Scholarly Works, Mechanical Engineering

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https://hdl.handle.net/10919/23262

Research articles, presentations, and other scholarship

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A Roadmap to Holographic Focused Ultrasound Approaches to Generate Thermal Patterns
Cengiz, Ceren; Eger, Zekeriya Ender; Acar, Pinar; Legon, Wynn; Shahab, Shima (Wiley, 2026-04)
In therapeutic focused ultrasound (FUS), such as thermal ablation and hyperthermia, effective acousto-thermal manipulation requires precise targeting of complex geometries, sound wave propagation through irregular structures and selective focusing at specific depths. Acoustic holographic lenses (AHLs) provide a distinctive capability to shape acoustic fields into precise, complex and multifocal FUS-thermal patterns. Acknowledging the underexplored potential of AHLs in shaping ultrasound-induced heating patterns, this study introduces a roadmap for acousto-thermal modeling in the design of AHLs. Three primary modeling approaches are studied and contrasted using four distinct shape groups for the imposed target field. They include pressure-based (BSC-TR and ITER-TR), temperature-based (IHTO-TR), and machine learning (ML)-based (GaN and Feat-GAN) methods. Novel metrics including image quality, thermal efficiency, control, and computational time are introduced, providing each method’s strengths and weaknesses. The importance of evaluating target pattern complexity, thermal and pressure requirements, and computational resources is highlighted for selecting the appropriate methods. For lightly heterogeneous media and targets with lower pattern complexity, BSC-TR combined with error diffusion algorithms provides an effective solution. As pattern complexity increases, ITERTR becomes more suitable, enabling optimization through iterative forward and backward propagations controlled by different error metrics. IHTO-TR is recommended for highly heterogeneous media, particularly in applications requiring thermal control and precise heat deposition. GaN is ideal for rapid solutions that account for acousto-thermal effects, especially when model parameters and boundary conditions remain constant. In contrast, Feat-GaN is effective for moderately complex shape groups and applications where model parameters must be adjusted.
Corrosion Behavior of SS316H in Non-isothermal Fluoride Fuel Salt
Lee, Woohyuk; Leong, Amanda; Zhang, Jinsuo (TMS 2026, 2026-03-17)
This study investigates the impact of thermally driven mass transfer on the corrosion behavior of SS316H in molten NaF-BeF₂-UF₄-ZrF₄ salt using a static, non-isothermal system. A vertical temperature gradient from 717 °C (bottom) to 634 °C (top) was established over 6.5 inches without a forced salt circulation. SS316H samples were placed at heights of 0, 3, and 6.5 inches and exposed for 5, 10, and 15 days. Despite the absence of forced convection, significant mass transfer effects were observed. The hottest sample exhibited the greatest chromium depletion, while cooler regions showed less. Metallic deposition occurred on colder samples and in the surrounding salt, indicating directional transport driven by the thermal gradient. These results demonstrate that thermal gradients alone can induce notable corrosion-related mass transfer, challenging the conventional reliance on thermal convection loops. This highlights the need to consider temperature-driven mass transport in evaluating materials for molten salt reactor environments.
Metal redox control in molten NaF-BeF2-UF4-ZrF4 salt for corrosion mitigation
Lee, Woohyuk; Leong, Amanda; Park, Jaewoo; Zhang, Jinsuo (2024-10-11)
Real-Time Hand Pose Tracking using 6-Axis IMUs
Sarker, Anik; Kou, Ziyi; Ristani, Ergys; Guan, Li; Niehues, Taylor (ACM, 2026-03-16)
We introduce a real-time system for tracking hand pose using 6- axis inertial measurement units (IMUs) without requiring magnetometers or external sensors. Accurate hand pose tracking with only 6-axis IMUs is known to be fundamentally challenging due to the absence of a shared heading reference, leading to severe drift and inter-sensor misalignment. To overcome these limitations, we propose a hybrid method that combines a learning-based pose estimation approach followed by a late-stage Extended Kalman Filter (EKF). The learning-based model estimates noisy yet reasonable hand poses and is trained with drift-insensitive features like gravity vectors and wrist-relative gyroscope signals. On the other hand the EKF can appropriately filter the noise from pose estimates leading to robust tracking. Evaluated on a 12-hour dataset spanning 23 interaction tasks across 10 participants, our system improves joint angle accuracy by 40% over an EKF-only baseline and by 18% over a learning-only approach, achieving a mean joint error below 10°. The resulting framework enables real-time hand tracking invariant to magnetic perturbations, occlusion, or lighting changes, and is well suited for robotics, human–robot interaction (HRI), and human-computer interaction (HCI) applications.
