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A Low-Cost Bio-inspired Soft Wrist Enhancing Robotic Manipulation Dexterity

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A Low-Cost Bio-inspired Soft Wrist Enhancing Robotic Manipul...

IEEE International Conference on robotics and Biomimetics

作者： Honghao Guo Junda Huang Jianshu Zhou Fei Chen Yunhui Liu Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong Center for Logistics Robotics (HKCLR)

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

Manipulation plays a crucial role in the automation of modern logistics. One of the most challenging tasks in logistics is packaging, which requires precise and skillful manipulation of target goods. Expert human laborers excel in this task, as their wrists can easily adjust the orientation of their hands to adapt to different picking surfaces and reorient the target as needed. This ability significantly enhances human maneuverability and efficiency. However, robots still struggle to match the capabilities of human laborers, particularly when it comes to manipulating objects' orientations in tight spaces. To address this challenge in automatic packaging, we propose a novel solution: a soft robotic wrist. Our robotic wrist utilizes a cable-driven origami mechanism to adjust its configuration, enabling omni-directional bending. This innovative design dramatically enhances dexterity and expands the workspace of existing robotic manipulators. To validate the effectiveness of our robotic wrist, we conducted dedicated experiments to assess its dexterity and precision. Building upon this technology, we developed an intelligent packaging system that incorporates the robotic wrist. This system can automatically locate the target and determine its orientation, reorient the targets as necessary, and place them in the desired position. Overall, our proposed robotic wrist offers a promising solution to the challenges of dexterous and efficient robotic packaging in logistics and warehouse applications.

关键词： Wrist Hands Biomimetics Buildings Grasping Packaging Soft robotics Manipulators Robots Logistics

Physics-Informed Deep Learning-Based Monocular Vision and IMU Fusion for Extracting Position and Heading Data of Multirotors

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Physics-Informed Deep Learning-Based Monocular Vision and IM...

RSI/ISM International Conference on robotics and Mechatronics (ICROM)

作者： Arshia Rezaei Erfan Radfar Amin Talaeizadeh Aria Alasty Department of Mechanical Engineering Advanced Research Lab for Control and Agricultural Robotics (Sharif AgRoLab) Sharif University of Technology

ISBN: (数字)9798331529734

ISBN: (纸本)9798331529741

Multirotor Unmanned Aerial Vehicles (UAV)s flight performance rely on accurate estimation of their attitude and position for stable and robust control, which is essential for precise path following and management. Traditional methods for estimating six Degrees of Freedom (DoF) parameters, such as position, orientation, and velocity, often combine data from Global Navigation Satellite System (GNSS), Inertial Measurement Unit (IMU), and vision sensors using complex algorithms for fusion and filtering. This approach can lead to challenges in real-time processing and robustness to environmental variations. In this paper, we propose a novel approach that leverages deep learning and incorporates a physics-informed loss function based on monocular vision data to enhance the accuracy of these sensors by eliciting position and heading data from a trained model of flow detection, enhanced with a mathematical model of optical flow. We implement a fusion of camera, IMU, magnetometer, and ultrasonic sensors for robust and accurate 6 DoF data acquisition in multirotor systems. Our proposed framework utilizes Convolutional Neural Networks (CNN)s to directly learn the mapping between sensor data and 6 DoF parameters. By jointly processing visual and inertial data in an end-to-end manner, our model exploits the complementary information provided by each sensor modality, thereby enhancing estimation accuracy and robustness to external disturbances. We demonstrate the effectiveness of our approach through extensive experiments on real-world multirotor platforms, show-casing significant improvements in pose and motion estimation accuracy compared to conventional methods. Our fusion deep learning framework offers a promising pathway for enhancing the autonomy and performance of multi rotor UAVs.

关键词： Deep learning Accuracy Measurement units Magnetic sensors Motion estimation Estimation Autonomous aerial vehicles Robot sensing systems Robustness Real-time systems

Robot Manipulation of Dynamic Object with Vision-Based Reinforcement Learning

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Robot Manipulation of Dynamic Object with Vision-Based Reinf...

