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

作者： Achyuthan Unni Krishnan Tsung-Chi Lin Zhi Li Robotics Engineering Worcester Polytechnic Institute Worcester MA USA

When humans control or supervise remote robot manipulation, augmented reality (AR) visual cues overlaid on the remote camera video stream can effectively enhance human's remote perception of task and robot states, and comprehension of the robot autonomy's capability and intent. In this work, we conducted a user study (N=18) to investigate: (RQ1) what AR cues humans prefer when controlling the robot with various levels of autonomy, and (RQ2) whether this preference can be influenced by the way humans learn to use the interface. We provided AR visual cues of various types (e.g., motion guidance, obstacle indicator, target hint, autonomy activation and intent) to assist humans to pick and place an object around an obstacle on a counter workspace. We found that: 1) Participants prefer different types of AR cues based on the level of robot autonomy; 2) The AR cues the participants prefer to use after hands-on robot operation converged to the recommendation of experienced users, and may largely differ from their initial selection based on video instruction.

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Decentralized Multi-Agent Reinforcement Learning with Global State Prediction

Decentralized Multi-Agent Reinforcement Learning with Global...

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

作者： Joshua Bloom Pranjal Paliwal Apratim Mukherjee Carlo Pinciroli Robotics Engineering Worcester Polytechnic Institute Worcester MA USA

Deep reinforcement learning (DRL) has seen re-markable success in the control of single robots. However, applying DRL to robot swarms presents significant challenges. A critical challenge is non-stationarity, which occurs when two or more robots update individual or shared policies concurrently, thereby engaging in an interdependent training process with no guarantees of convergence. Circumventing non-stationarity typically involves training the robots with global information about other agents' states and/or actions. In contrast, in this paper we explore how to remove the need for global information. We pose our problem as a Partially Observable Markov Decision Process, due to the absence of global knowledge on other agents. Using collective transport as a testbed scenario, we study two approaches to multi-agent training. In the first, the robots exchange no messages, and are trained to rely on implicit communication through push-and-pull on the object to transport. In the second approach, we introduce Global State Prediction (GSP), a network trained to form a belief over the swarm as a whole and predict its future states. We provide a comprehensive study over four well-known deep reinforcement learning algorithms in environments with obstacles, measuring performance as the successful transport of the object to a goal location within a desired time-frame. Through an ablation study, we show that including GSP boosts performance and increases robustness when compared with methods that use global knowledge.

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Design and Evaluation of Bidirectional Continuous Rotation and Variable Curvature Needle Steering Algorithm

Design and Evaluation of Bidirectional Continuous Rotation a...

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

作者： Farid Tavakkolmoghaddam Charles Bales Yang Wang Zhanyue Zhao Gregory S. Fischer Robotics Engineering Department Worcester Polytechnic Institute Worcester MA

The success rate of robotic-assisted needle-guided interventions for applications such as tissue biopsy and targeted drug delivery relies heavily on the accuracy of the needle placement. Tissue shift and needle tip deflection due to needle-tissue interaction are some factors that can adversely affect the outcome of these procedures. In this paper, we present a novel algorithm for robotically-steered bevel tip needles that provides variable needle curvatures by continuously controlling the rotation speed of the needle in a bidirectional manner. Our algorithm is an extension of the Continuous Rotation and Variable Curvature (CURV) algorithm and extends its use with wired sensorized needles. Additionally, we present algorithms for the implementation of our proposed method for closed-loop needle steering in robotic systems with image or sensor feedback. To validate our approach, we perform two benchtop needle insertion experiments in a gelatin phantom and ex vivo tissue. In the first experiment, we demonstrate the capability of our proposed algorithm in achieving variable curvatures and compare it with the CURV algorithm and our simulation results. The second experiment studies the effect of the unidirectional and bidirectional needle steering on the tissue wind-up using a novel force collection setup. Our results highlight the capability of the proposed algorithm in achieving variable curvature profiles and suggest a potential advantage compared to the original method in terms of reducing the imbalanced forces sensed at the load cell due to the needle-tissue friction buildup.

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Design and Testing of a Multi-Module, Tetherless, Soft Robotic Eel

Design and Testing of a Multi-Module, Tetherless, Soft Robot...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Robin Hall Gabriel Espinosa Shou-Shan Chiang Cagdas D. Onal Robotics Engineering Department WPI Soft Robotics Laboratory Worcester Polytechnic Institute MA USA

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

This paper presents a free-swimming, tetherless, cable-driven modular soft robotic fish. The body comprises a series of 3D-printed wave spring structures that create a flexible biologically inspired shape that is capable of an anguilliform swimming gait. A three-module soft robotic fish was designed, fabricated, and evaluated. The motion of the robot was characterized and different combinations of actuation amplitude, frequency, and phase shift were determined experimentally to determine the optimal parameters that maximized speed and minimized the cost of transport (COT). The maximum speed recorded was 0.20 BL/s (body lengths per second) with a COT of 15.82. These results were compared against other robotic and biological fish. We operated the robot, untethered, in a variety of environments to test how it was able to function outside of laboratory settings.

