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

Adaptive-Risk-Aware End-to-End Control System for Mapless Navigation of Drones 无人机无地图导航自适应风险感知端到端控制系统

IF 5.3 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-09-05 DOI: 10.1109/LRA.2025.3606805

Zhu Wang;Wei Li;Jiaxiang Gan;Guangtong Xu;Guoyu Zhang;Zengzhi Li;Tianning Wang

{"title":"Adaptive-Risk-Aware End-to-End Control System for Mapless Navigation of Drones","authors":"Zhu Wang;Wei Li;Jiaxiang Gan;Guangtong Xu;Guoyu Zhang;Zengzhi Li;Tianning Wang","doi":"10.1109/LRA.2025.3606805","DOIUrl":"https://doi.org/10.1109/LRA.2025.3606805","url":null,"abstract":"Safe and autonomous navigation of drones in 3D complex unknown environments remains a current research hotspot. A critical challenge is how to adaptively balance the safety with flight aggressiveness, fully exploiting the motion potential of the drone. This letter proposes an Adaptive-Risk-Aware End-to-End (ARA-E2E) control system which can generate real-time control commands capable of achieving adaptive balance in 3D environments. The system utilizes the Deterministic Implicit Quantile Network (DIQN) algorithm under the Actor-Critic framework, directly generating acceleration commands from LiDAR data. In the critic network, the Implicit Quantile Networks (IQN) algorithm, which integrates a risk-aware module, is employed. IQN evaluates actions with varying tendencies toward flight speed and risk, by calculating the parameter of Conditional Value-at-Risk (CVaR) based on the current obstacle environment. In the actor network, a continuous state space representation method is designed to provide more flexible control commands. The comparative experiments show that the proposed method outperforms in flight time, success rate, and energy consumption in the forest scenario. To further validate the effectiveness of the system, we deploy it on an autonomous drone platform, and flight tests are conducted in an outdoor forest environment. The drone can successfully complete the real-world navigation task at an average speed of 2.5 m/s (with a maximum of 5 m/s in the simulation), demonstrating the practicality and reliability of the proposed method.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 10","pages":"10990-10997"},"PeriodicalIF":5.3,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145061813","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

VDS-Nav: Volumetric Depth-Based Safe Navigation for Aerial Robots–Bridging the Sim-to-Real Gap VDS-Nav：基于体积深度的空中机器人安全导航——弥合模拟与真实的差距

IF 5.3 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-09-05 DOI: 10.1109/LRA.2025.3606806

Van Huyen Dang;Adrian Redder;Huy Xuan Pham;Andriy Sarabakha;Erdal Kayacan

{"title":"VDS-Nav: Volumetric Depth-Based Safe Navigation for Aerial Robots–Bridging the Sim-to-Real Gap","authors":"Van Huyen Dang;Adrian Redder;Huy Xuan Pham;Andriy Sarabakha;Erdal Kayacan","doi":"10.1109/LRA.2025.3606806","DOIUrl":"https://doi.org/10.1109/LRA.2025.3606806","url":null,"abstract":"End-to-end navigation via deep reinforcement learning has become a key approach for vision-based tasks. However, the sim-to-real gap remains a challenge, especially for aerial robots, where policies trained in simulation often fail in real-world environments. In this work, we propose a novel navigation paradigm – volumetric depth-based safe navigation (VDS-Nav), which trains a policy to infer linear velocities and yaw rate directly from a sequence of depth images, bypassing the need for a pre-trained latent space encoder. We enhance safety with a depth-based reward design, enabling the seamless incorporation of system constraints via logarithmic barrier function methods. Most importantly, using explicit sensor information in our reward design leads to seamless sim-to-real transfer by strengthening the correlation between state-action pairs and received rewards. To evaluate the effectiveness of VDS-Nav, we compare it to a baseline that first trains a variational autoencoder to encode depth images into a latent space for policy training. The simulation results show that VDS-Nav outperforms the baseline in terms of success rate. Furthermore, real-world experiments validate the policy, with real-time performance closely matching simulation results, suggesting an effective sim-to-real transfer.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 10","pages":"11038-11045"},"PeriodicalIF":5.3,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145073148","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

Aleatoric Uncertainty From AI-Based 6D Object Pose Predictors for Object-Relative State Estimation 基于人工智能的6D物体姿态预测器的任意不确定性

