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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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Shape Control of Variable Length Continuum Robots Using Clothoid-Based Visual Servoing

Shape Control of Variable Length Continuum Robots Using Clot...

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

作者： Abhinav Gandhi Shou-Shan Chiang Cagdas D. Onal Berk Calli Department of Robotics Engineering Worcester Polytechnic Institute Worcester MA USA

In this paper, we present a novel clothoid-based visual servoing method for controlling the shape of a variable length continuum manipulator. Clothoids are curves with linearly changing curvature. They allow us to obtain a smooth representation of a continuum manipulator's shape in a compact form with few parameters. Using this curve model, we generate image features that are used in an adaptive visual servoing method to drive the robot to a desired shape. The adaptive algorithm estimates and updates a local interaction matrix that maps the rate of change in clothoid features to actuator velocities of the continuum manipulator. As such, the method does not require any robot model or even actuator encoder measurements and only uses the visual clothoid features to control the robot shape. A unique advantage of using our clothoid representation is being able to generate reference shape curves without the need for taking images of the robot at the desired shapes. Experiments demonstrate successful shape and end effector pose convergence for a diverse set of references. Our repeatability tests demonstrate that the system performance is consistent. Notably, we also present the first results in the literature for the vision-based shape control of a variable length continuum robot, extending and contracting to achieve the desired shape.

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Robot-Assisted Wide-Area Photoacoustic System

Robot-Assisted Wide-Area Photoacoustic System

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IEEE Symposium (IUS) Ultrasonics

作者： Shang Gao Xihan Ma Haichong K. Zhang Department of Robotics Engineering Worcester Polytechnic Institute Worcester USA Department of Biomedical Engineering Worcester Polytechnic Institute Worcester USA Department of Computer Science Worcester Polytechnic Institute Worcester USA

Liver ischemia and reperfusion injury (IRI) is a vascular complication that can lead to hepatic functional impairment. IRI is relevant in liver transplantation and during liver surgery performed with intermittent vascular inflow occlusion. Detecting intraoperative liver oxygenation impairment and ischemia could help prevent IRI, which is challenging partially due to the multiple hepatic vascular inflows. We propose a robot-assisted wide-area photoacoustic (PA) imaging system to monitor the liver oxygenation level intraoperatively. PA imaging has been widely used for oxygenation detection in preclinical research. We used a robotic arm to actuate the 2D PA probe and scan across the liver surface. An accurate volumetric tomography can be reconstructed via registering 2D images to the recorded robot trajectory. An ex vivo porcine liver sample was scanned using the proposed system. The result suggested that the robot-assisted wide-area PA tomography has the potential to be incorporated into clinical practice to evaluate liver SO2 intraoperatively.

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Cut Sequencing Algorithm for Safely Disassembling Large Structures

Cut Sequencing Algorithm for Safely Disassembling Large Stru...

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IEEE Conference on Decision and Control

作者： James Akl Sumanth Pericherla Berk Calli Robotics Engineering Department Worcester Polytechnic Institute Worcester MA USA

Disassembly and fragmentation are key operations in the dismantling and recycling of decommissioned structures such as aircraft, vessels, and buildings. Often, such operations are hazardous requiring careful planning for safe execution based on the experience and intuition of workers and forepersons. We propose and devise an algorithm for the automated sequencing of cuts to disassemble large structures. Using feedback from physics-based simulations and a mathematical model for safety, our algorithm performs sequential decision-making yielding the order of the cuts on the structure and the corresponding safe standing positions of the cutter (representing a worker or a robot). Our goal is to determine a sequence of cuts and cutter locations to maximize safety for the cutter and the environment. We establish the optimal solution via exhaustive searching, and design a greedy decision scheme to reduce the search runtime. Using our evaluations in simulation, we compare our greedy decision scheme against exhaustive searching and random searching, concluding that it satisfices the goal with high safety scores and low runtime.

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Vision-Based Oxy-Fuel Torch Control for Robotic Metal Cutting

Vision-Based Oxy-Fuel Torch Control for Robotic Metal Cuttin...

