IEEE Robotics and Automation Letters最新文献_第7页

Exploring Unstructured Environments Using Minimal Sensing on Cooperative Nano-Drones 利用合作式纳米无人机上的最小传感探索非结构化环境

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2024-10-24 DOI: 10.1109/LRA.2024.3486212

Pedro Arias-Perez;Alvika Gautam;Miguel Fernandez-Cortizas;David Perez-Saura;Srikanth Saripalli;Pascual Campoy

{"title":"Exploring Unstructured Environments Using Minimal Sensing on Cooperative Nano-Drones","authors":"Pedro Arias-Perez;Alvika Gautam;Miguel Fernandez-Cortizas;David Perez-Saura;Srikanth Saripalli;Pascual Campoy","doi":"10.1109/LRA.2024.3486212","DOIUrl":"https://doi.org/10.1109/LRA.2024.3486212","url":null,"abstract":"Recent advances have improved autonomous navigation and mapping under payload constraints, but current multi-robot inspection algorithms are unsuitable for nano-drones, due to their need for heavy sensors and high computational resources. To address these challenges, we introduce \u0000<italic>ExploreBug\u0000, a novel hybrid frontier range-bug algorithm designed to handle limited sensing capabilities for a swarm of nano-drones. This system includes three primary components: a mapping subsystem, an exploration subsystem, and a navigation subsystem. Additionally, an intra-swarm collision avoidance system is integrated to prevent collisions between drones. We validate the efficacy of our approach through extensive simulations and real-world exploration experiments, involving up to seven drones in simulations and three in real-world settings, across various obstacle configurations and with a maximum navigation speed of 0.75 m/s. Our tests prove that the algorithm efficiently completes exploration tasks, even with minimal sensing, across different swarm sizes and obstacle densities. Furthermore, our frontier allocation heuristic ensures an equal distribution of explored areas and paths traveled by each drone in the swarm. We publicly release the source code of the proposed system to foster further developments in mapping and exploration using autonomous nano drones.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"9 12","pages":"11202-11209"},"PeriodicalIF":4.6,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10734219","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142598642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ATI-CTLO: Adaptive Temporal Interval-Based Continuous-Time LiDAR-Only Odometry ATI-CTLO：基于时间间隔的自适应连续时间激光雷达测距仪

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2024-10-24 DOI: 10.1109/LRA.2024.3486233

Bo Zhou;Jiajie Wu;Yan Pan;Chuanzhao Lu

{"title":"ATI-CTLO: Adaptive Temporal Interval-Based Continuous-Time LiDAR-Only Odometry","authors":"Bo Zhou;Jiajie Wu;Yan Pan;Chuanzhao Lu","doi":"10.1109/LRA.2024.3486233","DOIUrl":"https://doi.org/10.1109/LRA.2024.3486233","url":null,"abstract":"The motion distortion in LiDAR scans caused by the robot's aggressive motion and environmental terrain features significantly impacts the positioning and mapping performance of 3D LiDAR odometry. Existing distortion correction solutions struggle to balance computational complexity and accuracy. In this letter, we propose an \u0000<bold>A\u0000daptive \u0000<bold>T\u0000emporal \u0000<bold>I\u0000nterval-based \u0000<bold>C\u0000ontinuous-\u0000<bold>T\u0000ime \u0000<bold>L\u0000iDAR-only \u0000<bold>O\u0000dometry (ATI-CTLO), which is based on straightforward and efficient linear interpolation. Our method can flexibly adjust the temporal intervals between control nodes according to the motion dynamics and environmental degeneracy. This adaptability enhances performance across various motion states and improves the algorithms robustness in degenerate, particularly feature-sparse, environments. We validated our method's effectiveness on multiple datasets across different platforms, achieving comparable accuracy to state-of-the-art LiDAR-only odometry methods. Notably, in situations involving aggressive motion and sparse features, our method outperforms existing LiDAR-only methods.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"9 12","pages":"11162-11169"},"PeriodicalIF":4.6,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142598621","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

Kernel-Based Metrics Learning for Uncertain Opponent Vehicle Trajectory Prediction in Autonomous Racing 基于核的度量学习用于自主赛车中不确定对手车辆的轨迹预测

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2024-10-24 DOI: 10.1109/LRA.2024.3486178

Hojin Lee;Youngim Nam;Sanghun Lee;Cheolhyeon Kwon

引用次数: 0

Safer Gap: Safe Navigation of Planar Nonholonomic Robots With a Gap-Based Local Planner 更安全的间隙：利用基于间隙的局部规划器实现平面非全局机器人的安全导航

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2024-10-24 DOI: 10.1109/LRA.2024.3486231

