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HPA-MPC: Hybrid Perception-Aware Nonlinear Model Predictive Control for Quadrotors with Suspended Loads

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arXiv 2024年

作者： Sarvaiya, Mrunal Li, Guanrui Loianno, Giuseppe New York University Tandon School of Engineering BrooklynNY11201 United States Worcester Polytechnic Institute Robotics Engineering WorcesterMA01609 United States

Quadrotors equipped with cable-suspended loads represent a versatile, low-cost, and energy efficient solution for aerial transportation, construction, and manipulation tasks. However, their real-world deployment is hindered by several challenges. The system is difficult to control because it is nonlinear, underactuated, involves hybrid dynamics due to slack-taut cable modes, and evolves on complex configuration spaces. Additionally, it is crucial to estimate the full state and the cable’s mode transitions in real-time using on-board sensors and computation. To address these challenges, we present a novel Hybrid Perception-Aware Nonlinear Model Predictive Control (HPA-MPC) control approach for quadrotors with suspended loads. Our method considers the complete hybrid system dynamics and includes a perception-aware cost to ensure the payload remains visible in the robot’s camera during navigation. Furthermore, the full state and hybrid dynamics’ transitions are estimated using onboard sensors. Experimental results demonstrate that our approach enables stable load tracking control, even during slack-taut transitions, and operates entirely onboard. The experiments also show that the perception-aware term effectively keeps the payload in the robot’s camera field of view when a human operator interacts with the load. © 2024, CC BY.

关键词： Predictive control systems

A Hybrid Model and Learning-Based Force Estimation Framework for Surgical Robots

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A Hybrid Model and Learning-Based Force Estimation Framework...

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Hao Yang Haoying Zhou Gregory S. Fischer Jie Ying Wu Department of Computer Science Vanderbilt University TN USA Department of Robotics Engineering Worcester Polytechnic Institute MA USA

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Haptic feedback to the surgeon during robotic surgery would enable safer and more immersive surgeries but estimating tissue interaction forces at the tips of robotically controlled surgical instruments has proven challenging. Few existing surgical robots can measure interaction forces directly and the additional sensor may limit the life of instruments. We present a hybrid model and learning-based framework for force estimation for the Patient Side Manipulators (PSM) of a da Vinci Research Kit (dVRK). The model-based component identifies the dynamic parameters of the robot and estimates free-space joint torque, while the learning-based component compensates for environmental factors, such as the additional torque caused by trocar interaction between the PSM instrument and the patient’s body wall. We evaluate our method in an abdominal phantom and achieve an error in force estimation of under 10% normalized root-mean-squared error. We show that by using a model-based method to perform dynamics identification, we reduce reliance on the training data covering the entire workspace. Although originally developed for the dVRK, the proposed method is a generalizable framework for other compliant surgical robots. The code is available at https://***/vu-maple-lab/dvrk_force_estimation.

关键词： Learning systems Torque Medical robotics Instruments Force Dynamics Estimation Surgery Robot sensing systems Trajectory optimization

Fluence Compensation for Linear Array-Based Photoacoustic Imaging System Using Geometrical Depth Mapping

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Fluence Compensation for Linear Array-Based Photoacoustic Im...

IEEE Symposium (IUS) Ultrasonics

作者： Yichuan Tang Wojciech G. Lesniak Martin G. Pomper Haichong K. Zhang Department of Robotics Engineering Worcester Polytechnic Institute Worcester MA USA Department of Radiology and Radiological Science Johns Hopkins University Baltimore MD USA Department of Biomedical Engineering Worcester Polytechnic Institute Worcester MA USA

Linear array-based photoacoustic (PA) imaging has been applied in tissue imaging. Due to optical attenuation, deep tissue receives less optical energy compared to surface tissue and thus generates weaker acoustic signals. To compensate for fluence change along light penetration direction, methods have been proposed based on optical transport model or statistical learning. However, these methods either require knowledge about optical properties of the tissue or involve extensive computation. In this work, we propose a fluence compensation method based on geometrical depth mapping derived from outer contour of the tissue. Our method requires no information on optical properties and does not involve extensive computation. Validations were carried out in simulation and ex vivo experiment. Results show that image intensities in deep tissue are enhanced, and image intensity distribution is more homogenous along the light penetration depth after fluence compensation. The proposed method proved effective for fluence compensation in imaging ex vivo tissue with homogeneous optical properties.

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A Tetherless Soft Robotic Wearable Haptic Human Machine Interface for Robot Teleoperation

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A Tetherless Soft Robotic Wearable Haptic Human Machine Inte...

