IEEE Robotics and Automation Letters最新文献_第9页

Agile Plane Transition of a Hexapod Climbing Robot 六足爬行机器人的敏捷平面过渡

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-04-14 DOI: 10.1109/LRA.2025.3560894

Chengzhang Gong;Li Fan;Chao Xu;Dacheng Wang

{"title":"Agile Plane Transition of a Hexapod Climbing Robot","authors":"Chengzhang Gong;Li Fan;Chao Xu;Dacheng Wang","doi":"10.1109/LRA.2025.3560894","DOIUrl":"https://doi.org/10.1109/LRA.2025.3560894","url":null,"abstract":"Traversing across adjacent planes is an important ability for legged climbing robots. While many robots can achieve autonomous ground-to-wall transitions, most are limited to scenarios where the angle between the planes has a certain value. In some cases, however, the robot needs to traverse planes with a wide variety of angles. To enhance the adaptability of the robot in such diverse scenarios, we analyze the plane transition process and propose a universal methodology for hexapod climbing robots with a two-stage workflow. In the first stage, we plan a trajectory of body without considering configuration of legs, within a reachable map. This low-dimensional map can be efficiently sampled and explored to identify feasible transitions. In the second stage, we use a motion prediction to generate landing points, as well as swing and stance trajectories for each leg. By tracking these trajectories, the robot can autonomously transition from one plane to another. Guided by this methodology, we design a hexapod climbing robot capable of autonomously traversing planes with angles ranging from 30° to 270°. For further validation, we build the physical prototype of the robot and conduct a series of plane transition experiments. The results demonstrate the feasibility of both our methodology and the robot.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 6","pages":"5959-5966"},"PeriodicalIF":4.6,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143901865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SkateDuct: Utilizing Vector Thrust of Ducted Fan UAVs for Terrestrial-Aerial Locomotion SkateDuct：利用矢量推力的管道风扇无人机的地面空中运动

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-04-14 DOI: 10.1109/LRA.2025.3560882

Zhong Yin;Hailong Pei

{"title":"SkateDuct: Utilizing Vector Thrust of Ducted Fan UAVs for Terrestrial-Aerial Locomotion","authors":"Zhong Yin;Hailong Pei","doi":"10.1109/LRA.2025.3560882","DOIUrl":"https://doi.org/10.1109/LRA.2025.3560882","url":null,"abstract":"Ducted fan UAVs (DFUAVs), characterized by vector thrust, vertical takeoff and landing (VTOL) capabilities, and high safety, have found widespread applications in both military and civilian scenarios. However, their limited endurance remains a significant constraint on their broader applications. To address this challenge, in this letter we explore a novel approach that exploits the vector thrust capabilities of DFUAVs to enable terrestrial-aerial locomotion through simple modifications without the need for additional actuators. The design of a DFUAV employing passive wheels for continuous ground and aerial operation is presented. This configuration allows for unchanged attitude and static stability during ground movement, with only a 10.3% increase in weight. Fluid simulations were conducted to analyze the variation in control vane aerodynamic efficiency under ground effect, leading to the development of a ground-effect-adjusted aerodynamic model based on experimental data. Furthermore, the dynamics of ground movement are analyzed, and a corresponding controller is developed, establishing a complete framework for seamless transition between terrestrial and aerial modes. Extensive real-world flight experiments validate the proposed structural design and control methods. By utilizing terrestrial locomotion, the UAV's energy consumption is reduced to just 33.9% of that during flight, effectively extending its operational duration by more than ten times.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 6","pages":"6047-6054"},"PeriodicalIF":4.6,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143908440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Online Motion Generation via Tangential Sampling-Based MPC Around Nonconvex Obstacles 基于切线采样的MPC在非凸障碍物周围的在线运动生成

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-04-14 DOI: 10.1109/LRA.2025.3560885