IF 5.3 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-09-05 DOI: 10.1109/LRA.2025.3606700

Thomas Jantos;Stephan Weiss;Jan Steinbrener

{"title":"Aleatoric Uncertainty From AI-Based 6D Object Pose Predictors for Object-Relative State Estimation","authors":"Thomas Jantos;Stephan Weiss;Jan Steinbrener","doi":"10.1109/LRA.2025.3606700","DOIUrl":"https://doi.org/10.1109/LRA.2025.3606700","url":null,"abstract":"Deep Learning (DL) has become essential in various robotics applications due to excelling at processing raw sensory data to extract task specific information from semantic objects. For example, vision-based object-relative navigation relies on a DL-based 6D object pose predictor to provide the relative pose between the object and the robot as measurements to the robot's state estimator. Accurately knowing the uncertainty inherent in such Deep Neural Network (DNN) based measurements is essential for probabilistic state estimators subsequently guiding the robot's tasks. Thus, in this letter, we show that we can extend any existing DL-based object-relative pose predictor for aleatoric uncertainty inference simply by including two multi-layer perceptrons detached from the translational and rotational part of the DL predictor. This allows for efficient training while freezing the existing pre-trained predictor. We then use the inferred 6D pose and its uncertainty as a measurement and corresponding noise covariance matrix in an extended Kalman filter (EKF). Our approach induces minimal computational overhead such that the state estimator can be deployed on edge devices while benefiting from the dynamically inferred measurement uncertainty. This increases the performance of the object-relative state estimation task compared to a fix-covariance approach. We conduct evaluations on synthetic data and real-world data to underline the benefits of aleatoric uncertainty inference for the object-relative state estimation task.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 10","pages":"10902-10909"},"PeriodicalIF":5.3,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11152309","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145061956","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

Towards Equilibrium Coordination With Stein Variational Game 基于Stein变分对策的均衡协调

IF 5.3 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-09-05 DOI: 10.1109/LRA.2025.3606795

Zhiyuan Zhang;Panagiotis Tsiotras

{"title":"Towards Equilibrium Coordination With Stein Variational Game","authors":"Zhiyuan Zhang;Panagiotis Tsiotras","doi":"10.1109/LRA.2025.3606795","DOIUrl":"https://doi.org/10.1109/LRA.2025.3606795","url":null,"abstract":"Differential Dynamic Games (DDG) often admit non-unique and, in some cases, an infinite number of Nash Equilibria (NE). Miscoordination among agents in selecting a specific Nash Equilibrium can result in cost inefficiencies and compromise any stabilizing properties of these equilibria. In the case of Generalized Nash Equilibria (GNE), such miscoordination can lead to constraint violations, potentially causing catastrophic outcomes, such as collisions in practical applications. To address the equilibrium coordination problem, we propose a novel framework for estimating and selecting equilibria in a DDG. Our method utilizes a diffusion process to model the distribution of Nash Equilibria in a DDG, constructed through a sequence of mappings defined by Stein Variational Gradient Descent (SVGD). This distribution serves as a prior belief over the non-ego agents' equilibrium choices, which is dynamically refined using Bayesian inference as the game evolves. We validate the proposed approach in an autonomous vehicle traffic trajectory planning problem, demonstrating its effectiveness in environments where agents operate without explicit communication regarding their chosen Nash Equilibria.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 11","pages":"11220-11226"},"PeriodicalIF":5.3,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145090006","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

MPC-ABCO: An MPC-Based Adaptive Bezier Curve Optimization Framework for UAV-UGV Cooperative Landing 基于MPC-ABCO的无人机- ugv协同着陆自适应Bezier曲线优化框架

IF 5.3 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-09-05 DOI: 10.1109/LRA.2025.3606796

