Publications

2025

1. ACC

   Modeling human-autonomy team steering behavior in shared-autonomy driving scenarios

   Rene Mai, Kara Daveron, Agung Julius, and 1 more author

   In 2025 American Controls Conference (ACC) (Under review), 2025

   Bib

   @inproceedings{mai_ACC_2025,
     address = {Denver, CO, USA},
     title = {Modeling human-autonomy team steering behavior in shared-autonomy driving scenarios},
     booktitle = {2025 {American} {Controls} {Conference} ({ACC}) (Under review)},
     publisher = {IFAC},
     author = {Mai, Rene and Daveron, Kara and Julius, Agung and Mishra, Sandipan},
     year = {2025}
   }

2024

1. An unsupervised online anomaly detection method for metal additive manufacturing processes via a statistical time-frequency domain algorithm

   Alvin Chen, Fotis Kopsaftopoulos, and Sandipan Mishra

   Structural Health Monitoring, May 2024

   Abs Bib

   Anomalies often occur in metal additive manufacturing from processing inconsistencies and uncertainty. A robust fault detection system that uses sensor measurements such as melt pool imaging has the potential to improve part quality and save production time by anticipating print failure. Toward this goal, we develop and validate a fault detection technique using melt pool geometry-related measurements from an in situ near-infrared optical camera. This method is unsupervised and is trained on a small dataset, mitigating human error in classifying fault types, and reducing lead times for preparing training datasets. Furthermore, this method uses learned geometry-informed nominal behavior of the melt pool signal to make informed decisions on the process health. There are spatial-temporal characteristics embedded in the melt pool images, caused by the periodicity in the geometry-dependent raster pattern. These characteristics can be captured in the frequency domain using the signal spectrogram, a representation of the frequency content over time. Defects will appear in the spectrogram, disrupting the healthy spectral response. To quantify healthy spectrograms, we use principal component (PC) decomposition to extract the features of these spectrograms as a set of nominal basis vectors. Anomaly detection is then performed by calculating the error between the original and reconstructed spectrogram vector by projection of the spectrogram PCs onto the nominal basis. The reconstruction error for anomalous signals is larger than that from healthy signals, which is then used for fault detection. A one-tailed statistical test is used to determine the fault detection threshold for the reconstruction error signal. This method is tested on three raster patterns and performs better than a comparative time-series thresholding method. We demonstrate that this time-frequency algorithm can detect both temporal faults (which occur at a single time instant) and spatial faults (such as those introduced by an improper sintering), differentiating them from nominal operation.

   @article{chen_unsupervised_2024,
     title = {An unsupervised online anomaly detection method for metal additive manufacturing processes via a statistical time-frequency domain algorithm},
     volume = {23},
     issn = {1475-9217, 1741-3168},
     url = {https://journals.sagepub.com/doi/10.1177/14759217231193702},
     doi = {10.1177/14759217231193702},
     language = {en},
     number = {3},
     urldate = {2024-10-20},
     journal = {Structural Health Monitoring},
     author = {Chen, Alvin and Kopsaftopoulos, Fotis and Mishra, Sandipan},
     month = may,
     year = {2024},
     pages = {1926--1948}
   }

2. IEEE

   Real-Time Melt Pool Homogenization Through Geometry-Informed Control in Laser Powder Bed Fusion Using Reinforcement Learning

   Bumsoo Park, Alvin Chen, and Sandipan Mishra

   IEEE Transactions on Automation Science and Engineering, May 2024

   Bib

   @article{park_real-time_2024,
     title = {Real-{Time} {Melt} {Pool} {Homogenization} {Through} {Geometry}-{Informed} {Control} in {Laser} {Powder} {Bed} {Fusion} {Using} {Reinforcement} {Learning}},
     copyright = {https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html},
     issn = {1545-5955, 1558-3783},
     url = {https://ieeexplore.ieee.org/document/10499998/},
     doi = {10.1109/TASE.2024.3386882},
     urldate = {2024-10-20},
     journal = {IEEE Transactions on Automation Science and Engineering},
     author = {Park, Bumsoo and Chen, Alvin and Mishra, Sandipan},
     year = {2024},
     pages = {1--12}
   }