Guangbao Zhao;Ninglong Jin;Jianhua Wu;Zhenhua Xiong

{"title":"Online Motion Generation via Tangential Sampling-Based MPC Around Nonconvex Obstacles","authors":"Guangbao Zhao;Ninglong Jin;Jianhua Wu;Zhenhua Xiong","doi":"10.1109/LRA.2025.3560885","DOIUrl":"https://doi.org/10.1109/LRA.2025.3560885","url":null,"abstract":"Collision-free motion planning has a well-established research history, but the majority of studies have been centered around Euclidean space and conducted offline. The primary challenge in online motion planning lies in circumventing local minima, which become more pronounced in configuration space. We propose an online method for generating collision-free motion in configuration space that effectively avoids local minima. Our approach decomposes the optimal velocity into nominal and tangential components, with the tangential velocity optimized to facilitate escape from local minima. The tangential velocity is defined in the tangential space, with its normal direction determined by the gradient of the nearest distance between the robot and obstacles, relative to the robot's states. Direct optimization of the tangential velocity is challenging due to its dependence on varying tangent spaces. To address this, we represent the tangential velocity using the orthogonal basis of the tangent space, decoupling it from the varying tangent space. This allows explicit optimization of the tangential velocity by optimizing its components. Additionally, we introduce a warm-start operator in the tangent space to ensure the consistency and convergence. Furthermore, we propose a dynamic weight based on proximity to local minima to balance the tangential and nominal velocities, forming the optimal velocity. The effectiveness of our approach in avoiding local minima is validated through simulations and physical experiments.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 6","pages":"5537-5544"},"PeriodicalIF":4.6,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143888290","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Task-Parameterized Dynamic Movement Primitives With Reinforcement Learning for Improved Motion Planning 基于强化学习的任务参数化动态运动原语改进运动规划

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-04-14 DOI: 10.1109/LRA.2025.3560876

Kaiqi Huang;Xiaochun Ji;Jianhua Su;Xiaoyi Qu

{"title":"Task-Parameterized Dynamic Movement Primitives With Reinforcement Learning for Improved Motion Planning","authors":"Kaiqi Huang;Xiaochun Ji;Jianhua Su;Xiaoyi Qu","doi":"10.1109/LRA.2025.3560876","DOIUrl":"https://doi.org/10.1109/LRA.2025.3560876","url":null,"abstract":"Online trajectory planning in unstructured environments poses significant challenges for mobile robots, particularly when navigating complex obstacles. Traditional learning-from-demonstration (LfD) methods depend on offline datasets, limiting their ability to adapt to varying obstacle shapes and dynamic conditions. To address these limitations, we propose a novel motion planning framework that combines global trajectory generation with local adaptability. Dynamic Movement Primitives (DMPs) are employed to generate global trajectories based on demonstrations, while Task-Parameterized Potential Fields (TPPFs) enhance local adaptability. The Policy Improvement through Path Integrals (PI<inline-formula><tex-math>$^{2}$</tex-math></inline-formula>) algorithm is utilized to optimize model parameters. The TPPF framework consists of two key components: (a) an obstacle avoidance field, which accounts for the robot's size, obstacle dimensions, and relative distances, allowing effective volumetric avoidance without extensive modeling; and (b) an attractive field, which directs the robot toward task-specific goals while steering it away from undesirable paths. By leveraging the PI<inline-formula><tex-math>$^{2}$</tex-math></inline-formula> algorithm, model parameters are optimized to produce trajectories that preserve the characteristics of demonstrated motions, while improving obstacle avoidance and task-oriented navigation. Experiments conducted in both simulations and dynamic real-world scenarios validate the proposed framework's effectiveness, demonstrating smoother trajectories and enhanced obstacle avoidance compared to baseline approaches.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 6","pages":"5457-5464"},"PeriodicalIF":4.6,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143888361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Champion-Level Vision-Based Reinforcement Learning Agent for Competitive Racing in Gran Turismo 7 《Gran Turismo 7》中基于视觉的强化学习代理

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-04-14 DOI: 10.1109/LRA.2025.3560873

Hojoon Lee;Takuma Seno;Jun Jet Tai;Kaushik Subramanian;Kenta Kawamoto;Peter Stone;Peter R. Wurman

引用次数: 0

Using Mobile AR for Rapid Feasibility Analysis for Deployment of Robots: A Usability Study With Non-Expert Users 使用移动AR进行机器人部署的快速可行性分析：非专业用户的可用性研究

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-04-14 DOI: 10.1109/LRA.2025.3560888

