Author name cluster

Gerry Chen

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9 papers

1 author row

IROS Conference 2024 Conference Paper

Architectural-Scale Artistic Brush Painting with a Hybrid Cable Robot

Gerry Chen
Tristan Al-Haddad
Frank Dellaert
Seth Hutchinson 0001

Robot art presents an opportunity to both showcase and advance state-of-the-art robotics through the challenging task of creating art. Creating large-scale artworks in particular engages the public in a way that small-scale works cannot, and the distinct qualities of brush strokes contribute to an organic and human-like quality. Combining the large scale of murals with the strokes of the brush medium presents an especially impactful result, but also introduces unique challenges in maintaining precise, dextrous motion control of the brush across such a large workspace. In this work, we present the first robot to our knowledge that can paint architectural-scale murals with a brush. We create a hybrid robot consisting of a cable-driven parallel robot and 4 degree of freedom (DoF) serial manipulator to paint a 27m by 3. 7m mural on windows spanning 2-stories of a building. We discuss our approach to achieving both the scale and accuracy required for brush-painting a mural through a combination of novel mechanical design elements, coordinated planning and control, and on-site calibration algorithms with experimental validations.

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ICRA Conference 2024 Conference Paper

Generalizing Trajectory Retiming to Quadratic Objective Functions

Gerry Chen
Frank Dellaert
Seth Hutchinson 0001

Trajectory retiming is the task of computing a feasible time parameterization to traverse a path. It is commonly used in the decoupled approach to trajectory optimization whereby a path is first found, then a retiming algorithm computes a speed profile that satisfies kino-dynamic and other constraints. While trajectory retiming is most often formulated with the minimum-time objective (i. e. traverse the path as fast as possible), it is not always the most desirable objective, particularly when we seek to balance multiple objectives or when bang-bang control is unsuitable. In this paper, we present a novel algorithm based on factor graph variable elimination that can solve for the global optimum of the retiming problem with quadratic objectives as well (e. g. minimize control effort or match a nominal speed by minimizing squared error), which may extend to arbitrary objectives with iteration. Our work extends prior works, which find only solutions on the boundary of the feasible region, while maintaining the same linear time complexity from a single forward-backward pass. We experimentally demonstrate that (1) we achieve better real-world robot performance by using quadratic objectives in place of the minimum-time objective, and (2) our implementation is comparable or faster than state-of-the-art retiming algorithms.

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ICRA Conference 2023 Conference Paper

A Hybrid Cable-Driven Robot for Non-Destructive Leafy Plant Monitoring and Mass Estimation using Structure from Motion

Gerry Chen
Harsh Muriki
Andrew Sharkey
Cédric Pradalier
Yongsheng Chen
Frank Dellaert

We propose a novel hybrid cable-based robot with manipulator and camera for high-accuracy, medium-throughput plant monitoring in a vertical hydroponic farm and, as an example application, demonstrate non-destructive plant mass estimation. Plant monitoring with high temporal and spatial resolution is important to both farmers and researchers to detect anomalies and develop predictive models for plant growth. The availability of high-quality, off-the-shelf structure-from-motion (SfM) and photogrammetry packages has enabled a vibrant community of roboticists to apply computer vision for non-destructive plant monitoring. While existing approaches tend to focus on either high-throughput (e. g. satellite, unmanned aerial vehicle (UAV), vehicle-mounted, conveyor-belt imagery) or high-accuracy/robustness to occlusions (e. g. turn-table scanner or robot arm), we propose a middle-ground that achieves high accuracy with a medium-throughput, highly automated robot. Our design pairs the workspace scalability of a cable-driven parallel robot (CDPR) with the dexterity of a 4 degree-of-freedom (DoF) robot arm to autonomously image many plants from a variety of viewpoints. We describe our robot design and demonstrate it experimentally by collecting daily photographs of 54 plants from 64 viewpoints each. We show that our approach can produce scientifically useful measurements, operate fully autonomously after initial calibration, and produce better reconstructions and plant property estimates than those of over-canopy methods (e. g. UAV). As example applications, we show that our system can successfully estimate plant mass with a Mean Absolute Error (MAE) of 0. 586g and, when used to perform hypothesis testing on the relationship between mass and age, produces p-values comparable to ground-truth data (p=0. 0020 and p=0. 0016, respectively).

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ICRA Conference 2023 Conference Paper

Constraint Manifolds for Robotic Inference and Planning

Yetong Zhang
Fan Jiang
Gerry Chen
Varun Agrawal
Adam Rutkowski
Frank Dellaert

We propose a manifold optimization approach for solving constrained inference and planning problems. The approach employs a framework that transforms an arbitrary nonlinear equality constrained optimization problem into an unconstrained manifold optimization problem. The core of the transformation process is the formulation of constraint manifolds that represent sets of variables subject to equality constraints. We propose various approaches to define the tan-gent spaces and retraction operations of constraint manifolds, which are crucial for manifold optimization. We evaluate our constraint manifold optimization approach on multiple constrained inference and planning problems, and show that it generates strictly feasible results with increased efficiency as compared to state-of-the-art constrained optimization methods.

