Pub Date : 2025-02-11DOI: 10.1109/LRA.2025.3540634
Yi Zhang;Chao Zeng;Jian Zhang;Chenguang Yang
Real-world tasks often require combinations of both discrete and rhythmic movements. However, most of current methods can only address one of them. This letter proposes a unified framework, Wavelet Movement Primitives (WMPs), which are built on Probabilistic Movement Primitives (ProMPs) integrated with Discrete Wavelet Transform (DWT), to model and learn both discrete and rhythmic trajectories from demonstrations. The key advantage of WMPs lies in its ability to naturally identify and facilitate a smooth transition between discrete and rhythmic motions using wavelet transforms. Additionally, we propose local frame WMPs (LF-WMPs) for discrete tasks, enabling the learned movements to generalize to new environments. For rhythmic tasks, a phase-adaptive weight adjustment algorithm is proposed, allowing the system to capture time-frequency features of the demonstrations and safely guide the trajectory back to the desired region. Finally, the method is validated through several simulations and a real-world robotic stirring task, demonstrating its good extrapolation capabilities.
{"title":"Wavelet Movement Primitives: A Unified Framework for Learning Discrete and Rhythmic Movements","authors":"Yi Zhang;Chao Zeng;Jian Zhang;Chenguang Yang","doi":"10.1109/LRA.2025.3540634","DOIUrl":"https://doi.org/10.1109/LRA.2025.3540634","url":null,"abstract":"Real-world tasks often require combinations of both discrete and rhythmic movements. However, most of current methods can only address one of them. This letter proposes a unified framework, Wavelet Movement Primitives (WMPs), which are built on Probabilistic Movement Primitives (ProMPs) integrated with Discrete Wavelet Transform (DWT), to model and learn both discrete and rhythmic trajectories from demonstrations. The key advantage of WMPs lies in its ability to naturally identify and facilitate a smooth transition between discrete and rhythmic motions using wavelet transforms. Additionally, we propose local frame WMPs (LF-WMPs) for discrete tasks, enabling the learned movements to generalize to new environments. For rhythmic tasks, a phase-adaptive weight adjustment algorithm is proposed, allowing the system to capture time-frequency features of the demonstrations and safely guide the trajectory back to the desired region. Finally, the method is validated through several simulations and a real-world robotic stirring task, demonstrating its good extrapolation capabilities.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 4","pages":"3142-3149"},"PeriodicalIF":4.6,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143455245","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}
Pub Date : 2025-02-11DOI: 10.1109/LRA.2025.3540627
Jonáš Kříž;Vojtěch Vonásek
The asymptotically optimal version of Rapidly-exploring Random Tree (RRT*) is often used to find optimal paths in a high-dimensional configuration space. The well-known issue of RRT* is its slow convergence towards the optimal solution. A possible solution is to draw random samples only from a subset of the configuration space that is known to contain configurations that can improve the cost of the path (omniscient set). A fast convergence rate may be achieved by approximating the omniscient with a low-volume set. In this letter, we propose new methods to approximate the omniscient set and methods for their effective sampling. First, we propose to approximate the omniscient set using several (small) hyperellipsoids defined by sections of the current best solution. The second approach approximates the omniscient set by a convex hull computed from the current solution. Both approaches ensure asymptotical optimality and work in a general n-dimensional configuration space. The experiments have shown superior performance of our approaches in multiple scenarios in 3D and 6D configuration spaces.