IEEE International Conference on Control and robotics engineering (ICCRE)

作者： Chenchen Liu Zhengshen Zhang Lei Zhou Zhiyang Liu Marcelo H. Ang Wenfeng Lu Francis EH Tay Advanced Robotics Centre National University of Singapore Singapore Department of Mechanical Engineering National University of Singapore Singapore

ISBN: (数字)9798350372694

ISBN: (纸本)9798350372700

With the advanced development of AI, Deep Reinforcement Learning (DRL) is one way to let robots learn how to complete tasks independently. Based on DRL algorithms, robot manipulation in the tabletop scene is regarded as a typical environment for testing and validation. Within this context, we designed a vision-based scene to explore the performance of DRL algorithms in manipulating a dynamic object. Contrary to previous work with simple moving trajectories, the complexity of our approach was increased by applying a randomized vibrating trajectory. To better capture visual cues in motion, we also incorporated a Long Short-term Memory (LSTM) module to process the temporal information between the serial frames. By comparing with setups that only used basic CNN encoders, we found the addition of LSTM can drastically increase the learning efficiency under model-free RL algorithms like SAC. The learning inflection point of applying LSTM appeared at 300k time steps while the others were delayed to 500k time steps or even later, which underscores the necessity of extracting temporal features in dynamic scenes. Furthermore, we observed that by integrating LSTM, there is no need to explicitly append kinematic information, which also supports the idea that temporal information inherently encodes kinematics data.

关键词： Visualization Heuristic algorithms Kinematics Feature extraction Trajectory Data mining Robots

BIRD: Battlefield-Integrated Reconnaissance Drone with VQA

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BIRD: Battlefield-Integrated Reconnaissance Drone with VQA

AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Rayon, Nathan Menser, Kobi Catalano, Louis Vega, Jonathan Najarro, Julio Sevil, Hakki Erhan Department of Computer Science University of West Florida FL32514 United States Department of Mechanical Engineering University of West Florida FL32514 United States Catalano Aerospace LLC FL32502 United States Department of Intelligent Systems & Robotics University of West Florida FL32514 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

This paper introduces a new unmanned aerial system (UAS) design featuring coaxial rotors and tethered operation with a base station. The coaxial rotor configuration enhances stability and maneuverability, particularly in challenging environments. The tethered connection to a base station ensures prolonged flight duration and enables continuous power and data transmission. Moreover, the UAS incorporates a visual question answering (VQA) based target identification system. This system leverages advanced computer vision algorithms to analyze visual input from onboard cameras, coupled with natural language processing techniques to interpret and respond to queries regarding identified targets. The integration of VQA technology enables critical decision-making decisions through providing crucial information identify about targets in real-time scenarios. The new design has a significant potential for applications in surveillance, reconnaissance, search and rescue, and disaster response, as this design was conceptualized to provide persistent Intelligence, Reconnaissance & Surveillance (ISR) capabilities. Further analysis of the concept design was conducted to investigate the feasibility of novel design characteristics. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Reconnaissance aircraft

Adaptive observer for a LTV system with partially unknown state matrix and delayed measurements 14

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Adaptive observer for a LTV system with partially unknown st...

14th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops, ICUMT 2022

作者： Bobtsov, Alexey Nikolaev, Nikolay Slita, Olga Kozachek, Olga Oskina, Olga Itmo University Institute of Problems of Mechanical Engineering Laboratory 'Control of Complex Systems' Department of Control Systems and Robotics Saint-Petersburg Russia Itmo University Department of Control Systems and Robotics Saint-Petersburg Russia

ISBN: (数字)9798350398663

ISBN: (纸本)9798350398663

Problem of adaptive state observer synthesis for linear time-varying (LTV) system with unknown time-varying parameter and delayed output measurements is considered. State observation problem has attracted the attention of many researchers [1]. In this paper the results proposed in the [2]-[4] are developed. It is supposed that the state matrix can be represented as sum of known and unknown parts. Output vector is measured with known constant delay. An adaptive identification algorithm which reconstructs unknown state and unknown time-varying parameter is proposed. © 2022 IEEE.

关键词： Time varying control systems

Single-Fiber Optical Frequency Domain Reflectometry Shape Sensing of Continuum Manipulators with Planar Bending

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arXiv 2024年

作者： Tavangarifard, Mobina Ovalle, Wendy Rodriguez Alambeigi, Farshid The Walker Department of Mechanical Engineering The Texas Robotics The University of Texas at Austin AustinTX78712 United States

To address the challenges associated with shape sensing of continuum manipulators (CMs) using Fiber Bragg Grating (FBG) optical fibers, we feature a unique shape sensing assembly utilizing solely a single Optical Frequency Domain Reflectometry (OFDR) fiber attached to a flat nitinol wire (NiTi). Integrating this easy-to-manufacture unique sensor with a long and soft CM with 170 mm length, we performed different experiments to evaluate its shape reconstruction ability. Results demonstrate phenomenal shape reconstruction accuracy for both C-shape ( © 2024, CC BY-NC-ND.