T-Type Labyrinth Seals Dynamic Response Evaluation Using Computational Analysis
Ashraf, Muhammad Mubashar; Untaroiu, Alexandrina (ASME International, 2026-03-06)
Effective sealing in rotating machinery is fundamental to maintaining efficiency and ensuring stable operation. Secondary leakage between high and low-pressure regions not only reduces performance but can also introduce destabilizing aerodynamic forces. Among annular gas seal technologies such as brush, hole-pattern, and honeycomb designs, labyrinth seals remain the most widely used because they are mechanically simple, reliable, and cost-effective. Recently, a modified T-type labyrinth seal has been introduced, demonstrating improved flow control and reduced flow-induced excitations compared to conventional straight-through configurations. The distinguishing feature of the T-type design is its T-shaped tooth geometry, which modifies the internal flow structure and enhances the inward radial forces associated with the Lomakin effect. This change in flow physics directly influences both leakage characteristics and rotordynamic behavior. Seal tip clearance plays a pivotal role. A smaller clearance generally reduces leakage but can alter aerodynamic stiffness and damping, thereby affecting rotor stability. Determining an appropriate clearance, therefore, requires more than a simple comparison at fixed geometry; it demands a structured parametric evaluation that captures the coupled aerodynamic and rotordynamic effects. Previous investigations have demonstrated leakage reductions of 23.6–25.3% for T-type labyrinth seals relative to straight-through designs, with axial length and tip clearance held constant. These findings point to clear performance advantages but leave open the question of optimal geometric tuning. Building on this, the present study conducts a sensitivity analysis using a design of experiments (DOE) framework coupled with steady-state computational fluid dynamics (CFD). The DOE approach enables systematic exploration of the clearance parameter space and quantifies the influence of the clearance parameter on leakage performance. In parallel, equivalent rotordynamic force coefficients are extracted from the CFD solutions to evaluate seal-induced stiffness and damping and to assess stability trends. To further establish practical relevance, the seal performance is examined across a range of pressure ratios and rotational speeds representative of aero-engine operating conditions. The results provide a coherent picture of how tip clearance governs both leakage and rotordynamic response in T-type labyrinth seals. Beyond confirming their leakage advantage, the study offers quantitative guidance for clearance selection and contributes to the broader effort to integrate aerodynamic performance and stability considerations into advanced seal design.
Homework Software Access Code Replacements and Strategies: A Roundtable Discussion
Walz, Anita R.; Russell, J. Morgan; Hart, Heath David; Lord, James K.; Grohs, Jacob R. (2025-02-13)
Homework software systems save time for instructors, particularly in large-enrollment courses. However, student-paid access codes have limited functionality and are expensive--between $50-150 per course per semester for the 30% of courses which require them. Functionality affects learning and costs disproportionally affect historically underserved students and student academic performance. Virginia Tech’s Open Education Initiative is working to establish a variety of options for instructors. Join this Roundtable to discuss with instructors from STEM and non-STEM disciplines who use university approved, no-fee-to-students alternatives including: WeBWorK, PressbooksResults, peer-reviewed test banks for LMS import, and problem set environment for engineering. Downloadable files include slides and submitted proposal.