关键词： Shape Tail Soft robotics Fish Motors Biology Robustness

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Characterization and Design of A Hollow Cylindrical Ultrasonic Motor

arXiv

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arXiv 2024年

作者： Zhao, Zhanyue Wang, Yang Bales, Charles Ruiz-Cadalso, Daniel Zheng, Howard Furlong-Vazquez, Cosme Fischer, Gregory The Department of Robotics Engineering Worcester Polytechnic Institute WorcesterMA01605 United States The Department of Mechanical & Materials Engineering Worcester Polytechnic Institute WorcesterMA01605 United States

Piezoelectric ultrasonic motors perform the advantages of compact design, faster reaction time, and simpler setup compared to other motion units such as pneumatic and hydraulic motors, especially its non-ferromagnetic property makes it a perfect match in MRI-compatible robotics systems compared to traditional DC motors. Hollow shaft motors address the advantages of being lightweight and comparable to solid shafts of the same diameter, low rotational inertia, high tolerance to rotational imbalance due to low weight, and tolerance to high temperature due to low specific mass. This article presents a prototype of a hollow cylindrical ultrasonic motor (HCM) to perform direct drive, eliminate mechanical non-linearity, and reduce the size and complexity of the actuator or end effector assembly. Two equivalent HCMs are presented in this work, and under 50g prepressure on the rotor, it performed 383.3333rpm rotation speed and 57.3504mNm torque output when applying 282Vpp driving voltage. © 2024, CC BY-NC-SA.

关键词： Piezoelectric motors

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Study of MRI-compatible Notched Plastic Ultrasonic Stator with FEM Simulation and Holography Validation

arXiv

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arXiv 2024年

作者： Zhao, Zhanyue Tang, Haimi Carvalho, Paulo Furlong, Cosme Fischer, Gregory S. Department of Robotics Engineering Worcester Polytechnic Institute WorcesterMA01605 United States Department of Mechanical & Materials Engineering Worcester Polytechnic Institute WorcesterMA01605 United States

Intra-operative image guidance using magnetic resonance imaging (MRI) can significantly enhance the precision of surgical procedures, such as deep brain tumor ablation. However, the powerful magnetic fields and limited space within an MRI scanner require the use of robotic devices to aid surgeons. Piezoelectric motors are commonly utilized to drive these robots, with piezoelectric ultrasonic motors being particularly notable. These motors consist of a piezoelectric ring stator that is bonded to a rotor through frictional coupling. When the stator is excited at specific frequencies, it generates distinctive mode shapes with surface waves that exhibit both in-plane and out-of-plane displacement, leading to the rotation of the rotor. In this study, we continue our previous work and refine the motor design and performance, we combine finite element modeling (FEM) with stroboscopic and time-averaged digital holography to validate a further plastic-based ultrasonic motor with better rotary performance. © 2024, CC BY-NC-SA.

关键词： Piezoelectric motors

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Decentralized Multi-Agent Reinforcement Learning with Global State Prediction

arXiv

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arXiv 2023年

作者： Bloom, Joshua Paliwal, Pranjal Mukherjee, Apratim Pinciroli, Carlo Robotics Engineering Worcester Polytechnic Institute WorcesterMA United States

Deep reinforcement learning (DRL) has seen remarkable success in the control of single robots. However, applying DRL to robot swarms presents significant challenges. A critical challenge is non-stationarity, which occurs when two or more robots update individual or shared policies concurrently, thereby engaging in an interdependent training process with no guarantees of convergence. Circumventing non-stationarity typically involves training the robots with global information about other agents’ states and/or actions. In contrast, in this paper we explore how to remove the need for global information. We pose our problem as a Partially Observable Markov Decision Process, due to the absence of global knowledge on other agents. Using collective transport as a testbed scenario, we study two approaches to multi-agent training. In the first, the robots exchange no messages, and are trained to rely on implicit communication through push-and-pull on the object to transport. In the second approach, we introduce Global State Prediction (GSP), a network trained to form a belief over the swarm as a whole and predict its future states. We provide a comprehensive study over four well-known deep reinforcement learning algorithms in environments with obstacles, measuring performance as the successful transport of the object to a goal location within a desired time-frame. Through an ablation study, we show that including GSP boosts performance and increases robustness when compared with methods that use global knowledge. © 2023, CC BY.