Krzysztof Zielinski;Slawomir Tadeja;Bruce Blumberg;Mikkel Baun Kjærgaard

{"title":"Using Mobile AR for Rapid Feasibility Analysis for Deployment of Robots: A Usability Study With Non-Expert Users","authors":"Krzysztof Zielinski;Slawomir Tadeja;Bruce Blumberg;Mikkel Baun Kjærgaard","doi":"10.1109/LRA.2025.3560888","DOIUrl":"https://doi.org/10.1109/LRA.2025.3560888","url":null,"abstract":"Automating a production line with robotic arms is a complex, demanding task that requires not only substantial resources but also a deep understanding of the automated processes and available technologies and tools. Expert integrators must consider factors such as placement, payload, and robot reach requirements to determine the feasibility of automation. Ideally, such considerations are based on a detailed digital simulation developed before any hardware is deployed. However, this process is often time-consuming and challenging. To simplify these processes, we introduce a much simpler method for the feasibility analysis of robotic arms' reachability, designed for non-experts. We implement this method through a mobile, sensing-based prototype tool. The two-step experimental evaluation included the expert user study results, which helped us identify the difficulty levels of various deployment scenarios and refine the initial prototype. The results of the subsequent quantitative study with 22 non-expert participants utilizing both scenarios indicate that users could complete both simple and complex feasibility analyses in under ten minutes, exhibiting similar cognitive loads and high engagement. Overall, the results suggest that the tool was well-received and rated as highly usable, thereby showing a new path for changing the ease of feasibility analysis for automation.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 6","pages":"5489-5496"},"PeriodicalIF":4.6,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143888433","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Design and Control of a High-Performance Hopping Robot 高性能跳跃机器人的设计与控制

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-04-14 DOI: 10.1109/LRA.2025.3560884

Samuel Burns;Matthew Woodward

{"title":"Design and Control of a High-Performance Hopping Robot","authors":"Samuel Burns;Matthew Woodward","doi":"10.1109/LRA.2025.3560884","DOIUrl":"https://doi.org/10.1109/LRA.2025.3560884","url":null,"abstract":"Jumping and hopping locomotion are efficient means of traversing unstructured rugged terrain with the former being the focus of roboticists; a focus that has recently been changing. This focus has led to significant performance and understanding in jumping robots but with limited practical applications as they require significant time between jumps to store energy, thus relegating jumping to a secondary role in locomotion. Hopping locomotion, however, can preserve and transfer energy to subsequent hops without long energy storage periods. However, incorporating the performance observed in jumping systems into their hopping counterparts is an ongoing challenge. To date, hopping robots typically operate around 1 m with a maximum of 1.63 m whereas jumping robots have reached heights of 30 m. This is due to the added design and control complexity inherent in developing a system able to input and store the necessary energy while withstanding the forces involved and managing the system's state. Here we report hopping robot design principles for efficient, robust, high-specific energy, and high-energy input actuation through analytical, simulation, and experimental results. The resulting robot (MultiMo-MHR) can hop over 4 meters or <inline-formula><tex-math>$sim$</tex-math></inline-formula>2.4x the current state-of-the-art.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 6","pages":"5641-5648"},"PeriodicalIF":4.6,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143883388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dual Agent Learning Based Aerial Trajectory Tracking 基于双智能体学习的空中轨迹跟踪

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-04-14 DOI: 10.1109/LRA.2025.3560841

Shaswat Garg;Houman Masnavi;Baris Fidan;Farrokh Janabi-Sharifi

{"title":"Dual Agent Learning Based Aerial Trajectory Tracking","authors":"Shaswat Garg;Houman Masnavi;Baris Fidan;Farrokh Janabi-Sharifi","doi":"10.1109/LRA.2025.3560841","DOIUrl":"https://doi.org/10.1109/LRA.2025.3560841","url":null,"abstract":"This paper presents a novel reinforcement learning framework for trajectory tracking of autonomous aerial vehicles in cluttered environments using a dual-agent architecture. Traditional optimization methods for trajectory tracking face significant computational challenges and lack robustness in dynamic environments. Our approach employs deep reinforcement learning (RL) to overcome these limitations, leveraging 3D pointcloud data to perceive the environment without relying on memory-intensive obstacle representations like occupancy grids. The proposed system features two RL agents: one for predicting AAV velocities to follow a reference trajectory and another for managing collision avoidance in the presence of obstacles. This architecture ensures real-time performance and adaptability to uncertainties. We demonstrate the efficacy of our approach through simulated and real-world experiments, highlighting improvements over state-of-the-art RL and optimization-based methods. Additionally, a curriculum learning paradigm is employed to scale the algorithms to more complex environments, ensuring robust trajectory tracking and obstacle avoidance in both static and dynamic scenarios.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 6","pages":"5609-5616"},"PeriodicalIF":4.6,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143883455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

FoAR: Force-Aware Reactive Policy for Contact-Rich Robotic Manipulation 富接触机器人操作的力感知响应策略

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-04-14 DOI: 10.1109/LRA.2025.3560871

Zihao He;Hongjie Fang;Jingjing Chen;Hao-Shu Fang;Cewu Lu

引用次数: 0

Safe Multi-Agent Reinforcement Learning for Behavior-Based Cooperative Navigation 基于行为的协同导航安全多智能体强化学习

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-04-14 DOI: 10.1109/LRA.2025.3560830

Murad Dawood;Sicong Pan;Nils Dengler;Siqi Zhou;Angela P. Schoellig;Maren Bennewitz

引用次数: 0