Design, Manufacturing, and Analysis of a Carbon Fiber Reinforced Polymer Crash Box
Engul, Mehmet; Demir, Serdar; Ersoy, Nuri (MDPI, 2026-02-06)
This paper presents a novel carbon fiber reinforced polymer (CFRP) crash box design, incorporating numerical analysis and manufacturing aspects. Within the design and analysis phases, a novel numerical methodology is employed to mitigate computational costs in estimating specific energy absorption (SEA). The proposed approach involves a reduction in ply interfaces and modification of pertinent material properties to optimize energy dissipation, achieving more than 50% reduction in simulation time. This methodology is applied to the design of a composite crash box made of unidirectional (UD) carbon/epoxy prepregs, resulting in a new geometry: sun-like shape featuring four sinusoidal arms connected to a central circular core. Subsequent manufacturing and testing reveal a SEA value of 79.46 J/g for designed geometry, surpassing metallic counterparts by a factor of 3 to 4. Furthermore, this study conducts a comparative analysis of energy absorption performance between unidirectional and woven fabric prepregs for the same geometry. Utilizing carbon/epoxy woven fabric (WF) prepregs further enhances the SEA to 89.26 J/g. Finally, the application of edge tapering to the crash box structure is shown to eliminate initial peak loads, thereby preventing excessive deceleration.
RAPID Enabled Physics-Based Neural Networks for Predicting 3-D Fission Distributions in JSI TRIGA Reactor
Franck, Timothy; Haghighat, Alireza; Snoj, Luka (2026-04-23)
The current methods for high-fidelity simulations of nuclear reactor systems are complex and computationally expensive. To reduce computation time, artificial intelligence (AI) and machine learning (ML) are being considered. Despite showing promise for solving various neutronics problems, the limited availability of high-fidelity data constrains ML applications to simpler problems or systems. This paper utilizes the RAPID code system for its effectiveness at rapidly producing large quantities of high-fidelity data. This has enabled the development of physics-based neural networks (NN) to predict 3-D fission distributions as a function of CR positions for the JSI TRIGA Mark-II research reactor. We developed a NN architecture that contains two hidden layers, 4400 neurons per hidden layer, with Leaky ReLU activation functions. This model was capable of predicting more than 99% of the fission values in the fuel elements within ±0.5% rel. diff. The model also predicted about 98% of the fission values in the fuel followers within ±10% rel. diff. It was determined that errors in the fuel follower predictions did not significantly impact calculated power peaking factors, which fell within the range of -0.39% to 0.91% rel. diff. Hyperparameter tuning and its effect on model performance is also discussed, with some comparisons to simpler ML models developed in a previous study.
Next-Generation Research Reactors: Novel Virginia Research and Education Reactor (VA-RER)
Haghighat, Alireza; Franck, Timothy; Seidulla, Beksultan; Mascolino, Valerio (2026-04-21)
The Virginia Research and Education Reactor (VA-RER) is a next-generation research reactor concept engineered to meet the urgent national need for modern, flexible, and AI-enabled modeling, design, operation and monitoring for diagnostics/prognostics. Built on the patented Test and Education Microreactor (TEM) architecture, the design integrates a central irradiation cavity, a neutron-spectrum-tailoring buffer zone, and a circular TRIGA-fuel lattice to support advanced applications in materials testing, isotope production, reactor physics research, and workforce development. The VA-RER is envisioned as a dual-reactor system, a zero-power unit dedicated to training and hands-on education, paired with a 5 to 10 MWth reactor enabling high-flux irradiation experiments and validation of physics-based AI-driven digital-twin and autonomous monitoring technologies. This paper presents preliminary neutronics analyses using the OpenMC Monte Carlo code system with ENDF/B-VIII.0 nuclear data to evaluate the reactivity behavior of reactor configurations with irradiation cavity radii ranging from 6.25 cm to 100 cm. Five select core configurations are analyzed in this paper. The resulting eigenvalues range from 1.01581 to 1.06814, with reactivity gains diminishing for larger annular radii due to increased neutron leakage. Notably, an optimal moderator-to-fuel ratio emerges near a 25 cm radius, where the eigenvalue increases even with fewer total fuel rods. These findings provide early design guidance for maximizing irradiation volume while maintaining favorable neutronic performance, supporting ongoing development of a transformative research reactor for next-generation nuclear science and engineering.
