Pub Date : 2025-01-08DOI: 10.1109/LRA.2025.3527280
Anas Mahdi;Zonghao Dong;Jonathan Feng-Shun Lin;Yue Hu;Yasuhisa Hirata;Katja Mombaur
Sit-to-stand (STS) transfer is a fundamental but challenging movement that plays a vital role in older adults' daily activities. The decline in muscular strength and coordination ability can result in difficulties performing STS and, therefore, the need for mobility assistance by humans or assistive devices. Robotics rollators are being developed to provide active mobility assistance to older adults, including STS assistance. In this paper, we consider the robotic walker SkyWalker, which can provide active STS assistance by moving the handles upwards and forward to bring the user to a standing configuration. In this context, it is crucial to monitor if the user is performing the STS and adapt the rollator's control accordingly. To achieve this, we utilized a standard vision-based method for estimating the human pose during the STS movement using Mediapipe pose tracking. Since estimating a user's state from extreme proximity to the camera is challenging, we compared the pose identification results from Mediapipe to ground truth data obtained from Vicon marker-based motion capture to assess accuracy and reliability of the STS motion. The fourteen kinematic features critical for accurate pose estimation were selected based on literature review and the specific requirements of our robot's STS method. By employing these features, we have implemented a phase classification system that enables the SkyWalker to classify the user's STS phase in real-time. The selected kinematics from vision-based human state estimation method and trained classifier can be furthermore generalized to other types of motion support, including adaptive STS path planning and emergency stops for safety insurance during STS.
{"title":"Real-Time Sit-to-Stand Phase Classification With a Mobile Assistive Robot From Close Proximity Utilizing 3D Visual Skeleton Recognition","authors":"Anas Mahdi;Zonghao Dong;Jonathan Feng-Shun Lin;Yue Hu;Yasuhisa Hirata;Katja Mombaur","doi":"10.1109/LRA.2025.3527280","DOIUrl":"https://doi.org/10.1109/LRA.2025.3527280","url":null,"abstract":"Sit-to-stand (STS) transfer is a fundamental but challenging movement that plays a vital role in older adults' daily activities. The decline in muscular strength and coordination ability can result in difficulties performing STS and, therefore, the need for mobility assistance by humans or assistive devices. Robotics rollators are being developed to provide active mobility assistance to older adults, including STS assistance. In this paper, we consider the robotic walker SkyWalker, which can provide active STS assistance by moving the handles upwards and forward to bring the user to a standing configuration. In this context, it is crucial to monitor if the user is performing the STS and adapt the rollator's control accordingly. To achieve this, we utilized a standard vision-based method for estimating the human pose during the STS movement using Mediapipe pose tracking. Since estimating a user's state from extreme proximity to the camera is challenging, we compared the pose identification results from Mediapipe to ground truth data obtained from Vicon marker-based motion capture to assess accuracy and reliability of the STS motion. The fourteen kinematic features critical for accurate pose estimation were selected based on literature review and the specific requirements of our robot's STS method. By employing these features, we have implemented a phase classification system that enables the SkyWalker to classify the user's STS phase in real-time. The selected kinematics from vision-based human state estimation method and trained classifier can be furthermore generalized to other types of motion support, including adaptive STS path planning and emergency stops for safety insurance during STS.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 3","pages":"2160-2167"},"PeriodicalIF":4.6,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993211","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 : 2025-01-08DOI: 10.1109/LRA.2025.3527337
Amy Fang;Tenny Yin;Hadas Kress-Gazit
Given a collaborative high-level task and a team of heterogeneous robots with behaviors to satisfy it, this work focuses on the challenge of automatically adjusting the individual robot behaviors at runtime such that the task is still satisfied. We specifically address scenarios when robots encounter changes to their abilities–either failures or additional actions they can perform. We aim to minimize global teaming reassignments (and as a result, local resynthesis) when robots' capabilities change. The tasks are encoded in LTL$^psi$, an extension of LTL introduced in our prior work. We increase the expressivity of LTL$^psi$ by including additional types of constraints on the overall teaming assignment that the user can specify, such as the minimum number of robots required for each assignment. We demonstrate the framework in a simulated warehouse scenario.