Haiqi Li;Lei Qiang;Zihao Wu;Jiajun Chen;Yinsong Sun;Xinbo Li

{"title":"MPC-ABCO: An MPC-Based Adaptive Bezier Curve Optimization Framework for UAV-UGV Cooperative Landing","authors":"Haiqi Li;Lei Qiang;Zihao Wu;Jiajun Chen;Yinsong Sun;Xinbo Li","doi":"10.1109/LRA.2025.3606796","DOIUrl":"https://doi.org/10.1109/LRA.2025.3606796","url":null,"abstract":"This letter addresses the approach of enabling a UAV to autonomously land on an agile UGV that moves irregularly with sudden accelerations, decelerations or abrupt turns, where existing methods have limitations in handling nonlinear dynamics and visual recognition errors in high-dynamic situations. To overcome these limitations, we present a real-time UAV-UGV cooperative landing framework that integrates Model Predictive Control and Adaptive Bezier Curve Optimization (MPC-ABCO) to achieve UGV's position prediction and UAV's trajectory forecasting, ensuring robust performance in high-dynamic scenarios. MPC-ABCO utilizes an MPC model to predict the UGV's future states by leveraging odometry data to model UGV's kinematic dynamics. Concurrently, the UAV's landing trajectory is optimized by Bezier curves, with waypoints dynamically updated by considering UGV's relative future positions detected by AprilTag code. Experimental results demonstrate that the proposed framework achieves an average landing deviation below 5 cm for UGV's steering velocities up to 5 m/s under no-acceleration stations. When the UGV follows irregular paths with random accelerations, MPC-ABCO outperforms traditional strategies in both landing success rate and accuracy, maintaining reliable performance at maximum velocities of 5 m/s.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 11","pages":"11134-11140"},"PeriodicalIF":5.3,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145073309","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

Flexible Electromagnetic Fin: Accurate Electromechanical Modeling for Agile Robotic Design 柔性电磁鳍：面向敏捷机器人设计的精确机电建模

IF 5.3 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-09-04 DOI: 10.1109/LRA.2025.3606391

Zhe Wang;Tao Feng;Letian Gan;Hongcheng Qiu;Yanzhao Shi;Yuanlong Zhou;Yonghuan Li;Xinge Li;Fanghao Zhou;Tiefeng Li

{"title":"Flexible Electromagnetic Fin: Accurate Electromechanical Modeling for Agile Robotic Design","authors":"Zhe Wang;Tao Feng;Letian Gan;Hongcheng Qiu;Yanzhao Shi;Yuanlong Zhou;Yonghuan Li;Xinge Li;Fanghao Zhou;Tiefeng Li","doi":"10.1109/LRA.2025.3606391","DOIUrl":"https://doi.org/10.1109/LRA.2025.3606391","url":null,"abstract":"Bionic fish fins can significantly improve propulsion efficiency, maneuverability, and environmental adaptability in underwater robots. However, limitation exists in those bionic-fins-based robots to achieve high performance. For example, motor-actuated fins generate substantial thrust but are excessively bulky, whereas artificial muscle-driven fins offer biomimetic flexibility yet yield insufficient thrust. In this study, we introduce a miniaturized flexible electromagnetic fin that features high magnetic energy density, compact structure, frictionless elastic joint, and the ability to visually measure the electromagnetic torque. We also develop an electromechanical model that accurately predicts the thrust from an input current by incorporating underwater vibration dynamics. The model is based on three experimentally identified parameters: the electromagnetic torque constant, the underwater damping constant, and the added mass constant. In underwater experiments, the fin achieves a peak thrust of 493 mN, which is remarkable for a bio-inspired fin weighing only 17.2 g. Furthermore, a self-powered robotic fish equipped with this fin reaches a maximum swimming speed of 405.5 mm/s (1.66 body lengths per second) within 3 s, and attains a minimum turning radius of 210 mm (0.86 body lengths). These results provide a reliable theoretical and experimental basis for the design of agile robotic fish powered by flexible bionic fins.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 11","pages":"11070-11077"},"PeriodicalIF":5.3,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145073233","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

Distributed Multi-Robot Active-Sensing of a Diffusive Source 一种扩散源分布式多机器人主动传感

IF 5.3 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-09-04 DOI: 10.1109/LRA.2025.3606376

Francesca Pagano;Nicola De Carli;Esteban Restrepo;Antonio Marino;Paolo Robuffo Giordano

引用次数: 0

Bundle Adjustment With Backtracking Line Search on Manifold 集形上带回溯线搜索的集形调整

IF 5.3 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-09-04 DOI: 10.1109/LRA.2025.3606801