Feasibility Test for Determination of Dosimeter Response for the Watts Bar Unit 1 Reactor Extended Belt-line Region Using the DRF Method
Friedman, Cole N.; Haghighat, Alireza (2026-04-21)
New methods for solving 3-D reactor pressure vessel (RPV) dosimetry problems are emerging as the operating lifetimes of nuclear power reactors continue to increase. Accurately quantifying neutron fluence and its associated damage in RPV regions beyond the traditional beltline has become increasingly important. This work applies the detector response function (DRF) methodology to a 3-D RPV dosimetry problem using the TVA Watts Bar Unit 1 Benchmark as the reference model. A test problem consisting of four axial fuel segments is used to evaluate the effectiveness of the DRF approach for pressure vessel fluence calculations. DRF predictions are compared against reference MCNP Monte Carlo results. The good agreement between the two methods demonstrates the soundness of the DRF approach; however, additional analysis and optimization are required before extending the method to a full-core DRF solution.
Bubble-burst-induced Puddle Jumping and Jet Printing
Huang, Wenge; Lori, Mohammad Shamsodini; Yang, Anchenyi; Zhuang, Kai; Cheng, Yuanhao; Chen, Mojun; Sun, Chao; Ming, Tingzhen; Cui, Huachen; Cheng, Jiangtao (Springer, 2026-02-26)
Self-propelled droplet jumping has widespread applications in surface cleaning, condensation heat transfer, hydrogen production, and triboelectric nanogenerator due to the passive yet effective cross-interface transfer of mass, momentum, energy and charge, whose rates generally increase with droplet size. However, as droplet size increases, gravity inevitably impedes droplet’s mobility, imposing a capillary length constraint of 2.7mm for water droplet, beyond which self-propelled jumping remains a persistent challenge. Here, we report passive jumping of water puddle in the unprecedented centimeter scale from a superhydrophobic surface through the burst of an entrained bubble, breaking the capillary length limitation for droplet jumping. By virtue of direct and localized impact at droplet base, the bubble-burst-induced capillary waves play a paradigm-shifting role in shortening the impact duration, depressing droplet spreading, and facilitating momentum transfer. With >90% conversion to droplet jumping momentum, the impacting momentum of capillary waves scales linearly while droplet jumping height scales quadratically with bubble radius. Through studying the synergistic interplay between bubble bursting, fluidic jetting and droplet jumping, this work reveals a previously unexplored mechanism of capillary wave impact in fluid-structure interactions and offers a promising strategy for droplet actuations and the directional printing of particles in additive manufacturing.
Confinement in fibrous environments positions and orients mitotic spindles
Sarkar, Apurba; Jana, Aniket; Agashe, Atharva; Wang, Ji; Kapania, Rakesh; Gov, Nir S.; DeLuca, Jennifer G.; Paul, Raja; Nain, Amrinder (Oxford University Press, 2025-07)
Accurate positioning of the mitotic spindle within the rounded cell body is critical to physiological maintenance. Mitotic cells encounter confinement from neighboring cells or the extracellular matrix (ECM), which can cause rotation of mitotic spindles and tilting of the metaphase plate (MP). To understand the effect of confinement on mitosis by fibers (ECM confinement), we use flexible ECM-mimicking nanofibers that allow natural rounding of the cell body while confining it to differing levels. Rounded mitotic bodies are anchored in place by actin retraction fibers (RFs) originating from adhesions on fibers. We discover that the extent of confinement influences RF organization in 3D, forming triangular and band-like patterns on the cell cortex under low and high confinement, respectively. Our mechanistic analysis reveals that the patterning of RFs on the cell cortex is the primary driver of the MP rotation. A stochastic Monte Carlo simulation of the centrosome, chromosome, membrane interactions, and 3D arrangement of RFs recovers MP tilting trends observed experimentally. Under high ECM confinement, the fibers can mechanically pinch the cortex, causing the MP to have localized deformations at contact sites with fibers. Interestingly, high ECM confinement leads to low and high MP tilts, which we mechanistically show to depend upon the extent of cortical deformation, RF patterning, and MP position. We identify that cortical deformation and RFs work in tandem to limit MP tilt, while asymmetric positioning of MP leads to high tilts. Overall, we provide fundamental insights into how mitosis may proceed in ECM-confining microenvironments in vivo.