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

作者： James Akl Yash Patil Chinmay Todankar Berk Calli Robotics Engineering Department Worcester Polytechnic Institute Worcester MA USA

The automation of key processes in metal cutting would substantially benefit many industries such as manufacturing and metal recycling. We present a vision-based control scheme for automated metal cutting with oxy-fuel torches, an established cutting medium in industry. The system consists of a robot equipped with a cutting torch and an eye-in-hand camera observing the scene behind a tinted visor. We develop a vision-based control algorithm to servo the torch's motion by visually observing its effects on the metal surface. As such, the vision system processes the metal surface's heat pool and computes its associated features, specifically pool convexity and intensity, which are then used for control. The operating conditions of the control problem are defined within which the stability is proven. In addition, metal cutting experiments are performed using a physical 1-DOF robot and oxy-fuel cutting equipment. Our results demonstrate the successful cutting of metal plates across three different plate thicknesses, relying purely on visual information without a priori knowledge of the thicknesses.

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Acoustic Reflector-Integrated Encapsulation for Needle-Aligned Ultrasound-Guided PCNL Access

Acoustic Reflector-Integrated Encapsulation for Needle-Align...

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IEEE Symposium (IUS) Ultrasonics

作者： Abhinav Palisetti Alexander King Cabot Priestner Haohao Yi Yichuan Tang Ryo Murakami Haichong K. Zhang Biomedical Engineering Department Worcester Polytechnic Institute Worcester MA USA Mechanical Engineering Department Worcester Polytechnic Institute Worcester MA USA Robotics Engineering Department Worcester Polytechnic Institute Worcester MA USA

Percutaneous Nephrolithotomy (PCNL) is a procedure to remove kidney stones by needle insertion into the kidney followed by dilation of the needle path for surgical tools to enter and stones to be removed. Current techniques for needle insertion either do not visualize the needle entry point into tissue, or require careful hand-hand coordination to visualize the needle. This research aims to develop a stable system to visualize the entire needle insertion path during ultrasound-guided PCNL. A novel method, designed by Tang et al. (2022), implemented two parallel acoustic reflectors to reflect ultrasound into the patient, and a needle inserted through a gap between the reflectors. Results showed the ability to visualize the needle path at any needle pitch angle, and, most importantly, the ability to visualize the needle from its insertion point to the target. In this current research, a modular design was created that allowed any probe to fit into the system by altering just three components. Several structural modifications were made to make the system waterproof, allowing the fluid material that housed the reflectors to be replaced with a fluid with better sound transmission. An algorithm was created to allow better needle tip and needle path visualization by analyzing raw image data. Custom acoustic impedance-matched materials were used to allow better sound transmission and better image quality. Through multiple tests and a mathematical model, the team developed an optimal combination of materials that produced the best image quality, determined through Contrast to Noise Ratio (CNR), Signal to Noise Ratio (SNR), and Full-Width Half-Maximum (FWHM).

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Soft Fluidic Closed-Loop Controller for Untethered Underwater Gliders

Soft Fluidic Closed-Loop Controller for Untethered Underwate...

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IEEE International Conference on Soft robotics (RoboSoft)

作者： Kalina Bonofiglio Lauryn Whiteside Maya Angeles Matthew Haahr Brandon Simpson Josh Palmer Yijia Wu Markus P. Nemitz Department of Robotics Engineering Worcester Polytechnic Institute Worcester MA USA Department of Mechanical Engineering Worcester Polytechnic Institute Worcester MA USA Department of Electrical Engineering Worcester Polytechnic Institute Worcester MA USA

Soft underwater robots typically explore bio-inspired designs at the expense of power efficiency when compared to traditional underwater robots, which limits their practical use in real-world applications. We leverage a fluidic closed-loop controller to actuate a passive underwater glider. A soft hydrostatic pressure sensor is configured as a bang-bang controller actuating a swim bladder made from silicone balloons. Our underwater glider oscillates between the water surface and 4 m depth while traveling 15 m translationally. The fluidic underwater glider demonstrates a power efficiency of 28 mW/m. This work demonstrates a low-cost and power-efficient underwater glider and non-electronic controller. Due to its simple design, low cost, and ease of fabrication using FDM printing and soft lithography, it serves as a starting point for the exploration of non-electronic underwater soft robots.

关键词： Fabrication Pressure sensors Autonomous underwater vehicles Soft lithography Power demand Costs Soft robotics

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