3. ACC

   Allocation of Control Authority Between Dynamic Inversion and Reinforcement Learning for Autonomous Helicopter Aerial Refueling

   Damsara Jayarathne, Santiago Paternain, and Sandipan Mishra

   In 2024 American Control Conference (ACC), Jul 2024

   Bib

   @inproceedings{jayarathne_allocation_2024,
     address = {Toronto, ON, Canada},
     title = {Allocation of {Control} {Authority} {Between} {Dynamic} {Inversion} and {Reinforcement} {Learning} for {Autonomous} {Helicopter} {Aerial} {Refueling}},
     copyright = {https://doi.org/10.15223/policy-029},
     isbn = {9798350382655},
     url = {https://ieeexplore.ieee.org/document/10644725/},
     doi = {10.23919/ACC60939.2024.10644725},
     urldate = {2024-10-20},
     booktitle = {2024 {American} {Control} {Conference} ({ACC})},
     publisher = {IEEE},
     author = {Jayarathne, Damsara and Paternain, Santiago and Mishra, Sandipan},
     month = jul,
     year = {2024},
     pages = {2386--2392}
   }

4. IEEE/ASME

   Aerodynamics-Aware Design and Analysis of Controllers for Tailsitter Vehicles

   Kristoff McIntosh, Jayden Marvin Smith, and Sandipan Mishra

   IEEE/ASME Transactions on Mechatronics, Aug 2024

   Abs Bib

   Tailsitter transitioning unmanned aerial systems (t-UAS) are vehicles that, through rigid body rotation, are capable of operating in both vertical takeoff and landing (VTOL) and fixed-wing flight regimes. The typical approach for control design for tailsitter t-UAS considers the aerodynamics of the wings as a disturbance to either be avoided or compensated for, specifically during the pure VTOL and transition flight regimes. This can result in overly conservative controllers or otherwise degraded tracking performance. To address this, we present a unified design and analysis approach for a controller that explicitly accounts for the effect of wing aerodynamics for tailsitter t-UAS operating in the transition flight regime. The overall control architecture uses feedback linearization with nested control loops, with position controlled in the outer-loop and attitude controlled in the inner loop. The outer loop uses feedforward knowledge of the aerodynamic forces from the mission planning stage, whereas the inner loop is designed assuming that moments generated by the aerodynamic forces are negligible. We derive analytical conditions that guarantee stability of the outer and inner loop controllers in the presence of bounded uncertainty in the aerodynamic forces and moments. We then provide performance bounds for both the outer and inner loop in the presence of these unmodeled or uncertain aerodynamic forces and moments. Finally, we use a high fidelity simulation of a quadrotor biplane tailsitter to illustrate the stability result and quantify controller performance statistically.

   @article{mcintosh_aerodynamics-aware_2024,
     title = {Aerodynamics-{Aware} {Design} and {Analysis} of {Controllers} for {Tailsitter} {Vehicles}},
     volume = {29},
     copyright = {https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/USG.html},
     issn = {1083-4435, 1941-014X},
     url = {https://ieeexplore.ieee.org/document/10557463/},
     doi = {10.1109/TMECH.2024.3402621},
     number = {4},
     urldate = {2024-10-20},
     journal = {IEEE/ASME Transactions on Mechatronics},
     author = {McIntosh, Kristoff and Smith, Jayden Marvin and Mishra, Sandipan},
     month = aug,
     year = {2024},
     pages = {3100--3108}
   }

5. IFAC

   Analysis of human steering behavior differences in human-in-control and autonomy-in-control driving

   Rene Mai, Agung Julius, and Sandipan Mishra

   In 2024 IFAC Workshop on Cyber- Physical Human Systems (CPHS) (Accepted), Aug 2024