关键词： Errors

BenchNav: Simulation Platform for Benchmarking Off-road Navigation Algorithms with Probabilistic Traversability

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arXiv 2024年

作者： Endo, Masafumi Honda, Kohei Ishigami, Genya the Space Robotics Group Department of Mechanical Engineering Keio University Kanagawa223-8522 Japan the Mobility System Group Department of Mechanical Systems Engineering Nagoya University Aichi464-8603 Japan

As robotic navigation techniques in perception and planning advance, mobile robots increasingly venture into off-road environments involving complex traversability. However, selecting suitable planning methods remains a challenge due to their algorithmic diversity, as each offers unique benefits. To aid in algorithm design, we introduce BenchNav, an open-source PyTorch-based simulation platform for benchmarking off-road navigation with uncertain traversability. Built upon Gymnasium, BenchNav provides three key features: 1) a data generation pipeline for preparing synthetic natural environments, 2) built-in machine learning models for traversability prediction, and 3) consistent execution of path and motion planning across different algorithms. We show BenchNav’s versatility through simulation examples in off-road environments, employing three representative planning algorithms from different domains. https://***/masafumiendo/benchnav Copyright © 2024, The Authors. All rights reserved.

关键词： Motion planning

ProxFly: Robust Control for Close Proximity Quadcopter Flight via Residual Reinforcement Learning

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arXiv 2024年

作者： Zhang, Ruiqi Zhang, Dingqi Mueller, Mark W. High Performance Robotics Lab Department of Mechanical Engineering University of California BerkeleyCA94720 United States

This paper proposes the ProxFly, a residual deep Reinforcement Learning (RL)-based controller for close proximity quadcopter flight. Specifically, we design a residual module on top of a cascaded controller (denoted as basic controller) to generate high-level control commands, which compensate for external disturbances and thrust loss caused by downwash effects from other quadcopters. First, our method takes only the ego state and controllers' commands as inputs and does not rely on any communication between quadcopters, thereby reducing the bandwidth requirement. Through domain randomization, our method relaxes the requirement for accurate system identification and fine-tuned controller parameters, allowing it to adapt to changing system models. Meanwhile, our method not only reduces the proportion of unexplainable signals from the black box in control commands but also enables the RL training to skip the time-consuming exploration from scratch via guidance from the basic controller. We validate the effectiveness of the residual module in the simulation with different proximities. Moreover, we conduct the real close proximity flight test to compare ProxFly with the basic controller and an advanced model-based controller with complex aerodynamic compensation. Finally, we show that ProxFly can be used for challenging quadcopter in-air docking, where two quadcopters fly in extreme proximity, and strong airflow significantly disrupts flight. However, our method can stabilize the quadcopter in this case and accomplish docking. The resources are available at https://***/ruiqizhang99/ProxFly. Copyright © 2024, The Authors. All rights reserved.

关键词： Robust control

Towards Safe and Efficient Through-the-Canopy Autonomous Fruit Counting with UAVs

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arXiv 2024年

作者： Yang, Teaya Ibrahimov, Roman Mueller, Mark W. The High Performance Robotics Lab Department of Mechanical Engineering University of California BerkeleyCA94709 United States

We present an autonomous aerial system for safe and efficient through-the-canopy fruit counting. Aerial robot applications in large-scale orchards face significant challenges due to the complexity of fine-tuning flight paths based on orchard layouts, canopy density, and plant variability. Through-the-canopy navigation is crucial for minimizing occlusion by leaves and branches but is more challenging due to the complex and dense environment compared to traditional over-the-canopy flights. Our system addresses these challenges by integrating: i) a high-fidelity simulation framework for optimizing flight trajectories, ii) a low-cost autonomy stack for canopy-level navigation and data collection, and iii) a robust workflow for fruit detection and counting using RGB images. We validate our approach through fruit counting with canopy-level aerial images and by demonstrating the autonomous navigation capabilities of our experimental vehicle. Copyright © 2024, The Authors. All rights reserved.

关键词： Orchards