Volumetric imaging of the 3D orientation of cellular structures with a polarized fluorescence light-sheet microscope
Chandler, Talon; Guo, Min; Su, Yijun; Chen, Jiji; Wu, Yicong; Liu, Junyu; Agashe, Atharva; Fischer, Robert S.; Mehta, Shalin B.; Kumar, Abhishek; Baskin, Tobias I.; Jaumouille, Valentin; Liu, Huafeng; Swaminathan, Vinay; Nain, Amrinder; Oldenbourg, Rudolf; La Riviere, Patrick J.; Shroff, Hari (National Academy of Sciences, 2025-02-21)
Polarized fluorescence microscopy is a valuable tool for measuring molecular orientations in biological samples, but techniques for recovering three-dimensional orientations and positions of fluorescent ensembles are limited. We report a polarized dual-view light-sheet system for determining the diffraction-limited three-dimensional distribution of the orientations and positions of ensembles of fluorescent dipoles that label biological structures. We share a set of visualization, histogram, and profiling tools for interpreting these positions and orientations. We model the distributions based on the polarization-dependent efficiency of excitation and detection of emitted fluorescence, using coarse-grained representations we call orientation distribution functions (ODFs). We apply ODFs to create physics-informed models of image formation with spatio-angular point-spread and transfer functions. We use theory and experiment to conclude that light-sheet tilting is a necessary part of our design for recovering all three-dimensional orientations. We use our system to extend known two-dimensional results to three dimensions in FM1-43-labeled giant unilamellar vesicles, fast-scarlet-labeled cellulose in xylem cells, and phalloidin-labeled actin in U2OS cells. Additionally, we observe phalloidin-labeled actin in mouse fibroblasts grown on grids of labeled nanowires and identify correlations between local actin alignment and global cell-scale orientation, indicating cellular coordination across length scales.
Mechanical cues guide the formation and patterning of 3D spheroids in fibrous environments
Sharma, Sharan; Agashe, Atharva; Hill, Jennifer C.; Ganguly, Keya; Sharma, Puja; Richards, Tara D.; Huang, Weijian; Kaczorowski, David J.; Sanchez, Pablo G.; Kapania, Rakesh; Phillippi, Julie A.; Nain, Amrinder (Oxford University Press, 2025-09)
Multicellular spheroids have shown great promise in 3D biology. Many techniques exist to form spheroids, but how cells take mechanical advantage of native fibrous extracellular matrix (ECM) to form spheroids remains unknown. Here, we identify the role of fiber diameter, architecture, and cell contractility on spheroids’ spontaneous formation and growth in ECM-mimicking fiber networks. We show that matrix deformability revealed through force measurements on aligned fiber networks promotes spheroid formation independent of fiber diameter. At the same time, larger-diameter crosshatched networks of low deformability abrogate spheroid formation. Thus, designing fiber networks of varying diameters and architectures allows spatial patterning of spheroids and monolayers simultaneously. Forces quantified during spheroid formation revealed the contractile role of Rho-associated protein kinase in spheroid formation and maintenance. Interestingly, we observed spheroid–spheroid and multiple spheroid mergers initiated by cell exchanges to form cellular bridges connecting the two spheroids. Unexpectedly, we found large pericyte spheroids contract rhythmically. Transcriptomic analysis revealed striking changes in cell–cell, cell–matrix, and mechanosensing gene expression profiles concordant with spheroid assembly on fiber networks. Overall, we ascertained that contractility and network deformability work together to spontaneously form and pattern 3D spheroids, potentially connecting in vivo matrix biology with developmental, disease, and regenerative biology.