{"title":"Online Resynthesis of High-Level Collaborative Tasks for Robots With Changing Capabilities","authors":"Amy Fang;Tenny Yin;Hadas Kress-Gazit","doi":"10.1109/LRA.2025.3527337","DOIUrl":"https://doi.org/10.1109/LRA.2025.3527337","url":null,"abstract":"Given a collaborative high-level task and a team of heterogeneous robots with behaviors to satisfy it, this work focuses on the challenge of automatically adjusting the individual robot behaviors at runtime such that the task is still satisfied. We specifically address scenarios when robots encounter changes to their abilities–either failures or additional actions they can perform. We aim to minimize global teaming reassignments (and as a result, local resynthesis) when robots' capabilities change. The tasks are encoded in LTL<inline-formula><tex-math>$^psi$</tex-math></inline-formula>, an extension of LTL introduced in our prior work. We increase the expressivity of LTL<inline-formula><tex-math>$^psi$</tex-math></inline-formula> by including additional types of constraints on the overall teaming assignment that the user can specify, such as the minimum number of robots required for each assignment. We demonstrate the framework in a simulated warehouse scenario.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 2","pages":"2032-2039"},"PeriodicalIF":4.6,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993065","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 : 2025-01-08DOI: 10.1109/LRA.2025.3527306
Mohammad Amin Mirzaee;Hung-Jui Huang;Wenzhen Yuan
Scanning large-scale surfaces is widely demanded in surface reconstruction applications and detecting defects in industries' quality control and maintenance stages. Traditional vision-based tactile sensors have shown promising performance in high-resolution shape reconstruction while suffering limitations such as small sensing areas or susceptibility to damage when slid across surfaces, making them unsuitable for continuous sensing on large surfaces. To address these shortcomings, we introduce a novel vision-based tactile sensor designed for continuous surface sensing applications. Our design uses an elastomeric belt and two wheels to continuously scan the target surface. The proposed sensor showed promising results in both shape reconstruction and surface fusion, indicating its applicability. The dot product of the estimated and reference surface normal map is reported over the sensing area and for different scanning speeds. Results indicate that the proposed sensor can rapidly scan large-scale surfaces with high accuracy at speeds up to 45 mm/s.
{"title":"GelBelt: A Vision-Based Tactile Sensor for Continuous Sensing of Large Surfaces","authors":"Mohammad Amin Mirzaee;Hung-Jui Huang;Wenzhen Yuan","doi":"10.1109/LRA.2025.3527306","DOIUrl":"https://doi.org/10.1109/LRA.2025.3527306","url":null,"abstract":"Scanning large-scale surfaces is widely demanded in surface reconstruction applications and detecting defects in industries' quality control and maintenance stages. Traditional vision-based tactile sensors have shown promising performance in high-resolution shape reconstruction while suffering limitations such as small sensing areas or susceptibility to damage when slid across surfaces, making them unsuitable for continuous sensing on large surfaces. To address these shortcomings, we introduce a novel vision-based tactile sensor designed for continuous surface sensing applications. Our design uses an elastomeric belt and two wheels to continuously scan the target surface. The proposed sensor showed promising results in both shape reconstruction and surface fusion, indicating its applicability. The dot product of the estimated and reference surface normal map is reported over the sensing area and for different scanning speeds. Results indicate that the proposed sensor can rapidly scan large-scale surfaces with high accuracy at speeds up to 45 mm/s.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 2","pages":"2016-2023"},"PeriodicalIF":4.6,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142992986","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 : 2025-01-08DOI: 10.1109/LRA.2025.3527346
Miguel Saavedra-Ruiz;Steven A. Parkison;Ria Arora;James Richard Forbes;Liam Paull
Bayesian estimation is a vital tool in robotics as it allows systems to update the robot state belief using incomplete information from noisy sensors. To render the state estimation problem tractable, many systems assume that the motion and measurement noise, as well as the state distribution, are all unimodal and Gaussian. However, there are numerous scenarios and systems that do not comply with these assumptions. Existing nonparametric filters that are used to model multimodal distributions have drawbacks that limit their ability to represent a diverse set of distributions. This letter introduces a novel approach to nonparametric Bayesian filtering on motion groups, designed to handle multimodal distributions using harmonic exponential distributions. This approach leverages two key insights of harmonic exponential distributions: a) the product of two distributions can be expressed as the element-wise addition of their log-likelihood Fourier coefficients, and b) the convolution of two distributions can be efficiently computed as the tensor product of their Fourier coefficients. These observations enable the development of an efficient and asymptotically exact solution to the Bayes filter up to the band limit of a Fourier transform. We demonstrate our filter's superior performance compared with established nonparametric filtering methods across a range of simulated and real-world localization tasks.