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Shilpa Thakur Nathalia Diaz Armas Joseph Adegite Ritwik Pandey Joey Mead Pratap M. Rao Cagdas D. Onal Worcester Polytechnic Institute (WPI) Worcester MA USA Robotics Engineering Department University of Massachusetts Lowell Lowell MA USA Plastics Engineering Department Mechanical and Materials Engineering Department

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

This work describes the development, demonstration, and performance evaluation study of a wearable human machine interface for robotic teleoperation. We present a novel tetherless human machine interface in the form of a backpack, wearable 3D arm motion capture sensors, finger flexion sensors, and pneumatic haptic feedback muscles. The system is integrated in a complete teleoperation framework, enabling users to be immersed in a remote environment through virtual reality headgear, facilitating intuitive manipulation of an industrial articulated arm. The human machine interface samples the kinematic configuration of the user’s arm, hand, and fingers using multiple inertial measurement units and capacitive sensors respectively, and streams it to the teleoperation software stack. The gripping forces experienced at the robot’s end-effector are acquired using a custom three-dimensional Hall-effect magnetic sensor. The system simultaneously renders the kinesthetic and tactile feedback on the user’s fingers through custom designed pneumatically actuated soft robotic haptic muscles. The efficacy of the human machine interface and the teleoperation system was tested and evaluated by conducting user studies, which showed 31.4% faster teleoperation compared to a keypad controller, and 60% less gripping force utilized with haptics enabled. The findings of the study guided the design and prototype development of a printed electronics based stretchable sleeve and glove motion capture unit to improve the portability, ergonomics, and user experience of the human machine interface.

关键词： Human-machine systems Tactile sensors Virtual reality Soft robotics Muscles Three-dimensional printing Motion capture User experience Haptic interfaces Wearable sensors

Vision-based FDM Printing for Fabricating Airtight Soft Actuators

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arXiv 2023年

作者： Wu, Yijia Dai, Zilin Liu, Haotian Wang, Lehong Nemitz, Markus P. Department of Robotics Engineering Worcester Polytechnic Institute 100 Institute Road WorcesterMA01609 United States Department of Computer Science Worcester Polytechnic Institute 100 Institute Road WorcesterMA01609 United States Department of Mechanical and Materials Engineering Worcester Polytechnic Institute 100 Institute Road WorcesterMA01609 United States Department of Electrical and Computer Engineering Worcester Polytechnic Institute 100 Institute Road WorcesterMA01609 United States

Pneumatic soft robots are typically fabricated by molding, a manual fabrication process that requires skilled labor. Additive manufacturing has the potential to break this limitation and speed up the fabrication process but struggles with consistently producing high-quality prints. We propose a low-cost approach to improve the print quality of desktop fused deposition modeling by adding a webcam to the printer to monitor the printing process and detect and correct defects such as holes or gaps. We demonstrate that our approach improves the air-tightness of printed pneumatic actuators while reducing the need for fine-tuning printing parameters. Our approach presents a new option for robustly fabricating airtight, soft robotic actuators. Copyright © 2023, The Authors. All rights reserved.

关键词： Pneumatic actuators

Loss Distillation via Gradient Matching for Point Cloud Completion with Weighted Chamfer Distance

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arXiv 2024年

作者： Lin, Fangzhou Liu, Haotian Zhou, Haoying Hou, Songlin Yamada, Kazunori D. Fischer, Gregory S. Li, Yanhua Zhang, Haichong K. Zhang, Ziming Department of Robotics Engineering Worcester Polytechnic Institute WorcesterMA01609 United States Dell Technologies HopkintonMA01748 United States Graduate School of Information Sciences Tohoku University Sendai980-8579 Japan Department of Computer Science Worcester Polytechnic Institute WorcesterMA01609 United States Department of Electrical & Computer Engineering Worcester Polytechnic Institute WorcesterMA01609 United States

3D point clouds enhanced the robot’s ability to perceive the geometrical information of the environments, making it possible for many downstream tasks such as grasp pose detection and scene understanding. The performance of these tasks, though, heavily relies on the quality of data input, as incomplete can lead to poor results and failure cases. Recent training loss functions designed for deep learning-based point cloud completion, such as Chamfer distance (CD) and its variants (e.g. HyperCD [1]), imply a good gradient weighting scheme can significantly boost performance. However, these CD-based loss functions usually require data-related parameter tuning, which can be time-consuming for data-extensive tasks. To address this issue, we aim to find a family of weighted training losses (weighted CD) that requires no parameter tuning. To this end, we propose a search scheme, Loss Distillation via Gradient Matching, to find good candidate loss functions by mimicking the learning behavior in backpropagation between HyperCD and weighted CD. Once this is done, we propose a novel bilevel optimization formula to train the backbone network based on the weighted CD loss. We observe that: (1) with proper weighted functions, the weighted CD can always achieve similar performance to HyperCD, and (2) the Landau weighted CD, namely Landau CD, can outperform HyperCD for point cloud completion and lead to new state-of-the-art results on several benchmark datasets. Our demo code is available at https://***/Zhang-VISLab/ IROS2024-LossDistillationWeightedCD. © 2024, CC0.