Cell mechanics, environmental geometry, and cell polarity control cell-cell collision outcomes
Luo, Yongtian; Nain, Amrinder; Camley, Brian A. (Royal Society of Chemistry, 2025-09-18)
Interactions between crawling cells, which are essential for many biological processes, can be quantified by measuring cell-cell collisions. Conventionally, experiments of cell-cell collisions are conducted on two-dimensional flat substrates, where colliding cells repolarize and move away upon contact with one another in “contact inhibition of locomotion” (CIL). Inspired by recent experiments that show cells on suspended nanofibers have qualitatively different CIL behaviors than those on flat substrates, we develop a phase field model of cell motility and two-cell collisions in fiber geometries. Our model includes cell-cell and cell-fiber adhesion, and a simple positive feedback mechanism of cell polarity. We focus on cell collisions on two parallel fibers, finding that larger cell deformability (lower membrane tension), larger positive feedback of polarization, and larger fiber spacing promote more occurrences of cells walking past one another. We can capture this behavior using a simple linear stability analysis on the cell-cell interface upon collision.
Rapid Design and Fabrication of Body Conformable Surfaces with Kirigami Cutting and Machine Learning
Bali, Jyotshna; Li, Jinyang; Chen, Jie; Li, Suyi (Wiley, 2026-02-10)
By integrating the principles of kirigami cutting and data-driven modeling, this study aims to develop a personalized, rapid, and low-cost design and fabrication pipeline for creating body-conformable surfaces around the knee joint. The process begins with 3D scanning of the anterior knee surface of human subjects, followed by extracting the corresponding skin deformation between two joint angles in terms of longitudinal strain and Poisson's ratio. In parallel, a machine learning model is constructed using extensive simulation data from experimentally calibrated finite element analysis. This model employs Gaussian Process (GP) regression to relate kirigami cut lengths to the resulting longitudinal strain and Poisson's ratio. With an R² score of 0.996, GP regression outperforms other models in predicting kirigami's large deformations. Finally, an inverse design approach based on the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is used to generate kirigami patch designs that replicate the in-plane skin deformation observed from the knee scans. This pipeline was applied to three human subjects, and the resulting kirigami knee patches were fabricated using rapid laser cutting, requiring less than a business day from knee scanning to kirigami patch delivery. The low-cost, personalized kirigami patches successfully conformed to over 75% of the skin area across all subjects. The kirigami-inspired, machine-learning-driven design and fabrication pipeline presents a balanced trade-off between conformability performance and cost for personalizing wearables, thus establishing a foundation for a wide range of new functional devices.
Impact of Biomimetic Pinna Shape Variation on Clutter Echoes: A Machine Learning Approach
Eshera, Ibrahim; Lagad, Sanmeel; Mueller, Rolf (Wiley-VCH, 2025-10-05)
Bats species navigating dense vegetation based on biosonar must obtain sensory information about their environments from “clutter echoes”, i.e., echoes that are superpositions from many unresolved reflecting facets (e.g., leaves) with unpredictable individual waveforms. Prior results suggested that pinna deformations can aid performance in sensing tasks based on deterministic echo patterns, raising the question of whether varying pinna shapes can also have functional significance for biosonar tasks performed on clutter echoes. To test this hypothesis, this work investigates whether different pinna shapes have a consistent effect on clutter echoes despite the random nature of these signals. This is accomplished using a dedicated laboratory setup that produces large amounts of uncorrelated clutter echo data by agitating artificial foliage with fans between echo recordings. Deep learning then identifies the pinna shape that receives a given clutter echo using a data-driven classification approach that learns features directly from echoes without explicit physical modeling. A ResNet-50 achieves 97.8% overall classification accuracy for the pinna shape conformations (true-positive identifications 91.67–100%), whereas a two-dimensional convolutional neural network operating on echo spectrograms still achieves 90% accuracy. These findings demonstrate that even small pinna deformations can impart consistent effects on the clutter echoes.