{"title":"The Harmonic Exponential Filter for Nonparametric Estimation on Motion Groups","authors":"Miguel Saavedra-Ruiz;Steven A. Parkison;Ria Arora;James Richard Forbes;Liam Paull","doi":"10.1109/LRA.2025.3527346","DOIUrl":"https://doi.org/10.1109/LRA.2025.3527346","url":null,"abstract":"Bayesian estimation is a vital tool in robotics as it allows systems to update the robot state belief using incomplete information from noisy sensors. To render the state estimation problem tractable, many systems assume that the motion and measurement noise, as well as the state distribution, are all unimodal and Gaussian. However, there are numerous scenarios and systems that do not comply with these assumptions. Existing nonparametric filters that are used to model multimodal distributions have drawbacks that limit their ability to represent a diverse set of distributions. This letter introduces a novel approach to nonparametric Bayesian filtering on motion groups, designed to handle multimodal distributions using harmonic exponential distributions. This approach leverages two key insights of harmonic exponential distributions: a) the product of two distributions can be expressed as the element-wise addition of their log-likelihood Fourier coefficients, and b) the convolution of two distributions can be efficiently computed as the tensor product of their Fourier coefficients. These observations enable the development of an efficient and asymptotically exact solution to the Bayes filter up to the band limit of a Fourier transform. We demonstrate our filter's superior performance compared with established nonparametric filtering methods across a range of simulated and real-world localization tasks.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 2","pages":"2096-2103"},"PeriodicalIF":4.6,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142992992","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 : 2025-01-08DOI: 10.1109/LRA.2025.3527308
Derek J. Pravecek;Micah J. Oevermann;Gray C. Thomas;Robert O. Ambrose
Replacing spherical robots' hard shells with soft, pressurized tires has the potential to improve their off-road practicality immensely. This change leverages spherical robots as a simple and rugged solution to problems currently addressed using wheeled or tracked vehicles. Though numerous prototypes have been launched over the last three decades, there has not been a spherical robot that poses a serious contender to tracked and wheeled systems. Most prototypes are built with a hard outer shell for ease of construction and control. Hard outer shells fail to absorb the impacts from uneven terrain. We addressed this issue by constructing a one-of-a-kind spherical robot with a durable pneumatic, soft outer shell. Although a soft-shell is more desirable for locomotion, it introduces complicated, nonlinear shell dynamics that cause a more challenging control problem. This article presents an empirical model of the steady-state torque induced by soft-shell dynamics, developed using system identification and a model based on tire dynamics. We show how this model, which fingerprints the robot's contact dynamics, is incorporated into RoboBall's steering control algorithm to compensate for soft-shell effects, enhancing setpoint tracking and improving control performance.
{"title":"Empirically Compensated Setpoint Tracking for Spherical Robots With Pressurized Soft-Shells","authors":"Derek J. Pravecek;Micah J. Oevermann;Gray C. Thomas;Robert O. Ambrose","doi":"10.1109/LRA.2025.3527308","DOIUrl":"https://doi.org/10.1109/LRA.2025.3527308","url":null,"abstract":"Replacing spherical robots' hard shells with soft, pressurized tires has the potential to improve their off-road practicality immensely. This change leverages spherical robots as a simple and rugged solution to problems currently addressed using wheeled or tracked vehicles. Though numerous prototypes have been launched over the last three decades, there has not been a spherical robot that poses a serious contender to tracked and wheeled systems. Most prototypes are built with a hard outer shell for ease of construction and control. Hard outer shells fail to absorb the impacts from uneven terrain. We addressed this issue by constructing a one-of-a-kind spherical robot with a durable pneumatic, soft outer shell. Although a soft-shell is more desirable for locomotion, it introduces complicated, nonlinear shell dynamics that cause a more challenging control problem. This article presents an empirical model of the steady-state torque induced by soft-shell dynamics, developed using system identification and a model based on tire dynamics. We show how this model, which fingerprints the robot's contact dynamics, is incorporated into RoboBall's steering control algorithm to compensate for soft-shell effects, enhancing setpoint tracking and improving control performance.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 3","pages":"2136-2143"},"PeriodicalIF":4.6,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993208","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 : 2025-01-08DOI: 10.1109/LRA.2025.3527292
Jiale Han;Yi Zhu;Jian Yang
This letter introduces a novel deep reinforcement learning (DRL) method for collision avoidance problem of fixed-wing unmanned aerial vehicles (UAVs). First, with considering the characteristics of collision avoidance problem, a collision prediction method is proposed to identify the neighboring UAVs with a significant threat. A convolutional neural network model is devised to extract the dynamic environment features. Second, a trajectory tracking macro action is incorporated into the action space of the proposed DRL-based algorithm. Guided by the reward function that considers to reward for closing to the preset flight paths, UAVs could return to the preset flight path after completing the collision avoidance. The proposed method is trained in simulation scenarios, with model updates implemented using a soft actor-critic (SAC) algorithm. Validation experiments are conducted in several complex multi-UAV flight environments. The results demonstrate the superiority of our method over other advanced methods.