{"title":"Asymptotically Optimal Path Planning With an Approximation of the Omniscient Set","authors":"Jonáš Kříž;Vojtěch Vonásek","doi":"10.1109/LRA.2025.3540627","DOIUrl":"https://doi.org/10.1109/LRA.2025.3540627","url":null,"abstract":"The asymptotically optimal version of Rapidly-exploring Random Tree (RRT*) is often used to find optimal paths in a high-dimensional configuration space. The well-known issue of RRT* is its slow convergence towards the optimal solution. A possible solution is to draw random samples only from a subset of the configuration space that is known to contain configurations that can improve the cost of the path (omniscient set). A fast convergence rate may be achieved by approximating the omniscient with a low-volume set. In this letter, we propose new methods to approximate the omniscient set and methods for their effective sampling. First, we propose to approximate the omniscient set using several (small) hyperellipsoids defined by sections of the current best solution. The second approach approximates the omniscient set by a convex hull computed from the current solution. Both approaches ensure asymptotical optimality and work in a general n-dimensional configuration space. The experiments have shown superior performance of our approaches in multiple scenarios in 3D and 6D configuration spaces.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 4","pages":"3214-3221"},"PeriodicalIF":4.6,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143465787","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}
Pub Date : 2025-02-11DOI: 10.1109/LRA.2025.3540633
Yeping Wang;Michael Gleicher
End-effector trajectory tracking algorithms find joint motions that drive robot manipulators to track reference trajectories. In practical scenarios, anytime algorithms are preferred for their ability to quickly generate initial motions and continuously refine them over time. In this letter, we present an algorithmic framework that adapts common graph-based trajectory tracking algorithms to be anytime and enhances their efficiency and effectiveness. Our key insight is to identify guide paths that approximately track the reference trajectory and strategically bias sampling toward the guide paths. We demonstrate the effectiveness of the proposed framework by restructuring two existing graph-based trajectory tracking algorithms and evaluating the updated algorithms in three experiments.
{"title":"Anytime Planning for End-Effector Trajectory Tracking","authors":"Yeping Wang;Michael Gleicher","doi":"10.1109/LRA.2025.3540633","DOIUrl":"https://doi.org/10.1109/LRA.2025.3540633","url":null,"abstract":"End-effector trajectory tracking algorithms find joint motions that drive robot manipulators to track reference trajectories. In practical scenarios, anytime algorithms are preferred for their ability to quickly generate initial motions and continuously refine them over time. In this letter, we present an algorithmic framework that adapts common graph-based trajectory tracking algorithms to be anytime and enhances their efficiency and effectiveness. Our key insight is to identify guide paths that approximately track the reference trajectory and strategically bias sampling toward the guide paths. We demonstrate the effectiveness of the proposed framework by restructuring two existing graph-based trajectory tracking algorithms and evaluating the updated algorithms in three experiments.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 4","pages":"3246-3253"},"PeriodicalIF":4.6,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143480790","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}
Pub Date : 2025-02-11DOI: 10.1109/LRA.2025.3540529
Alexander Saikia;Chiara Di Vece;Sierra Bonilla;Chloe He;Morenike Magbagbeola;Laurent Mennillo;Tobias Czempiel;Sophia Bano;Danail Stoyanov
Minimally invasive surgery (MIS) offers significant benefits, such as reduced recovery time and minimised patient trauma, but poses challenges in visibility and access, making accurate 3D reconstruction a significant tool in surgical planning and navigation. This work introduces a robotic arm platform for efficient multi-view image acquisition and precise 3D reconstruction in MIS settings. We adapted a laparoscope to a robotic arm and captured ex-vivo images of several ovine organs across varying lighting conditions (operating room and laparoscopic) and trajectories (spherical and laparoscopic). We employed recently released learning-based feature matchers combined with COLMAP to produce our reconstructions. The reconstructions were evaluated against high-precision laser scans for quantitative evaluation. Our results show that whilst reconstructions suffer most under realistic MIS lighting and trajectory, two matching methods achieve close to sub-millimetre accuracy with 0.80 and 0.76 mm Chamfer distances and 1.06 and 0.98 mm RMSEs for ALIKED and GIM respectively. Our best reconstruction results occur with operating room lighting and spherical trajectories. Our robotic platform provides a tool for controlled, repeatable multi-view data acquisition for 3D generation in MIS environments, which can lead to new datasets necessary for novel learning-based surgical models.