关键词： Robots

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Graph Coordinates and Conventional Neural Networks - An Alternative for Graph Neural Networks

Graph Coordinates and Conventional Neural Networks - An Alte...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Qin, Zheyi Paffenroth, Randy Jayasumana, Anura P. Colorado State University Electrical and Computer Engineering Fort Collins United States Worcester Polytechnic Institute Department of Data Science Program WorcesterMA United States

ISBN: (纸本)9798350324457

Graph-based data present unique challenges and opportunities for machine learning. Graph Neural Networks (GNNs), and especially those algorithms that capture graph topology through message passing for neighborhood aggregation, have been a leading solution. However, these networks often require substantial computational resources and may not optimally leverage the information contained in the graph's topology, particularly for large-scale or complex *** propose Topology Coordinate Neural Network (TCNN) and Directional Virtual Coordinate Neural Network (DVCNN) as novel and efficient alternatives to message passing GNNs, that directly leverage the graph's topology, sidestepping the computational challenges presented by competing algorithms. Our proposed methods can be viewed as a reprise of classic techniques for graph embedding for neural network feature engineering, but they are novel in that our embedding techniques leverage ideas in Graph Coordinates (GC) that are lacking in current *** results, benchmarked against the Open Graph Benchmark Leaderboard, demonstrate that TCNN and DVCNN achieve competitive or superior performance to message passing GNNs. For similar levels of accuracy and ROC-AUC, TCNN and DVCNN need far fewer trainable parameters than contenders of the OGBN Leaderboard. The proposed TCNN architecture requires fewer parameters than any neural network method currently listed in the OGBN Leaderboard for both OGBN-Proteins and OGBN-Products datasets. Conversely, our methods achieve higher performance for a similar number of trainable parameters. These results hold across diverse datasets and edge features, underscoring the robustness and generalizability of our methods. By providing an efficient and effective alternative to message passing GNNs, our work expands the toolbox of techniques for graph-based machine learning. A significantly lower number of tunable parameters for a given evaluation metric makes TCNN and DVCNN especiall

关键词： graph analytics graph coordinate graph embedding green AI network embedding

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Markerless Suture Needle Tracking From A Robotic Endoscope Based On Deep Learning

Markerless Suture Needle Tracking From A Robotic Endoscope B...

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International Symposium on Medical robotics (ISMR)

作者： Yiwei Jiang Haoying Zhou Gregory S. Fischer Department of Robotics Engineering Worcester Polytechnic Institute Worcester MA USA

Advancements in robot-assisted surgery have been rapidly growing since two decades ago. More recently, the automation of robotic surgical tasks has become the focus of research. In this area, the detection and tracking of a surgical tool are crucial for an autonomous system to plan and perform a procedure. For example, knowing the position and posture of a needle is a prerequisite for an automatic suturing system to grasp it and perform suturing tasks. In this paper, we proposed a novel method, based on Deep Learning and Point-to-point Registration, to track the 6 degrees of freedom (DOF) pose of a metal suture needle from a robotic endoscope (an Endoscopic Camera Manipulator from the da Vinci Robotic Surgical Systems), without the help of any marker. The proposed approach was implemented and evaluated in a standard simulated surgical environment provided by the 2021–2022 AccelNet Surgical robotics Challenge, thus demonstrates the potential to be translated into a real-world scenario. A customized dataset containing 836 images collected from the simulated scene with ground truth of poses and key points information was constructed to train the neural network model. The best pipeline achieved an average position error of 1.76 mm while the average orientation error is 8.55 degrees, and it can run up to 10 Hz on a PC.

关键词： Deep learning Medical robotics Endoscopes Robot vision systems Pipelines Neural networks Surgery

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A Shared Autonomous Nursing Robot Assistant with Dynamic Workspace for Versatile Mobile Manipulation

A Shared Autonomous Nursing Robot Assistant with Dynamic Wor...

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

作者： Nikita Boguslavskii Zhuoyun Zhong Lorena Maria Genua Zhi Li Robotics Engineering Worcester Polytechnic Institute (WPI) Worcester MA USA

This paper presents a novel integration of a shared autonomous mobile humanoid robot for remote nursing assistance. The proposed nursing robot has a motorized versatile supporting structure to allow flexible integration of the system components, autonomously adjust its mobile manipulation workspace and improve its reachability and manipulability to operate in a cluttered environment. The robot also provides a novel integration of robot autonomy to reduce the human effort to coordinate the motorized chest and arm motion, control the precise manipulation of objects and camera viewpoint, and handle complex collision avoidance in human-guided gross manipulation. Moreover, we developed an open-source virtual testbed that integrates ROS- and Unity-based robot simulation and benchmark mobile manipulation nursing tasks and scenarios in a realistic simulation of a hospital environment. The virtual testbed supports various contemporary gaming and AR/VR interfaces to control the virtual human and robots, and provides autonomy for navigation, manipulation, and remote active perception assistance. We conducted a user study (N=9) to validate that the versatile supporting structure and shared autonomy of the physical testbed can effectively reduce the human effort to control unstructured manipulation, and improve the robot's reachability and manipulability. In addition, we conducted a pilot study (N=8) to test the usability of the virtual testbed and collect feedback from representative users.

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