{"title":"A Deep Reinforcement Learning Method for Collision Avoidance with Dense Speed-Constrained Multi-UAV","authors":"Jiale Han;Yi Zhu;Jian Yang","doi":"10.1109/LRA.2025.3527292","DOIUrl":"https://doi.org/10.1109/LRA.2025.3527292","url":null,"abstract":"This letter introduces a novel deep reinforcement learning (DRL) method for collision avoidance problem of fixed-wing unmanned aerial vehicles (UAVs). First, with considering the characteristics of collision avoidance problem, a collision prediction method is proposed to identify the neighboring UAVs with a significant threat. A convolutional neural network model is devised to extract the dynamic environment features. Second, a trajectory tracking macro action is incorporated into the action space of the proposed DRL-based algorithm. Guided by the reward function that considers to reward for closing to the preset flight paths, UAVs could return to the preset flight path after completing the collision avoidance. The proposed method is trained in simulation scenarios, with model updates implemented using a soft actor-critic (SAC) algorithm. Validation experiments are conducted in several complex multi-UAV flight environments. The results demonstrate the superiority of our method over other advanced methods.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 3","pages":"2152-2159"},"PeriodicalIF":4.6,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993209","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 : 2025-01-06DOI: 10.1109/LRA.2025.3526439
Shaunak A. Mehta;Yusuf Umut Ciftci;Balamurugan Ramachandran;Somil Bansal;Dylan P. Losey
Behavior cloning is a common imitation learning paradigm. Under behavior cloning the robot collects expert demonstrations, and then trains a policy to match the actions taken by the expert. This works well when the robot learner visits states where the expert has already demonstrated the correct action; but inevitably the robot will also encounter new states outside of its training dataset. If the robot learner takes the wrong action at these new states it could move farther from the training data, which in turn leads to increasingly incorrect actions and compounding errors. Existing works try to address this fundamental challenge by augmenting or enhancing the training data. By contrast, in our letter we develop the control theoretic properties of behavior cloned policies. Specifically, we consider the error dynamics between the system's current state and the states in the expert dataset. From the error dynamics we derive model-based and model-free conditions for stability: under these conditions the robot shapes its policy so that its current behavior converges towards example behaviors in the expert dataset. In practice, this results in Stable-BC, an easy to implement extension of standard behavior cloning that is provably robust to covariate shift. We demonstrate the effectiveness of our algorithm in simulations with interactive, nonlinear, and visual environments. We also conduct experiments where a robot arm uses Stable-BC to play air hockey.
