameras. Instead of working with processed depth maps, we model the raw ToF sensor measurements to improve reconstruction quality and avoid issues with low reflectance regions, multi-path interference, and a sensor's limited unambiguous depth range. We show that this approach improves robustness of dynamic scene reconstruction to erroneous calibration and large motions, and discuss the benefits and limitations of integrating RGB+ToF sensors that are now available on modern smartphones.more » « less
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Neural networks can represent and accurately reconstruct radiance fields for static 3D scenes (e.g., NeRF). Several works extend these to dynamic scenes captured with monocular video, with promising performance. However, the monocular setting is known to be an under-constrained problem, and so methods rely on data-driven priors for reconstructing dynamic content. We replace these priors with measurements from a time-of-flight (ToF) camera, and introduce a neural representation based on an image formation model for continuous-wave ToF cameras. Instead of working with processed depth maps, we model the raw ToF sensor measurements to improve reconstruction quality and avoid issues with low reflectance regions, multi-path interference, and a sensor's limited unambiguous depth range. We show that this approach improves robustness of dynamic scene reconstruction to erroneous calibration and large motions, and discuss the benefits and limitations of integrating RGB+ToF sensors now available on modern smartphones.more » « less
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Abstract Ongoing efforts in triboelectric nanogenerators (TENGs) focus on enhancing power generation, but obstacles concerning the economical and cost‐effective production of TENGs continue to prevail. Micro‐/nanostructure engineering of polymer surfaces has been dominantly utilized for boosting the contact triboelectrification, with deposited metal electrodes for collecting the scavenged energy. Nevertheless, this state‐of‐the‐art approach is limited by the vague potential for producing 3D hierarchical surface structures with conformable coverage of high‐quality metal. Laser‐shock imprinting (LSI) is emerging as a potentially scalable approach for directly surface patterning of a wide range of metals with 3D nanoscale structures by design, benefiting from the ultrahigh‐strain‐rate forming process. Here, a TENG device is demonstrated with LSI‐processed biomimetic hierarchically structured metal electrodes for efficient harvesting of water‐drop energy in the environment. Mimicking and transferring hierarchical microstructures from natural templates, such as leaves, into these water‐TENG devices is effective regarding repelling water drops from the device surface, since surface hydrophobicity from these biomicrostructures maximizes the TENG output. Among various leaves' microstructures, hierarchical microstructures from dried bamboo leaves are preferable regarding maximizing power output, which is attributed to their unique structures, containing both dense nanostructures and microscale features, compared with other types of leaves. Also, the triboelectric output is significantly improved by closely mimicking the hydrophobic nature of the leaves in the LSI‐processed metal surface after functionalizing it with low‐surface‐energy self‐assembled‐monolayers. The approach opens doors to new manufacturable TENG technologies for economically feasible and ecologically friendly production of functional devices with directly patterned 3D biomimic metallic surfaces in energy, electronics, and sensor applications.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