{"title":"Robotic Arm Platform for Multi-View Image Acquisition and 3D Reconstruction in Minimally Invasive Surgery","authors":"Alexander Saikia;Chiara Di Vece;Sierra Bonilla;Chloe He;Morenike Magbagbeola;Laurent Mennillo;Tobias Czempiel;Sophia Bano;Danail Stoyanov","doi":"10.1109/LRA.2025.3540529","DOIUrl":"https://doi.org/10.1109/LRA.2025.3540529","url":null,"abstract":"Minimally invasive surgery (MIS) offers significant benefits, such as reduced recovery time and minimised patient trauma, but poses challenges in visibility and access, making accurate 3D reconstruction a significant tool in surgical planning and navigation. This work introduces a robotic arm platform for efficient multi-view image acquisition and precise 3D reconstruction in MIS settings. We adapted a laparoscope to a robotic arm and captured <italic>ex-vivo</i> images of several ovine organs across varying lighting conditions (operating room and laparoscopic) and trajectories (spherical and laparoscopic). We employed recently released learning-based feature matchers combined with COLMAP to produce our reconstructions. The reconstructions were evaluated against high-precision laser scans for quantitative evaluation. Our results show that whilst reconstructions suffer most under realistic MIS lighting and trajectory, two matching methods achieve close to sub-millimetre accuracy with 0.80 and 0.76 mm Chamfer distances and 1.06 and 0.98 mm RMSEs for ALIKED and GIM respectively. Our best reconstruction results occur with operating room lighting and spherical trajectories. Our robotic platform provides a tool for controlled, repeatable multi-view data acquisition for 3D generation in MIS environments, which can lead to new datasets necessary for novel learning-based surgical models.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 4","pages":"3174-3181"},"PeriodicalIF":4.6,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143464355","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}
Pub Date : 2025-02-11DOI: 10.1109/LRA.2025.3541459
Rohan Khatavkar;The Bach Nguyen;Yuanhao Chen;Hyunglae Lee;Jiefeng Sun
Back support devices(BSDs) have the potential to mitigate overexertion in industrial tasks and also to provide assistance to people with weak back muscle strength in daily activity. While state-of-the-art active BSDs can offer a high assistive force, they are bulky and heavy, making them uncomfortable for daily use. On the contrary, passive BSDs are compact but need manual adjustment to be versatile. This work presents a hybrid soft BSD that can provide task-oriented assistance by tuning the stiffness (0.58 N/mm, 0.92 N/mm, and 1.7 N/mm) and slack length (0 mm to 67 mm) in a compact design. The tunable stiffness allows for selecting a task-specific force profile, and the slack tuning will ensure that the device enables unhindered movement when assistance is not required. Compared with rigid devices, the device's compliance can potentially increase human comfort. We propose an analytical model that facilitates device design and estimates the device performance. Furthermore, the device's tuning capabilities are evaluated in human squatting and stooping experiments, showing that the desired force profile is correctly applied.
{"title":"A Hybrid Variable-Stiffness Soft Back Support Device","authors":"Rohan Khatavkar;The Bach Nguyen;Yuanhao Chen;Hyunglae Lee;Jiefeng Sun","doi":"10.1109/LRA.2025.3541459","DOIUrl":"https://doi.org/10.1109/LRA.2025.3541459","url":null,"abstract":"Back support devices(BSDs) have the potential to mitigate overexertion in industrial tasks and also to provide assistance to people with weak back muscle strength in daily activity. While state-of-the-art active BSDs can offer a high assistive force, they are bulky and heavy, making them uncomfortable for daily use. On the contrary, passive BSDs are compact but need manual adjustment to be versatile. This work presents a hybrid soft BSD that can provide task-oriented assistance by tuning the stiffness (0.58 N/mm, 0.92 N/mm, and 1.7 N/mm) and slack length (0 mm to 67 mm) in a compact design. The tunable stiffness allows for selecting a task-specific force profile, and the slack tuning will ensure that the device enables unhindered movement when assistance is not required. Compared with rigid devices, the device's compliance can potentially increase human comfort. We propose an analytical model that facilitates device design and estimates the device performance. Furthermore, the device's tuning capabilities are evaluated in human squatting and stooping experiments, showing that the desired force profile is correctly applied.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 4","pages":"3238-3245"},"PeriodicalIF":4.6,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143480791","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}