{"title":"Stable-BC: Controlling Covariate Shift With Stable Behavior Cloning","authors":"Shaunak A. Mehta;Yusuf Umut Ciftci;Balamurugan Ramachandran;Somil Bansal;Dylan P. Losey","doi":"10.1109/LRA.2025.3526439","DOIUrl":"https://doi.org/10.1109/LRA.2025.3526439","url":null,"abstract":"Behavior cloning is a common imitation learning paradigm. Under behavior cloning the robot collects expert demonstrations, and then trains a policy to match the actions taken by the expert. This works well when the robot learner visits states where the expert has already demonstrated the correct action; but inevitably the robot will also encounter new states outside of its training dataset. If the robot learner takes the wrong action at these new states it could move farther from the training data, which in turn leads to increasingly incorrect actions and compounding errors. Existing works try to address this fundamental challenge by augmenting or enhancing the training data. By contrast, in our letter we develop the <italic>control theoretic</i> properties of behavior cloned policies. Specifically, we consider the error dynamics between the system's current state and the states in the expert dataset. From the error dynamics we derive model-based and model-free conditions for stability: under these conditions the robot shapes its policy so that its current behavior converges towards example behaviors in the expert dataset. In practice, this results in Stable-BC, an easy to implement extension of standard behavior cloning that is provably robust to covariate shift. We demonstrate the effectiveness of our algorithm in simulations with interactive, nonlinear, and visual environments. We also conduct experiments where a robot arm uses Stable-BC to play air hockey.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 2","pages":"1952-1959"},"PeriodicalIF":4.6,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142992978","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}
Modular Self-Reconfigurable Robots offer exceptional adaptability and versatility through reconfiguration, but traditional rigid robot designs lack the compliance necessary for effective interaction with complex environments. Recent advancements in modular soft robots address this shortcoming with enhanced flexibility; however, their designs lack the capability of active self-reconfiguration and heavily rely on manual assembly. In this letter, we present a modular self-reconfigurable soft continuum robotic system featuring a continuum backbone and an omnidirectional docking mechanism. This design enables each module to independently perform loco-manipulation and self-reconfiguration. We then propose a kinetostatic model and conduct a geometrical docking range analysis to characterize the robot's performance. The reconfiguration process and the distinct motion gait for each configuration are also developed, including rolling, crawling, and snake-like undulation. Experimental demonstrations show that both single and multiple connected modules can achieve successful loco-manipulation, adapting effectively to various environments.
{"title":"Modular Self-Reconfigurable Continuum Robot for General Purpose Loco-Manipulation","authors":"Yilin Cai;Haokai Xu;Yifan Wang;Desai Chen;Wojciech Matusik;Wan Shou;Yue Chen","doi":"10.1109/LRA.2025.3526560","DOIUrl":"https://doi.org/10.1109/LRA.2025.3526560","url":null,"abstract":"Modular Self-Reconfigurable Robots offer exceptional adaptability and versatility through reconfiguration, but traditional rigid robot designs lack the compliance necessary for effective interaction with complex environments. Recent advancements in modular soft robots address this shortcoming with enhanced flexibility; however, their designs lack the capability of active self-reconfiguration and heavily rely on manual assembly. In this letter, we present a modular self-reconfigurable soft continuum robotic system featuring a continuum backbone and an omnidirectional docking mechanism. This design enables each module to independently perform loco-manipulation and self-reconfiguration. We then propose a kinetostatic model and conduct a geometrical docking range analysis to characterize the robot's performance. The reconfiguration process and the distinct motion gait for each configuration are also developed, including rolling, crawling, and snake-like undulation. Experimental demonstrations show that both single and multiple connected modules can achieve successful loco-manipulation, adapting effectively to various environments.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 2","pages":"1976-1983"},"PeriodicalIF":4.6,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142992981","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 : 2025-01-06DOI: 10.1109/LRA.2025.3526571
Haixuan Wang;Fei Qiao;Shengxi Jiang;Haibin Zhu;Junkai Wang
The deterioration phenomenon is common and lasting as machines' service time increases within energy-intensive manufacturing processes such as heat treatment, which may bring about processes time extension or even the breakdown of a machine. It is crucial to collaboratively optimize batch scheduling and maintenance to ensure stable, efficient production, and achieve energy efficiency. This study takes into account preventive maintenance, where a maintenance activity is carried out after a certain number of batches are processed. A novel multi-objective mixed-integer programming model for unrelated parallel batching machines is proposed to minimize the makespan, total completion time and total energy consumption. The entire problem is broken down into four sub-issues: job division, job dispatching, batch formation and batch sequencing. Given the NP-hard nature of the problem, three heuristic algorithms based on several structural properties are designed according to the features of the latter three parts. Meanwhile, an integrated methodology, a Multi-Objective Evolutionary Algorithm based on Decomposition combined with Variable Neighborhood Search (MOEA/D-VNS), is put forward to handle job division and the multi-dimensional collaborative optimization problem. The performance of the proposed algorithms is compared with that of other typical dominance-based evolutionary algorithms. Extensive numerical experiments are conducted to validate the effectiveness of the proposed model and algorithms.