Shiyu Feng;Ahmad Abuaish;Patricio A. Vela

{"title":"Safer Gap: Safe Navigation of Planar Nonholonomic Robots With a Gap-Based Local Planner","authors":"Shiyu Feng;Ahmad Abuaish;Patricio A. Vela","doi":"10.1109/LRA.2024.3486231","DOIUrl":"https://doi.org/10.1109/LRA.2024.3486231","url":null,"abstract":"This paper extends the gap-based navigation technique \u0000<italic>Potential Gap\u0000 with safety guarantees at the local planning level for a kinematic planar nonholonomic robot model, leading to \u0000<italic>Safer Gap\u0000. It relies on a subset of navigable free space from the robot to a gap, denoted the keyhole region. The region is defined by the union of the largest collision-free disc centered on the robot and a collision-free trapezoidal region directed through the gap. \u0000<italic>Safer Gap\u0000 first generates Bézier-based collision-free paths within the keyhole regions. The keyhole region of the top scoring path is encoded by a shallow neural network-based zeroing barrier function (ZBF) synthesized in real-time. Nonlinear Model Predictive Control (NMPC) with \u0000<italic>Keyhole ZBF\u0000 constraints and output tracking of the Bézier path, synthesizes a safe kinematically feasible trajectory. The \u0000<italic>Potential Gap\u0000 projection operator serves as a last action to enforce safety if the NMPC optimization fails to converge to a solution within the prescribed time. Simulation and experimental validation of \u0000<italic>Safer Gap\u0000 confirm its collision-free navigation properties.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"9 12","pages":"11034-11041"},"PeriodicalIF":4.6,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142565543","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

Collision-Free Trajectory Optimization in Cluttered Environments Using Sums-of-Squares Programming 利用平方和编程实现杂乱环境中的无碰撞轨迹优化

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2024-10-24 DOI: 10.1109/LRA.2024.3486235

Yulin Li;Chunxin Zheng;Kai Chen;Yusen Xie;Xindong Tang;Michael Yu Wang;Jun Ma

{"title":"Collision-Free Trajectory Optimization in Cluttered Environments Using Sums-of-Squares Programming","authors":"Yulin Li;Chunxin Zheng;Kai Chen;Yusen Xie;Xindong Tang;Michael Yu Wang;Jun Ma","doi":"10.1109/LRA.2024.3486235","DOIUrl":"https://doi.org/10.1109/LRA.2024.3486235","url":null,"abstract":"In this work, we propose a trajectory optimization approach for robot navigation in cluttered 3D environments. We represent the robot's geometry as a semialgebraic set defined by polynomial inequalities such that robots with general shapes can be suitably characterized. We exploit the collision-free space directly to construct a graph of free regions, search for the reference path, and allocate each waypoint on the trajectory to a specific region. Then, we incorporate a uniform scaling factor for each free region and formulate a Sums-of-Squares (SOS) optimization problem whose optimal solutions reveal the containment relationship between robots and the free space. The SOS optimization problem is further reformulated to a semidefinite program (SDP), and the collision-free constraints are shown to be equivalent to limiting the scaling factor along the entire trajectory. Next, to solve the trajectory optimization problem with the proposed safety constraints, we derive a guiding direction for updating the robot configuration to decrease the minimum scaling factor by calculating the gradient of the Lagrangian at the primal-dual optimum of the SDP. As a result, this seamlessly facilitates the use of gradient-based methods in efficient solving of the trajectory optimization problem. Through a series of simulations and real-world experiments, the proposed trajectory optimization approach is validated in various challenging scenarios, and the results demonstrate its effectiveness in generating collision-free trajectories in dense and intricate environments.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"9 12","pages":"11026-11033"},"PeriodicalIF":4.6,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142565553","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

Floor Plan Based Active Global Localization and Navigation Aid for Persons With Blindness and Low Vision 基于平面图的盲人和低视力者主动全球定位和导航辅助系统

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2024-10-24 DOI: 10.1109/LRA.2024.3486208

