Pub Date : 2025-01-03DOI: 10.1109/tro.2025.3526078
Ihab S. Mohamed, Junhong Xu, Gaurav S. Sukhatme, Lantao Liu
{"title":"Towards Efficient MPPI Trajectory Generation with Unscented Guidance: U-MPPI Control Strategy","authors":"Ihab S. Mohamed, Junhong Xu, Gaurav S. Sukhatme, Lantao Liu","doi":"10.1109/tro.2025.3526078","DOIUrl":"https://doi.org/10.1109/tro.2025.3526078","url":null,"abstract":"","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"30 1","pages":""},"PeriodicalIF":7.8,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142924659","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2024-12-25DOI: 10.1109/TRO.2024.3521859
Sven Lilge;Timothy Barfoot;Jessica Burgner-Kahrs
In contrast to conventional robots, accurately modeling the kinematics and statics of continuum robots is challenging due to partially unknown material properties, parasitic effects, or unknown forces acting on the continuous body. Consequentially, state estimation approaches that utilize additional sensor information to predict the shape of continuum robots have garnered significant interest. This article presents a novel approach to state estimation for systems with multiple coupled continuum robots, which allows estimating the shape and strain variables of multiple continuum robots in an arbitrary coupled topology. Simulations and experiments demonstrate the capabilities and versatility of the proposed method, while achieving accurate and continuous estimates for the state of such systems, resulting in average end-effector errors of 3.3 mm and 5.02$^circ$ depending on the sensor setup. It is further shown, that the approach offers fast computation times of below 10 ms, enabling its utilization in quasi-static real-time scenarios with average update rates of 100–200 Hz. An open-source C++ implementation of the proposed state estimation method is made publicly available to the community.
{"title":"State Estimation for Continuum Multirobot Systems on SE(3)","authors":"Sven Lilge;Timothy Barfoot;Jessica Burgner-Kahrs","doi":"10.1109/TRO.2024.3521859","DOIUrl":"10.1109/TRO.2024.3521859","url":null,"abstract":"In contrast to conventional robots, accurately modeling the kinematics and statics of continuum robots is challenging due to partially unknown material properties, parasitic effects, or unknown forces acting on the continuous body. Consequentially, state estimation approaches that utilize additional sensor information to predict the shape of continuum robots have garnered significant interest. This article presents a novel approach to state estimation for systems with multiple coupled continuum robots, which allows estimating the shape and strain variables of multiple continuum robots in an arbitrary coupled topology. Simulations and experiments demonstrate the capabilities and versatility of the proposed method, while achieving accurate and continuous estimates for the state of such systems, resulting in average end-effector errors of 3.3 mm and 5.02<inline-formula><tex-math>$^circ$</tex-math></inline-formula> depending on the sensor setup. It is further shown, that the approach offers fast computation times of below 10 ms, enabling its utilization in quasi-static real-time scenarios with average update rates of 100–200 Hz. An open-source C++ implementation of the proposed state estimation method is made publicly available to the community.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"41 ","pages":"905-925"},"PeriodicalIF":9.4,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142888198","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2024-12-25DOI: 10.1109/tro.2024.3522182
Abdulaziz Y. Alkayas, Anup Teejo Mathew, Daniel Feliu-Talegon, Ping Deng, Thomas George Thuruthel, Federico Renda
{"title":"Soft Synergies: Model Order Reduction of Hybrid Soft-Rigid Robots via Optimal Strain Parameterization","authors":"Abdulaziz Y. Alkayas, Anup Teejo Mathew, Daniel Feliu-Talegon, Ping Deng, Thomas George Thuruthel, Federico Renda","doi":"10.1109/tro.2024.3522182","DOIUrl":"https://doi.org/10.1109/tro.2024.3522182","url":null,"abstract":"","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"154 1","pages":""},"PeriodicalIF":7.8,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142888203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2024-12-25DOI: 10.1109/TRO.2024.3521856
Keenan Burnett;Angela P. Schoellig;Timothy D. Barfoot
In this work, we demonstrate continuous-time radar-inertial and lidar-inertial odometry using a Gaussian process motion prior. Using a sparse prior, we demonstrate improved computational complexity during preintegration and interpolation. We use a white-noise-on-acceleration motion prior and treat the gyroscope as a direct measurement of the state while preintegrating accelerometer measurements to form relative velocity factors. Our odometry is implemented using sliding-window batch trajectory estimation. To our knowledge, our work is the first to demonstrate radar-inertial odometry with a spinning mechanical radar using both gyroscope and accelerometer measurements. We improve the performance of our radar odometry by 43% by incorporating an inertial measurement unit. Our approach is efficient and we demonstrate real-time performance. Code for this article can be found at: https://github.com/utiasASRL/steam_icp.