关键词： Deep learning

Motor Unit Action Potential Based Classification of Hand and Arm Motions

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Motor Unit Action Potential Based Classification of Hand and...

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Michael D. Twardowski Michael D. Chan Zhi Li Gianluca De Luca Joshua C. Kline John P. Chiodini Delsys & Altec Inc. Natick MA USA Human Inspired Robotics Laboratory Worcester Polytechnic Institute Worcester MA USA The MITRE Corporation Bedford MA USA Embodied Inc. Department of Robotics Engineering Human Inspired Robotics Laboratory Worcester Polytechnic Institute Worcester MA USA

While motion classification architectures have improved in accuracy and robustness in recent years, computationally expensive approaches and sophisticated hardware dependencies limit their real-world applicability. To overcome these challenges, we have designed a lightweight, realtime architecture for classifying motions of the arm & hand using features derived from motor unit action potentials within surface Electromyographic (sEMG) signals, rather than which provide direct interrogation of underlying muscle activation patterns. We tested the architecture on 6 motions performed dynamically across a range of muscle contraction intensities achieving median classification accuracies ranging from 91.3% to 93.3 % and an average processing time of approximately 40 ms across three different classifiers. Taken together, our findings demonstrate potential robustness of motor unit based neural interfaces for motion classification tasks.

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Development and Evaluation of a Markerless 6 DOF Pose Tracking Method for a Suture Needle from a Robotic Endoscope

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Journal of Medical robotics Research 2023年第3-4期8卷

作者： Jiang, Yiwei Zhou, Haoying Fischer, Gregory S. Department of Robotics Engineering Worcester Polytechnic Institute 100 Institute Rd Worcester 01609 MA United States

Remarkable progress has been made in the field of robot-assisted surgery in recent years, particularly in the area of surgical task automation, though many challenges and opportunities still exist. Among these topics, the detection and tracking of surgical tools play a pivotal role in enabling autonomous systems to plan and execute procedures effectively. For instance, accurate estimation of a needle's position and posture is essential for surgical systems to grasp the needle and perform suturing tasks autonomously. In this paper, we developed image-based methods for markerless 6 degrees of freedom (DOF) suture needle pose estimation using keypoint detection technique based on Deep Learning and Point-to-point Registration, we also leveraged multi-viewpoint from a robotic endoscope to enhance the accuracy. The data collection and annotation process was automated by utilizing a simulated environment, enabling us to create a dataset with 3446 evenly distributed needle samples across a suturing phantom space for training and to demonstrate more convincing and unbiased performance results. We also investigated the impact of training set size on the keypoint detection accuracy. Our implemented pipeline that takes a single RGB image achieved a median position error of 1.4 mm and a median orientation error of 2.9, while our multi-viewpoint method was able to further reduce the random errors. © 2023 World Scientific Publishing Company.

关键词： deep learning keypoint detection markerless tracking Surgical robotics

A Language Anchor-Guided Method for Robust Noisy Domain Generalization

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arXiv 2025年

作者： Dai, Zilin Wang, Lehong Lin, Fangzhou Wang, Yidong Li, Zhigang Yamada, Kazunori D. Zhang, Ziming Lu, Wang Department of Computer Science Worcester Polytechnic Institute WorcesterMA01609 United States Department of Electrical Engineering Worcester Polytechnic Institute WorcesterMA01609 United States Department of Robotics Engineering Worcester Polytechnic Institute WorcesterMA01609 United States Carnegie Mellon University PittsburghPA15213 United States Tohoku University Sendai980-8572 Japan Tsinghua University Beijing100190 China Peking University Beijing100871 China

Real-world machine learning applications are often hindered by two critical challenges: distribution shift and label noise. Networks inherently tend to overfit to redundant, uninformative features present in the training distribution, which undermines their ability to generalize effectively to the target domain’s distribution. The presence of noisy data further exacerbates this issue by inducing additional overfitting to noise, causing existing domain generalization methods to fail in effectively distinguishing invariant features from spurious ones. To address these challenges, we propose Anchor Alignment and Adaptive Weighting (A3W), a novel algorithm based on sample reweighting guided by natural language processing (NLP) anchors that seeks to extract representative features. In particular, A3W leverages semantic representations derived from natural language models to serve as a source of domain-invariant prior knowledge. We also introduce a weighted loss function that dynamically adjusts the contribution of each sample based on its distance to the corresponding NLP anchor, thereby improving the model’s resilience to noisy labels. Extensive experiments on benchmark datasets demonstrate that A3W outperforms state-of-the-art domain generalization methods, yielding significant improvements in both accuracy and robustness across various datasets and noise levels. © 2025, CC BY.

关键词： Adversarial machine learning