Pub Date : 2025-02-11DOI: 10.1109/LRA.2025.3540579
Kenta Yokoe;Yuki Funabora;Tadayoshi Aoyama
Hand positional guidance with intuitive perception is crucial for enhancing user interaction and task performance in immersive environments. However, conventional hand positional guidance methods, relying on tactile sensations, lack intuitiveness. Consequently, users require instruction on the relationship between the tactile sensation and target position of the guidance before using these methods. Additionally, the user needs training to become familiar with tactile sensations. This study presents a hand positional guidance system with intuitive perception that leverages McKibben-based surface tactile sensations directed to the shoulder and elbow. We developed a wearable fabric actuator that provides McKibben-based surface tactile sensations to induce six specific movements: elbow flexion, extension, shoulder abduction, adduction, horizontal abduction, and horizontal adduction. The effectiveness of the actuator was experimentally validated, demonstrating its high accuracy in intuitively inducing six movements. An algorithm based on the equilibrium point hypothesis and Weber–Fechner law was implemented to regulate the intensity of the tactile sensations for hand positional guidance. Furthermore, the accuracy and speed of the proposed system were compared with that of conventional guidance methods utilizing synthesized speech and vibrotactile guidance.
{"title":"Intuitive Hand Positional Guidance Using McKibben-Based Surface Tactile Sensations to Shoulder and Elbow","authors":"Kenta Yokoe;Yuki Funabora;Tadayoshi Aoyama","doi":"10.1109/LRA.2025.3540579","DOIUrl":"https://doi.org/10.1109/LRA.2025.3540579","url":null,"abstract":"Hand positional guidance with intuitive perception is crucial for enhancing user interaction and task performance in immersive environments. However, conventional hand positional guidance methods, relying on tactile sensations, lack intuitiveness. Consequently, users require instruction on the relationship between the tactile sensation and target position of the guidance before using these methods. Additionally, the user needs training to become familiar with tactile sensations. This study presents a hand positional guidance system with intuitive perception that leverages McKibben-based surface tactile sensations directed to the shoulder and elbow. We developed a wearable fabric actuator that provides McKibben-based surface tactile sensations to induce six specific movements: elbow flexion, extension, shoulder abduction, adduction, horizontal abduction, and horizontal adduction. The effectiveness of the actuator was experimentally validated, demonstrating its high accuracy in intuitively inducing six movements. An algorithm based on the equilibrium point hypothesis and Weber–Fechner law was implemented to regulate the intensity of the tactile sensations for hand positional guidance. Furthermore, the accuracy and speed of the proposed system were compared with that of conventional guidance methods utilizing synthesized speech and vibrotactile guidance.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 4","pages":"3254-3261"},"PeriodicalIF":4.6,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10879806","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143480787","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}
This letter considers a generalization of the Path Finding (PF) problem with refuelling constraints referred to as the Gas Station Problem (GSP). Similar to PF, given a graph where vertices are gas stations with known fuel prices, and edge costs are the gas consumption between the two vertices, GSPseeks a minimum-cost path from the start to the goal vertex for a robot with a limited gas tank and a limited number of refuelling stops. While GSPis polynomial-time solvable, it remains a challenge to quickly compute an optimal solution in practice since it requires simultaneously determine the path, where to make the stops, and the amount to refuel at each stop. This letter develops a heuristic search algorithm called $text{Refuel A}^*$ ($text{RF-A}^*$) that iteratively constructs partial solution paths from the start to the goal guided by a heuristic while leveraging dominance rules for pruning during planning. $text{RF-A}^*$is guaranteed to find an optimal solution and often runs 2 to 8 times faster than the existing approaches in large city maps with several hundreds of gas stations.