   Bib

   @inproceedings{mai_analysis_2024,
     address = {Antalya, Turkiye},
     title = {Analysis of human steering behavior differences in human-in-control and autonomy-in-control driving},
     url = {https://arxiv.org/html/2410.00181v1 (previous version)},
     urldate = {2024-10-02},
     booktitle = {2024 {IFAC} {Workshop} {on} {Cyber-} {Physical} {Human} {Systems} ({CPHS}) (Accepted)},
     publisher = {IFAC},
     author = {Mai, Rene and Julius, Agung and Mishra, Sandipan},
     year = {2024},
     file = {© 2024 the authors. This work has been accepted to IFAC for publication at the 5th IFAC Workshop on Cyber-Physical Human Systems under a Creative Commons Licence CC-BY-NC-ND:C\:\\Users\\relis\\Zotero\\storage\\VVHNBEST\\2410.html:text/html}
   }

6. ASME

   Generalized two-point visual control model of human steering for accurate state estimation

   Rene Mai, Katherine Sears, Grace Roessling, and 2 more authors

   ASME Letters in Dynamic Systems and Control, Sep 2024

   Abs Bib

   We derive and validate a generalization of the two-point visual control model, an accepted cognitive science model for human steering behavior. The generalized model is needed as current steering models are either insufficiently accurate or too complex for online state estimation. We demonstrate that the generalized model replicates specific human steering behavior with high precision (85% reduction in modeling error) and integrate this model into a human-as-advisor framework where human steering inputs are used for state estimation. As a benchmark study, we use this framework to decipher ambiguous lane markings represented by biased lateral position measurements. We demonstrate that, with the generalized model, the state estimator can accurately estimate the true vehicle state, providing lateral state estimates with under 0.15 m error across participants. However, without the generalized model, the estimator cannot accurately estimate the vehicle’s lateral state.

   @article{mai_generalized_2024,
     title = {Generalized two-point visual control model of human steering for accurate state estimation},
     issn = {2689-6117},
     url = {https://doi.org/10.1115/1.4066630},
     doi = {10.1115/1.4066630},
     urldate = {2024-09-25},
     journal = {ASME Letters in Dynamic Systems and Control},
     author = {Mai, Rene and Sears, Katherine and Roessling, Grace and Julius, Agung and Mishra, Sandipan},
     month = sep,
     year = {2024},
     pages = {1--10}
   }

7. ASME

   Generalized Two-Point Visual Control Model of Human Steering for Accurate State Estimation1

   Rene E. Mai, Katherine Sears, Grace Roessling, and 2 more authors

   ASME Letters in Dynamic Systems and Control, Oct 2024

   Abs Bib

   We derive and validate a generalization of the two-point visual control model, an accepted cognitive science model for human steering behavior. The generalized model is needed as current steering models are either insufficiently accurate or too complex for online state estimation. We demonstrate that the generalized model replicates specific human steering behavior with high precision (85% reduction in modeling error) and integrate this model into a human-as-advisor framework where human steering inputs are used for state estimation. As a benchmark study, we use this framework to decipher ambiguous lane markings represented by biased lateral position measurements. We demonstrate that, with the generalized model, the state estimator can accurately estimate the true vehicle state, providing lateral state estimates with under 0.15 m error across participants. However, without the generalized model, the estimator cannot accurately estimate the vehicle’s lateral state.

   @article{mai_generalized,
     bibtex_show = true,
     author = {Mai, Rene E. and Sears, Katherine and Roessling, Grace and Julius, Agung and Mishra, Sandipan},
     title = {{Generalized Two-Point Visual Control Model of Human Steering for Accurate State Estimation1}},
     journal = {ASME Letters in Dynamic Systems and Control},
     volume = {5},
     number = {1},
     pages = {011004},
     year = {2024},
     month = oct,
     issn = {2689-6117},
     doi = {10.1115/1.4066630},
     url = {https://doi.org/10.1115/1.4066630},
     eprint = {https://asmedigitalcollection.asme.org/lettersdynsys/article-pdf/5/1/011004/7387153/aldsc\_5\_1\_011004.pdf}
   }