Deep Learning Methods for Assessing Time-Variant Nonlinear Signatures in Clutter Echoes
Eshera, Ibrahim; Duggal, Gaurav; Müller, Rolf (Wiley, 2026-02-10)
The biosonar systems of bats in families of horseshoe and Old-World leaf-nosed bats include peripheral dynamics where the outer ears undergo fast rotations and deformations during echo reception. These motions impart time-variant linear and nonlinear effects on the received echoes. In the present study, we have investigated whether such time-variant effects create discriminable and reliable signatures in clutter, i.e., echoes that are created by a superposition of reflections from multiple, unresolved scatterers. We have used a laboratory setup with artificial foliage that was agitated by fans to create large data sets of clutter echoes. These echoes were triggered by pulses with different time–frequency signatures (constant-frequency, frequency-modulated, and a compound of the two) and received by flexible biomimetic pinna that was actuated via strings to create two different motion shapes at five different motion speeds. Different deep-learning architectures (ResNets, transformers, and a 2D convolutional neural network) were tested for their ability to classify the different motions based on single clutter echoes. The achieved performances (up to 97% overall correct classifications) demonstrated that the time-variant signatures in the clutter echoes could form a reliable substrate for the encoding of sensory information that may provide functional advantages for navigating complex natural environments.
A Nonlinear MPC Framework for Loco-Manipulation of Quadrupedal Robots With Non-Negligible Manipulator Dynamics
Sambhus, Ruturaj S.; Mehta, Kapi Ketan; Sadeghi, Ali MirMohammad; Imran, Basit Muhammad; Kim, Jeeseop; Chunawala, Taizoon; Pastore, Vittorio; Vijayan, Sujith; Hamed, Kaveh Akbari (IEEE, 2026-01)
Model predictive control (MPC) with reduced-order template models has proven effective for dynamic legged locomotion, but loco-manipulation introduces additional complexity requiring efficient algorithms for high-degree-of-freedom (DoF) systems. This letter presents a computationally efficient nonlinear MPC (NMPC) framework tailored for loco-manipulation tasks of quadrupedal robots equipped with robotic manipulators whose dynamics are non-negligible relative to those of the quadruped. The proposed framework adopts a decomposition strategy that couples locomotion template models—such as the single rigid body model—with a full-order dynamic model of the robotic manipulator for torque-level control. This decomposition enables efficient real-time solution of the NMPC problem in a receding horizon fashion. The optimal state and input trajectories generated by the NMPC for locomotion are tracked by a low-level nonlinear whole-body controller, while the optimal torque commands for the manipulator are directly applied. The layered control architecture is validated through extensive numerical simulations and hardware experiments on a 15-kg Go2 quadrupedal robot augmented with a 4.4-kg 4-DoF Kinova arm. Given that the Kinova arm dynamics are non-negligible relative to the Go2 base, the proposed NMPC framework demonstrates robust stability in performing diverse loco-manipulation tasks, effectively handling external disturbances, payload variations, and uneven terrain.
An Open-Source Framework to Design, Tune, and Fly Nonlinear Control Systems for Autonomous UAVs
Gramuglia, Mattia; Kumar, Giri Mugundan; Orlando, Giorgio A.; L’Afflitto, Andrea (American Institute of Aeronautics and Astronautics, 2025-01)
This paper introduces a freeware open-source software ecosystem to design, tune, and test control systems for autonomous vertical take-off and landing (VTOL) multi-rotor uncrewed aerial vehicles (UAVs) such as quadcopters and quad-biplanes. This environment comprises C++-coded flight stacks with a suite of control systems, a high-fidelity simulator that allows model-in-the-loop and hardware-in-the-loop tests, computer-aided design (CAD) models of UAVs, and a website for a broad overview of this project. This ecosystem aims to serve as a common platform for the aerospace control community and ease comparative analyses for control design techniques produced by multiple research groups.

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