{"title":"Heuristic Search for Path Finding With Refuelling","authors":"Shizhe Zhao;Anushtup Nandy;Howie Choset;Sivakumar Rathinam;Zhongqiang Ren","doi":"10.1109/LRA.2025.3540736","DOIUrl":"https://doi.org/10.1109/LRA.2025.3540736","url":null,"abstract":"This letter considers a generalization of the Path Finding (PF) problem with refuelling constraints referred to as the Gas Station Problem (GSP). Similar to PF, given a graph where vertices are gas stations with known fuel prices, and edge costs are the gas consumption between the two vertices, GSPseeks a minimum-cost path from the start to the goal vertex for a robot with a limited gas tank and a limited number of refuelling stops. While GSPis polynomial-time solvable, it remains a challenge to quickly compute an optimal solution in practice since it requires simultaneously determine the path, where to make the stops, and the amount to refuel at each stop. This letter develops a heuristic search algorithm called <inline-formula><tex-math>$text{Refuel A}^*$</tex-math></inline-formula> (<inline-formula><tex-math>$text{RF-A}^*$</tex-math></inline-formula>) that iteratively constructs partial solution paths from the start to the goal guided by a heuristic while leveraging dominance rules for pruning during planning. <inline-formula><tex-math>$text{RF-A}^*$</tex-math></inline-formula>is guaranteed to find an optimal solution and often runs 2 to 8 times faster than the existing approaches in large city maps with several hundreds of gas stations.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 4","pages":"3230-3237"},"PeriodicalIF":4.6,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143480788","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}
Pub Date : 2025-02-11DOI: 10.1109/LRA.2025.3540531
Patrick Pastorelli;Simone Dagnino;Enrico Saccon;Marco Frego;Luigi Palopoli
In this work, we propose the Dubins Path Smoothing (DPS) algorithm, a novel and efficient method for smoothing polylines in motion planning tasks. DPS applies to motion planning of vehicles with bounded curvature. In the letter, we show that the generated path: 1) has minimal length, 2) is $G^{1}$ continuous, and 3) is collision-free by construction, under mild hypotheses. We compare our solution with the state-of-the-art and show its convenience both in terms of computation time and of length of the compute path.
{"title":"Fast Shortest Path Polyline Smoothing With $G^{1}$ Continuity and Bounded Curvature","authors":"Patrick Pastorelli;Simone Dagnino;Enrico Saccon;Marco Frego;Luigi Palopoli","doi":"10.1109/LRA.2025.3540531","DOIUrl":"https://doi.org/10.1109/LRA.2025.3540531","url":null,"abstract":"In this work, we propose the Dubins Path Smoothing (DPS) algorithm, a novel and efficient method for smoothing polylines in motion planning tasks. DPS applies to motion planning of vehicles with bounded curvature. In the letter, we show that the generated path: 1) has minimal length, 2) is <inline-formula><tex-math>$G^{1}$</tex-math></inline-formula> continuous, and 3) is collision-free by construction, under mild hypotheses. We compare our solution with the state-of-the-art and show its convenience both in terms of computation time and of length of the compute path.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 4","pages":"3182-3189"},"PeriodicalIF":4.6,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143465785","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}
Pub Date : 2025-02-10DOI: 10.1109/LRA.2025.3540379
Hang Zhou;Lu Yao;Haoping She;Weiyong Si
Utilizing deep learning techniques for spacecraft pose estimation enables using low-cost sensors like monocular cameras. However, the existing methods have drawbacks, such as complex models or low estimation accuracy. Therefore, this letter proposes the Spacecraft Discrete Pose Estimation Network (SDPENet). Firstly, we design a feature fusion network and a pose estimation head applicable to the spacecraft pose estimation task and devise the Spatial-Semantic Interaction Attention (SSIA) mechanism for feature fusion. Secondly, the discrete Euler angle probability distribution is proposed to represent the spacecraft attitude, significantly reducing the number of parameters while improving the accuracy. Finally, we put forward three data augmentation methods named CropAndPad, DropBlockSafe and Z-axis Rotation Safe to improve the performance of the network for the spacecraft pose estimation task. The experimental results demonstrate that, compared with the existing works, the errors in the spacecraft position and attitude estimated by SDPENet are reduced by 8.7%–83.1% and 31.7%–87.8% respectively, and simultaneously, the number of parameters is decreased by 33.3%–82.4%.