2023

1. A DATA-SPARSE APPROACH TO IN-SITU FAULT DETECTION AND IDENTIFICATION FOR METAL ADDITIVE MANUFACTURING

   Alvin Chen, Fotis Kopsaftopoulos, and Sandipan Mishra

   In Proceedings of the 14th International Workshop on Structural Health Monitoring, Sep 2023

   Abs Bib PDF

   With increasing adoption of metal additive manufacturing (AM) in manufacturing, detecting faults in the printing process has the potential to reduce waste from failed prints and streamline the production process. To increase robustness of anomaly detection, a statistical method of detecting faults from melt pool images is presented. This method uses parametric identification of 1D compression of melt pool images to build a nominal predictive model. Nominal melt pools result in residuals that are Gaussian white noise processes, whereas anomalous melt pools will not follow this distribution. Detection is performed through statistical comparison of incoming data with a nominal reference generated on sparse data. This approach successfully applies statistical time-series methods to detect anomalous melt pools in a metal AM process.

   @inproceedings{chen_data-sparse_2023,
     title = {A {DATA}-{SPARSE} {APPROACH} {TO} {IN}-{SITU} {FAULT} {DETECTION} {AND} {IDENTIFICATION} {FOR} {METAL} {ADDITIVE} {MANUFACTURING}},
     isbn = {9781605956930},
     url = {https://www.dpi-proceedings.com/index.php/shm2023/article/view/36939},
     doi = {10.12783/shm2023/36939},
     urldate = {2024-10-20},
     booktitle = {Proceedings of the 14th {International} {Workshop} on {Structural} {Health} {Monitoring}},
     publisher = {Destech Publications, Inc.},
     author = {Chen, Alvin and Kopsaftopoulos, Fotis and Mishra, Sandipan},
     month = sep,
     year = {2023},
   }

2. IEEE/ASME

   Safe residual reinforcement learning for helicopter aerial refueling

   Damsara Jayarathne, Santiago Paternain, and Sandipan Mishra

   In 2023 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), Jun 2023

   Bib

   @inproceedings{jayarathne_safe_2023,
     address = {Seattle, WA, USA},
     title = {Safe residual reinforcement learning for helicopter aerial refueling},
     copyright = {https://doi.org/10.15223/policy-029},
     isbn = {9781665476331},
     url = {https://ieeexplore.ieee.org/document/10196137/},
     doi = {10.1109/AIM46323.2023.10196137},
     urldate = {2024-10-20},
     booktitle = {2023 {IEEE}/{ASME} {International} {Conference} on {Advanced} {Intelligent} {Mechatronics} ({AIM})},
     publisher = {IEEE},
     author = {Jayarathne, Damsara and Paternain, Santiago and Mishra, Sandipan},
     month = jun,
     year = {2023},
     pages = {263--269}
   }

3. ACC

   Human-as-advisor in the loop for autonomous lane-keeping

   Rene Mai, Sandipan Mishra, and Agung Julius

   In 2023 American Control Conference (ACC), May 2023

   Abs Bib

   This paper presents a human-as-advisor architecture for shared human-machine autonomy in dynamic systems. In the human-as-advisor architecture, the human provides suggested control actions to the autonomous system; the system uses a model of the human controller to ascertain the system’s state as perceived by the human. The system combines this information with additional sensor measurements, yielding an improved state estimate. We apply this architecture to the problem of lane-centering an autonomous vehicle in the presence of conflicting lane markings that render the true lane center uncertain. We model conflicting lane markings with a multi-component Gaussian mixture model. The humansuggested course of action is interpreted as an additional sensor measurement, which a Kalman filter is designed to combine with a speedometer and camera for improving the state estimate. With human input from our human-as-advisor architecture, the vehicle centers itself in the lane; without human input, the vehicle does not center itself. We also demonstrate the human-as-advisor architecture is robust to additive output matrix uncertainty and non-linear perturbations in the human model used to interpret the human-suggested control actions.

