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Yueqi Wang

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3 papers

1 author row

IROS Conference 2024 Conference Paper

Hardware-Based Time Synchronization for a Multi-Sensor System

Yueqi Wang
Tangyou Liu
Licheng Feng
Jinze Wang
Yang Yang
Jianjun Bao
Binghao Li
Liao Wu

Accurate time synchronization is crucial for multisensor fusion, which is widely used in mobile robotics, autonomous driving, and virtual reality. Despite many advancements, precise multi-sensor synchronization is still challenging due to the sensors’ internal characteristics, data filtering, disjointed clock reference, and transmission delay caused by operation system scheduling. This paper proposes a novel hardware-based synchronization solution to achieve synchronization in microsecond-level precision. By introducing a Sensor Adaptor board that provides a unified clock reference, the proposed hardware architecture enables high-precision synchronization across multiple sensors. Furthermore, we develop a method for Visual-Inertial time synchronization that actively controls the exposure duration using an ambient light sensor. By managing the IMU clock signal and exposure trigger, we align the camera’s sampling moment with the authentic IMU sampling time and significantly reduce the time discrepancy in the Visual-Inertial system. Experiments are conducted to evaluate the efficiency of the proposed method and system, including comparisons with previous work. The results indicate that our method can achieve precise time synchronization and be successfully implemented in multi-sensor systems.

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ICML Conference 2020 Conference Paper

Neural Clustering Processes

Ari Pakman
Yueqi Wang
Catalin Mitelut
Jin Hyung Lee
Liam Paninski

Probabilistic clustering models (or equivalently, mixture models) are basic building blocks in countless statistical models and involve latent random variables over discrete spaces. For these models, posterior inference methods can be inaccurate and/or very slow. In this work we introduce deep network architectures trained with labeled samples from any generative model of clustered datasets. At test time, the networks generate approximate posterior samples of cluster labels for any new dataset of arbitrary size. We develop two complementary approaches to this task, requiring either O(N) or O(K) network forward passes per dataset, where N is the dataset size and K the number of clusters. Unlike previous approaches, our methods sample the labels of all the data points from a well-defined posterior, and can learn nonparametric Bayesian posteriors since they do not limit the number of mixture components. As a scientific application, we present a novel approach to neural spike sorting for high-density multielectrode arrays.

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IROS Conference 2017 Conference Paper

Differential pressure control of 3D printed soft fluidic actuators

Tom Kalisky
Yueqi Wang
Benjamin Shih
Dylan Drotman
Saurabh Jadhav
Eliah Aronoff Spencer
Michael T. Tolley

Fluidically actuated soft robots show a great promise for operation in sensitive and unknown environments due to their intrinsic compliance. However, most previous designs use either flow control systems that are noisy, inefficient, sensitive to leaks, and cannot achieve differential pressure (i. e. can only apply either positive or negative pressures with respect to atmospheric), or closed volume control systems that are not adaptable and prohibitively expensive. In this paper, we present a modular, low cost volume control system for differential pressure control of soft actuators. We use this system to actuate three-chamber 3D printed soft robotic modules. For this design, we find a 54% increase in achievable blocked force, and a significant increase in actuator workspace when using differential pressure actuation as compared to the use of only pressure or vacuum. The increased workspace allowed the robot to achieve complex tasks such as writing on a screen with a laser pointer or manipulating fragile objects. Furthermore, we demonstrate a self-healing capability of the combined system by using vacuum to actuate ruptured modules which were no longer responsive to positive pressure.

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