{"title":"SDPENet: A Lightweight Spacecraft Pose Estimation Network With Discrete Euler Angle Probability Distribution","authors":"Hang Zhou;Lu Yao;Haoping She;Weiyong Si","doi":"10.1109/LRA.2025.3540379","DOIUrl":"https://doi.org/10.1109/LRA.2025.3540379","url":null,"abstract":"Utilizing deep learning techniques for spacecraft pose estimation enables using low-cost sensors like monocular cameras. However, the existing methods have drawbacks, such as complex models or low estimation accuracy. Therefore, this letter proposes the Spacecraft Discrete Pose Estimation Network (SDPENet). Firstly, we design a feature fusion network and a pose estimation head applicable to the spacecraft pose estimation task and devise the Spatial-Semantic Interaction Attention (SSIA) mechanism for feature fusion. Secondly, the discrete Euler angle probability distribution is proposed to represent the spacecraft attitude, significantly reducing the number of parameters while improving the accuracy. Finally, we put forward three data augmentation methods named CropAndPad, DropBlockSafe and Z-axis Rotation Safe to improve the performance of the network for the spacecraft pose estimation task. The experimental results demonstrate that, compared with the existing works, the errors in the spacecraft position and attitude estimated by SDPENet are reduced by 8.7%–83.1% and 31.7%–87.8% respectively, and simultaneously, the number of parameters is decreased by 33.3%–82.4%.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 4","pages":"3086-3093"},"PeriodicalIF":4.6,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143446264","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}
Pub Date : 2025-02-10DOI: 10.1109/LRA.2025.3540383
Arjun Kumar;Thales C. Silva;Victoria Edwards;M. Ani Hsieh
This letter addresses the problem of Multi-Robot Simultaneous Localization and Mapping (SLAM) in dynamic feature-free marine environments. Traditional SLAM approaches rely on static environmental features, which are often scarce in marine environments, hindering their applicability in aquatic environments like rivers, lakes, and oceans. We propose a localization and mapping formulation that jointly optimizes robot odometry, relative robot bearings, and estimates of dynamic environmental flow parameters using state-of-the-art parameter estimation techniques like Sparse Identification of Nonlinear Dynamics (SINDy) (Brunton et al., 2016). Our approach not only provides an accurate flow field map but it also enhances pose estimation of multiple minimally actuated robots transported by the flow (Subbaraya et al., 2016), (Molchanov et al., 2015). We showcase our methodology on a series of increasingly dynamically complex flow fields including the Duffing oscillator, the wind-driven double-gyre, and real ocean data from the Gulf of Mexico.
{"title":"Flow-Based Localization and Mapping for Multi-Robot Systems","authors":"Arjun Kumar;Thales C. Silva;Victoria Edwards;M. Ani Hsieh","doi":"10.1109/LRA.2025.3540383","DOIUrl":"https://doi.org/10.1109/LRA.2025.3540383","url":null,"abstract":"This letter addresses the problem of Multi-Robot Simultaneous Localization and Mapping (SLAM) in dynamic feature-free marine environments. Traditional SLAM approaches rely on static environmental features, which are often scarce in marine environments, hindering their applicability in aquatic environments like rivers, lakes, and oceans. We propose a localization and mapping formulation that jointly optimizes robot odometry, relative robot bearings, and estimates of dynamic environmental flow parameters using state-of-the-art parameter estimation techniques like Sparse Identification of Nonlinear Dynamics (SINDy) (Brunton et al., 2016). Our approach not only provides an accurate flow field map but it also enhances pose estimation of multiple minimally actuated robots transported by the flow (Subbaraya et al., 2016), (Molchanov et al., 2015). We showcase our methodology on a series of increasingly dynamically complex flow fields including the Duffing oscillator, the wind-driven double-gyre, and real ocean data from the Gulf of Mexico.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 4","pages":"3278-3285"},"PeriodicalIF":4.6,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143480789","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}
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