Lipu Zhou

{"title":"Bundle Adjustment With Backtracking Line Search on Manifold","authors":"Lipu Zhou","doi":"10.1109/LRA.2025.3606801","DOIUrl":"https://doi.org/10.1109/LRA.2025.3606801","url":null,"abstract":"Bundle adjustment (BA) is a fundamental problem in visual 3D reconstruction. The Levenberg-Marquardt (LM) algorithm, a trust region method, is widely regarded as the gold standard for solving BA problems. In each LM iteration, the current solution is updated by an increment vector derived from solving a linear system with a damping factor to regularize the step size. However, directly applying this increment may fail to reduce the reprojection cost. To address this problem, the LM algorithm employs a trial-and-error strategy. Specifically, it repeatedly solves the linear system with an increasing damping factor until the cost decreases. This process leads to invalid iterations. Since solving the linear system is typically the most time-consuming step and a large damping factor limits the step size in the subsequent iterations, this strategy wastes computational resources and slows down convergence. However, this issue has received little attention in prior research on BA. On the other hand, line search offers an alternative technique to control the step size, however, its application to BA remains underexplored. This letter presents a simple yet effective solution to overcome the limitation of the LM algorithm. We introduce on-manifold backtracking line search into the LM algorithm to accelerate convergence. The Armijo condition is adopted to ensure a sufficient decrease in reprojection cost. We show that the Armijo condition on manifold can be efficiently computed in the LM framework. By fusing line search and the LM algorithm to control the step size, our method effectively reduces the number of invalid iterations and improves convergence speed. Extensive empirical evaluations on both unstructured internet image collections and sequential image streams show that our algorithm converges significantly faster compared to state-of-the-art BA algorithms.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 10","pages":"10998-11005"},"PeriodicalIF":5.3,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145061966","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

LIVOX-CAM: Adaptive Coarse-to-Fine Visual-Assisted LiDAR Odometry for Solid-State LiDAR LIVOX-CAM：用于固体激光雷达的自适应粗到精视觉辅助激光雷达里程计

IF 5.3 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-09-04 DOI: 10.1109/LRA.2025.3606803

Xiaolong Cheng;Keke Geng;Zhichao Liu;Tianxiao Ma;Ye Sun

引用次数: 0

Robust Moving Horizon Estimation for Autonomous Agricultural Vehicles With GNSS Outliers Using a Robust Loss Function 基于鲁棒损失函数的GNSS离群值自动农用车鲁棒运动地平线估计

IF 5.3 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-09-04 DOI: 10.1109/LRA.2025.3606377

Nestor N. Deniz;Guido M. Sanchez;Fernando A. Auat Cheein;Leonardo L. Giovanini

{"title":"Robust Moving Horizon Estimation for Autonomous Agricultural Vehicles With GNSS Outliers Using a Robust Loss Function","authors":"Nestor N. Deniz;Guido M. Sanchez;Fernando A. Auat Cheein;Leonardo L. Giovanini","doi":"10.1109/LRA.2025.3606377","DOIUrl":"https://doi.org/10.1109/LRA.2025.3606377","url":null,"abstract":"We propose a Moving Horizon Estimator (MHE) for autonomous agricultural vehicles to handle GNSS outliers, a common issue in farming. To improve robustness, we replace the standard <inline-formula><tex-math>$mathrm{L_{2}}$</tex-math></inline-formula> stage cost with a loss function based on the square of the derivative of the General Adaptive Robust Loss (GARL). The GARL framework, controlled by parameters <inline-formula><tex-math>$alpha in [1,,2)$</tex-math></inline-formula> and <inline-formula><tex-math>$c > 0$</tex-math></inline-formula>, balances between quadratic and outlier-resistant behavior. By using the derivative, we avoid singularities at <inline-formula><tex-math>$alpha = 0$</tex-math></inline-formula> and <inline-formula><tex-math>$alpha = 2$</tex-math></inline-formula>, simplifying tuning and ensuring stable optimization within MHE. This approach retains the flexibility of GARL while narrowing the design space to a singularity-free regime. We prove robust stability under standard assumptions. Simulations show our method outperforms <inline-formula><tex-math>$mathrm{L_{2}}$</tex-math></inline-formula>-based MHE and state-of-the-art methods, rejecting GNSS outliers. Field experiments validate its practical effectiveness.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 10","pages":"10815-10821"},"PeriodicalIF":5.3,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145036904","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