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IROS Conference 2022 Conference Paper

Efficient Range-Constraint Manifold Optimization with Application to Cooperative Navigation

Yetong Zhang
Gerry Chen
Adam Rutkowski
Frank Dellaert

We present a manifold optimization approach to solve inference and planning problems with range constraints. The core of our approach is the definition of a manifold that represents points or poses with range constraints. We discover that the manifold of range-constrained points is homogeneous under the rigid transformation group action, and utilize the group action to derive the tangent space, retraction and topology of the manifold. We evaluate the performance of manifold optimization approach on solving range-constrained inference problems over state-of-the-art constrained optimization methods. The results show that manifold optimization with the range-constraint manifold achieves both faster speed and better constraint satisfaction. We further study the conditions of inference problems that we can treat range measurements as constraints in practice.

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ICRA Conference 2022 Conference Paper

GTGraffiti: Spray Painting Graffiti Art from Human Painting Motions with a Cable Driven Parallel Robot

Gerry Chen
Sereym Baek
Juan-Diego Florez
Wanli Qian
Sang-won Leigh
Seth Hutchinson 0001
Frank Dellaert

We present GTGraffiti, a graffiti painting system from Georgia Tech that tackles challenges in art, hardware, and human-robot collaboration. The problem of painting graffiti in a human style is particularly challenging and requires a system-level approach because the robotics and art must be designed around each other. The robot must be highly dynamic over a large workspace while the artist must work within the robot's limitations. Our approach consists of three stages: artwork capture, robot hardware, and planning & control. We use motion capture to capture collaborator painting motions which are then composed and processed into a time-varying linear feedback controller for a cable-driven parallel robot (CDPR) to execute. In this work, we will describe the capturing process, the design and construction of a purpose-built CDPR, and the software for turning an artist's vision into control commands. Our work represents an important step towards faithfully recreating human graffiti artwork by demonstrating that we can reproduce artist motions up to 2m/s and 20m/s 2 within 9. 3mm RMSE to paint artworks.

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IROS Conference 2022 Conference Paper

Locally Optimal Estimation and Control of Cable Driven Parallel Robots using Time Varying Linear Quadratic Gaussian Control

Gerry Chen
Seth Hutchinson 0001
Frank Dellaert

We present a locally optimal tracking controller for Cable Driven Parallel Robot (CDPR) control based on a time-varying Linear Quadratic Gaussian (TV-LQG) controller. In contrast to many methods which use fixed feedback gains, our time-varying controller computes the optimal gains depending on the location in the workspace and the future trajectory. Meanwhile, we rely heavily on offline computation to reduce the burden of online implementation and feasibility checking. Following the growing popularity of probabilistic graphical models for optimal control, we use factor graphs as a tool to formulate our controller for their efficiency, intuitiveness, and modularity. The topology of a factor graph encodes the relevant structural properties of equations in a way that facilitates insight and efficient computation using sparse linear algebra solvers. We first use factor graph optimization to compute a nominal trajectory, then linearize the graph and apply variable elimination to compute the locally optimal, time varying linear feedback gains. Next, we leverage the factor graph formulation to compute the locally optimal, time-varying Kalman Filter gains, and finally combine the locally optimal linear control and estimation laws to form a TV-LQG controller. We compare the tracking accuracy of our TV-LQG controller to a state-of-the-art dual-space feed-forward controller on a 2. 9m x 2. 3m, 4-cable planar robot and demonstrate improved tracking accuracies of 0. 8° and 11. 6 mm root mean square error in rotation and translation respectively.

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ICRA Conference 2021 Conference Paper

Equality Constrained Linear Optimal Control With Factor Graphs

Shuo Yang
Gerry Chen
Yetong Zhang
Howie Choset
Frank Dellaert

This paper presents a novel factor graph-based approach to solve the discrete-time finite-horizon Linear Quadratic Regulator problem subject to auxiliary linear equality constraints within and across time steps. We represent such optimal control problems using constrained factor graphs and optimize the factor graphs to obtain the optimal trajectory and the feedback control policies using the variable elimination algorithm with a modified Gram-Schmidt process. We prove that our approach has the same order of computational complexity as the state-of-the-art dynamic programming approach. Furthermore, current dynamic programming approaches can only handle equality constraints between variables at the same time step, but ours can handle equality constraints among any combination of variables at any time step while maintaining linear complexity with respect to trajectory length. Our approach can be used to efficiently generate trajectories and feedback control policies to achieve periodic motion or repetitive manipulation.

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ICRA Conference 2018 Conference Paper

Robot Button Pressing in Human Environments

Fan Wang
Gerry Chen
Kris Hauser

In order to conduct many desirable functions, service robots will need to actuate buttons and switches that are designed for humans. This paper presents the design of a robot named SwitchIt that is small, relatively inexpensive, easily mounted on a mobile robot, and actuates buttons reliably. Its operating characteristics were developed after conducting a systematic study of buttons and switches in human environments. From this study, we develop a categorization of buttons based on a set of physical properties relevant for robots to operate them. After a human calibrates and annotates buttons in the robot's environment using a hand-held tablet, the system automatically recognizes, pushes, and detects the state of a variety of buttons. Empirical tests demonstrate that the system succeeds in operating 95. 7% of 234 total buttons/switches in an office building and a household environment.

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