{"title":"Deterioration-Aware Collaborative Energy-Efficient Batch Scheduling and Maintenance for Unrelated Parallel Machines Based on Improved MOEA/D","authors":"Haixuan Wang;Fei Qiao;Shengxi Jiang;Haibin Zhu;Junkai Wang","doi":"10.1109/LRA.2025.3526571","DOIUrl":"https://doi.org/10.1109/LRA.2025.3526571","url":null,"abstract":"The deterioration phenomenon is common and lasting as machines' service time increases within energy-intensive manufacturing processes such as heat treatment, which may bring about processes time extension or even the breakdown of a machine. It is crucial to collaboratively optimize batch scheduling and maintenance to ensure stable, efficient production, and achieve energy efficiency. This study takes into account preventive maintenance, where a maintenance activity is carried out after a certain number of batches are processed. A novel multi-objective mixed-integer programming model for unrelated parallel batching machines is proposed to minimize the makespan, total completion time and total energy consumption. The entire problem is broken down into four sub-issues: job division, job dispatching, batch formation and batch sequencing. Given the NP-hard nature of the problem, three heuristic algorithms based on several structural properties are designed according to the features of the latter three parts. Meanwhile, an integrated methodology, a Multi-Objective Evolutionary Algorithm based on Decomposition combined with Variable Neighborhood Search (MOEA/D-VNS), is put forward to handle job division and the multi-dimensional collaborative optimization problem. The performance of the proposed algorithms is compared with that of other typical dominance-based evolutionary algorithms. Extensive numerical experiments are conducted to validate the effectiveness of the proposed model and algorithms.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 2","pages":"2056-2063"},"PeriodicalIF":4.6,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993100","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 : 2025-01-06DOI: 10.1109/LRA.2025.3526572
Sibaek Lee;Kyeongsu Kang;Seongbo Ha;Hyeonwoo Yu
We present a Bayesian Neural Radiance Field (NeRF), which explicitly quantifies uncertainty in the volume density by modeling uncertainty in the occupancy, without the need for additional networks, making it particularly suited for challenging observations and uncontrolled image environments. NeRF diverges from traditional geometric methods by providing an enriched scene representation, rendering color and density in 3D space from various viewpoints. However, NeRF encounters limitations in addressing uncertainties solely through geometric structure information, leading to inaccuracies when interpreting scenes with insufficient real-world observations. While previous efforts have relied on auxiliary networks, we propose a series of formulation extensions to NeRF that manage uncertainties in density, both color and density, and occupancy, all without the need for additional networks. In experiments, we show that our method significantly enhances performance on RGB and depth images in the comprehensive dataset. Given that uncertainty modeling aligns well with the inherently uncertain environments of Simultaneous Localization and Mapping (SLAM), we applied our approach to SLAM systems and observed notable improvements in mapping and tracking performance. These results confirm the effectiveness of our Bayesian NeRF approach in quantifying uncertainty based on geometric structure, making it a robust solution for challenging real-world scenarios.
{"title":"Bayesian NeRF: Quantifying Uncertainty With Volume Density for Neural Implicit Fields","authors":"Sibaek Lee;Kyeongsu Kang;Seongbo Ha;Hyeonwoo Yu","doi":"10.1109/LRA.2025.3526572","DOIUrl":"https://doi.org/10.1109/LRA.2025.3526572","url":null,"abstract":"We present a Bayesian Neural Radiance Field (NeRF), which explicitly quantifies uncertainty in the volume density by modeling uncertainty in the occupancy, without the need for additional networks, making it particularly suited for challenging observations and uncontrolled image environments. NeRF diverges from traditional geometric methods by providing an enriched scene representation, rendering color and density in 3D space from various viewpoints. However, NeRF encounters limitations in addressing uncertainties solely through geometric structure information, leading to inaccuracies when interpreting scenes with insufficient real-world observations. While previous efforts have relied on auxiliary networks, we propose a series of formulation extensions to NeRF that manage uncertainties in density, both color and density, and occupancy, all without the need for additional networks. In experiments, we show that our method significantly enhances performance on RGB and depth images in the comprehensive dataset. Given that uncertainty modeling aligns well with the inherently uncertain environments of Simultaneous Localization and Mapping (SLAM), we applied our approach to SLAM systems and observed notable improvements in mapping and tracking performance. These results confirm the effectiveness of our Bayesian NeRF approach in quantifying uncertainty based on geometric structure, making it a robust solution for challenging real-world scenarios.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 3","pages":"2144-2151"},"PeriodicalIF":4.6,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993210","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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