R. G. Goswami;H. Sinha;P. V. Amith;J. Hari;P. Krishnamurthy;J. Rizzo;F. Khorrami

{"title":"Floor Plan Based Active Global Localization and Navigation Aid for Persons With Blindness and Low Vision","authors":"R. G. Goswami;H. Sinha;P. V. Amith;J. Hari;P. Krishnamurthy;J. Rizzo;F. Khorrami","doi":"10.1109/LRA.2024.3486208","DOIUrl":"https://doi.org/10.1109/LRA.2024.3486208","url":null,"abstract":"Navigation of an agent, such as a person with blindness or low vision, in an unfamiliar environment poses substantial difficulties, even in scenarios where prior maps, like floor plans, are available. It becomes essential first to determine the agent's pose in the environment. The task's complexity increases when the agent also needs directions for exploring the environment to reduce uncertainty in the agent's position. This problem of \u0000<italic>active global localization\u0000 typically involves finding a transformation to match the agent's sensor-generated map to the floor plan while providing a series of point-to-point directions for effective exploration. Current methods fall into two categories: learning-based, requiring extensive training for each environment, or non-learning-based, which generally depend on prior knowledge of the agent's initial position, or the use of floor plan maps created with the same sensor modality as the agent. Addressing these limitations, we introduce a novel system for real-time, active global localization and navigation for persons with blindness and low vision. By generating semantically informed real-time goals, our approach enables local exploration and the creation of a 2D semantic point cloud for effective global localization. Moreover, it dynamically corrects for odometry drift using the architectural floor plan, independent of the agent's global position and introduces a new method for real-time loop closure on reversal. Our approach's effectiveness is validated through multiple real-world indoor experiments, also highlighting its adaptability and ease of extension to any mobile robot.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"9 12","pages":"11058-11065"},"PeriodicalIF":4.6,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142565558","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

Flying Calligrapher: Contact-Aware Motion and Force Planning and Control for Aerial Manipulation 飞行书法家：用于空中操纵的接触感知运动和力规划与控制

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2024-10-24 DOI: 10.1109/LRA.2024.3486236

Xiaofeng Guo;Guanqi He;Jiahe Xu;Mohammadreza Mousaei;Junyi Geng;Sebastian Scherer;Guanya Shi

引用次数: 0

Design and Shape Control of Robotic Morphing Interface With Reprogrammable Stiffness Based on Machine Learning 基于机器学习的具有可重新编程刚度的机器人变形界面的设计与形状控制

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2024-10-21 DOI: 10.1109/LRA.2024.3484160

Xiaojie Diao;Juncai Long;Jituo Li;Chengdi Zhou;Huixu Dong;Guodong Lu

{"title":"Design and Shape Control of Robotic Morphing Interface With Reprogrammable Stiffness Based on Machine Learning","authors":"Xiaojie Diao;Juncai Long;Jituo Li;Chengdi Zhou;Huixu Dong;Guodong Lu","doi":"10.1109/LRA.2024.3484160","DOIUrl":"https://doi.org/10.1109/LRA.2024.3484160","url":null,"abstract":"Deformable organisms in nature inspire the design of shape-shifting robots, including soft robots, bionic robots and physical human-robot interfaces. However, to achieve multi-objective shape imitation and multi-form transformation, shape-shifting robots often require complex actuation systems, control strategies, and inverse design algorithms. In this letter, we propose a robotic morphing interface with reprogrammable stiffness (RoMI-RS) based on machine learning. RoMI-RS uses a circular elastic bilayer as the base, which can produce isotropic deformation under pneumatic actuation. By repeatedly attaching and detaching high-stiffness limiting layers to the surface of the base, the stiffness distribution can be reprogrammed, guiding anisotropic deformation. Thus, without changing the base material or actuation mechanism, RoMI-RS can precisely mimic various static shapes and dynamic movements. To address the nonlinear coupling of soft materials and pneumatic actuation, we employed a data-driven approach to inversely design limiting layer arrangements (i.e., the stiffness distribution of RoMI-RS) in the form of images. Hence, our proposed pneumatic RoMI-RS not only responds quickly and deforms reversibly but also allows users to intuitively and rapidly reconfigure target shapes. We also demonstrate the applications of RoMI-RS in shape-shifting robotics, particularly in soft grippers and physical human-robot interfaces, verifying its deformation flexibility and adaptability.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"9 12","pages":"10930-10937"},"PeriodicalIF":4.6,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142565560","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

One-Step Identification of Robot Physical Dynamic Parameters Considering the Velocity-Load Friction Model 考虑速度-负载摩擦模型的机器人物理动态参数的一步式识别

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2024-10-21 DOI: 10.1109/LRA.2024.3484133

Yian Qian;Lijin Fang;Jiqian Xu;Tangzhong Song;Guanghui Liu

引用次数: 0

Data-Driven Dynamics Modeling of Miniature Robotic Blimps Using Neural ODEs With Parameter Auto-Tuning 利用带有参数自动调整功能的神经 ODE 建立微型机器人飞艇的数据驱动动力学模型

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2024-10-21 DOI: 10.1109/LRA.2024.3484182

Yongjian Zhu;Hao Cheng;Feitian Zhang

引用次数: 0