{"title":"Continuous-Time Radar-Inertial and Lidar-Inertial Odometry Using a Gaussian Process Motion Prior","authors":"Keenan Burnett;Angela P. Schoellig;Timothy D. Barfoot","doi":"10.1109/TRO.2024.3521856","DOIUrl":"10.1109/TRO.2024.3521856","url":null,"abstract":"In this work, we demonstrate continuous-time radar-inertial and lidar-inertial odometry using a Gaussian process motion prior. Using a sparse prior, we demonstrate improved computational complexity during preintegration and interpolation. We use a white-noise-on-acceleration motion prior and treat the gyroscope as a direct measurement of the state while preintegrating accelerometer measurements to form relative velocity factors. Our odometry is implemented using sliding-window batch trajectory estimation. To our knowledge, our work is the first to demonstrate radar-inertial odometry with a spinning mechanical radar using both gyroscope and accelerometer measurements. We improve the performance of our radar odometry by 43% by incorporating an inertial measurement unit. Our approach is efficient and we demonstrate real-time performance. Code for this article can be found at: <uri>https://github.com/utiasASRL/steam_icp</uri>.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"41 ","pages":"1059-1076"},"PeriodicalIF":9.4,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142888195","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Aerial swarm systems possess immense potential in various aspects, such as cooperative exploration, target tracking, and search and rescue. Efficient accurate self- and mutual state estimation are the critical preconditions for completing these swarm tasks, which remain challenging research topics. This article proposes Swarm-LIO2, a fully decentralized, plug-and-play, computationally efficient, and bandwidth-efficient light detection and ranging (LiDAR)-inertial odometry for aerial swarm systems. Swarm-LIO2 uses a decentralized plug-and-play network as the communication infrastructure. Only bandwidth-efficient and low-dimensional information is exchanged, including identity, ego state, mutual observation measurements, and global extrinsic transformations. To support the plug and play of new teammate participants, Swarm-LIO2 detects potential teammate autonomous aerial vehicles (AAVs) and initializes the temporal offset and global extrinsic transformation all automatically. To enhance the initialization efficiency, novel reflectivity-based AAV detection, trajectory matching, and factor graph optimization methods are proposed. For state estimation, Swarm-LIO2 fuses LiDAR, inertial measurement units, and mutual observation measurements within an efficient error state iterated Kalman filter (ESIKF) framework, with careful compensation of temporal delay and modeling of measurements to enhance the accuracy and consistency. Moreover, the proposed ESIKF framework leverages the global extrinsic for ego state estimation in the case of LiDAR degeneration or refines the global extrinsic along with the ego state estimation otherwise. To enhance the scalability, Swarm-LIO2 introduces a novel marginalization method in the ESIKF, which prevents the growth of computational time with swarm size. Extensive simulation and real-world experiments demonstrate the broad adaptability to large-scale aerial swarm systems and complicated scenarios, including GPS-denied scenes and degenerated scenes for cameras or LiDARs. The experimental results showcase the centimeter-level localization accuracy, which outperforms other state-of-the-art LiDAR-inertial odometry for a single-AAV system. Furthermore, diverse applications demonstrate the potential of Swarm-LIO2 to serve as a reliable infrastructure for various aerial swarm missions.
{"title":"Swarm-LIO2: Decentralized Efficient LiDAR-Inertial Odometry for Aerial Swarm Systems","authors":"Fangcheng Zhu;Yunfan Ren;Longji Yin;Fanze Kong;Qingbo Liu;Ruize Xue;Wenyi Liu;Yixi Cai;Guozheng Lu;Haotian Li;Fu Zhang","doi":"10.1109/TRO.2024.3522155","DOIUrl":"10.1109/TRO.2024.3522155","url":null,"abstract":"Aerial swarm systems possess immense potential in various aspects, such as cooperative exploration, target tracking, and search and rescue. Efficient accurate self- and mutual state estimation are the critical preconditions for completing these swarm tasks, which remain challenging research topics. This article proposes Swarm-LIO2, a fully decentralized, plug-and-play, computationally efficient, and bandwidth-efficient light detection and ranging (LiDAR)-inertial odometry for aerial swarm systems. Swarm-LIO2 uses a decentralized plug-and-play network as the communication infrastructure. Only bandwidth-efficient and low-dimensional information is exchanged, including identity, ego state, mutual observation measurements, and global extrinsic transformations. To support the plug and play of new teammate participants, Swarm-LIO2 detects potential teammate autonomous aerial vehicles (AAVs) and initializes the temporal offset and global extrinsic transformation all automatically. To enhance the initialization efficiency, novel reflectivity-based AAV detection, trajectory matching, and factor graph optimization methods are proposed. For state estimation, Swarm-LIO2 fuses LiDAR, inertial measurement units, and mutual observation measurements within an efficient error state iterated Kalman filter (ESIKF) framework, with careful compensation of temporal delay and modeling of measurements to enhance the accuracy and consistency. Moreover, the proposed ESIKF framework leverages the global extrinsic for ego state estimation in the case of LiDAR degeneration or refines the global extrinsic along with the ego state estimation otherwise. To enhance the scalability, Swarm-LIO2 introduces a novel marginalization method in the ESIKF, which prevents the growth of computational time with swarm size. Extensive simulation and real-world experiments demonstrate the broad adaptability to large-scale aerial swarm systems and complicated scenarios, including GPS-denied scenes and degenerated scenes for cameras or LiDARs. The experimental results showcase the centimeter-level localization accuracy, which outperforms other state-of-the-art LiDAR-inertial odometry for a single-AAV system. Furthermore, diverse applications demonstrate the potential of Swarm-LIO2 to serve as a reliable infrastructure for various aerial swarm missions.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"41 ","pages":"960-981"},"PeriodicalIF":9.4,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142887959","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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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