   @inproceedings{mai_human-as-advisor_2023,
     address = {San Diego, CA, USA},
     title = {Human-as-advisor in the loop for autonomous lane-keeping},
     isbn = {9798350328066},
     url = {https://ieeexplore.ieee.org/document/10156374/},
     doi = {10.23919/ACC55779.2023.10156374},
     language = {en},
     urldate = {2023-09-05},
     booktitle = {2023 {American} {Control} {Conference} ({ACC})},
     publisher = {IEEE},
     author = {Mai, Rene and Mishra, Sandipan and Julius, Agung},
     month = may,
     year = {2023},
     pages = {3895--3900}
   }

2022

1. ASME

   Unsupervised Online Anomaly Detection of Metal Additive Manufacturing Processes via a Statistical Time-Frequency Domain Approach

   Alvin Chen, Fotis Kopsaftopoulos, and Sandipan Mishra

   In Volume 1: Acoustics, Vibration, and Phononics, Oct 2022

   Abs Bib

   Fault detection techniques in metal additive manufacturing (AM) have explored a variety of monitoring methods to flag anomalies as they occur during the sintering process. Although many in-situ techniques are able to adeptly detect these abnormalities, several utilize machine learning black box methods that do not easily transfer to varying print geometries. An approach that is adaptable to a multitude of geometries holds an advantage in determining anomalies for more complex cross-sections and raster patterns. To address this lack of a geometry agnosticism, we propose a method that detects faults using the frequency content of the melt pool image response through an unsupervised approach. Scan line length and scan speed extracted from known geometry can be translated to associated frequencies via a spectrogram. We examine three specific geometries to determine detection performance on each by comparing the frequency content to the nominal response. A deviation from the expected performance will signify that an anomaly has occurred. We verify this approach is feasible for fault detection and is accurate in detecting anomalies that are hard to observe in the image time series. A feasible geometry agnostic method and the current interpretability will be discussed in this paper. The results reached in this paper strongly indicate that the approach is promising, has potential for improvement, and that a geometrically independent method is sensible. With further work, a generic algorithm applicable on any geometry will be achievable.

   @inproceedings{chen_unsupervised_2022,
     address = {Columbus, Ohio, USA},
     title = {Unsupervised {Online} {Anomaly} {Detection} of {Metal} {Additive} {Manufacturing} {Processes} via a {Statistical} {Time}-{Frequency} {Domain} {Approach}},
     isbn = {9780791886625},
     url = {https://asmedigitalcollection.asme.org/IMECE/proceedings/IMECE2022/86625/V001T01A007/1156737},
     doi = {10.1115/IMECE2022-94486},
     urldate = {2024-10-20},
     booktitle = {Volume 1: {Acoustics}, {Vibration}, and {Phononics}},
     publisher = {American Society of Mechanical Engineers},
     author = {Chen, Alvin and Kopsaftopoulos, Fotis and Mishra, Sandipan},
     month = oct,
     year = {2022},
     pages = {V001T01A007}
   }

2. IEEE/ASME

   A Learn-and-Control Strategy for Jet-Based Additive Manufacturing

   Uduak Inyang-Udoh, Alvin Chen, and Sandipan Mishra

   IEEE/ASME Transactions on Mechatronics, Aug 2022

   Bib

   @article{inyang-udoh_learn-and-control_2022,
     title = {A {Learn}-and-{Control} {Strategy} for {Jet}-{Based} {Additive} {Manufacturing}},
     volume = {27},
     copyright = {https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html},
     issn = {1083-4435, 1941-014X},
     url = {https://ieeexplore.ieee.org/document/9803861/},
     doi = {10.1109/TMECH.2022.3175949},
     number = {4},
     urldate = {2024-10-20},
     journal = {IEEE/ASME Transactions on Mechatronics},
     author = {Inyang-Udoh, Uduak and Chen, Alvin and Mishra, Sandipan},
     month = aug,
     year = {2022},
     pages = {1946--1954}
   }