Pub Date : 2024-04-16DOI: 10.1177/02783649241238358
Ravindu G Thalagala, Oscar De Silva, Awantha Jayasiri, Arthur Gubbels, George KI Mann, Raymond G Gosine
This paper presents a unique outdoor aerial visual-inertial-LiDAR dataset captured using a multi-sensor payload to promote the global navigation satellite system (GNSS)-denied navigation research. The dataset features flight distances ranging from 300 m to 5 km, collected using a DJI-M600 hexacopter drone and the National Research Council (NRC) Bell412 Advanced Systems Research Aircraft (ASRA). The dataset consists of hardware-synchronized monocular images, inertial measurement unit (IMU) measurements, 3D light detection and ranging (LiDAR) point-clouds, and high-precision real-time kinematic (RTK)-GNSS based ground truth. Nine data sequences were collected as robot operating system (ROS) bags over 100 mins of outdoor environment footage ranging from urban areas, highways, airports, hillsides, prairies, and waterfronts. The dataset was collected to facilitate the development of visual-inertial-LiDAR odometry and mapping algorithms, visual-inertial navigation algorithms, object detection, segmentation, and landing zone detection algorithms based on real-world drone and full-scale helicopter data. All the data sequences contain raw sensor measurements, hardware timestamps, and spatio-temporally aligned ground truth. The intrinsic and extrinsic calibrations of the sensors are also provided, along with raw calibration datasets. A performance summary of state-of-the-art methods applied on the data sequences is also provided.
{"title":"MUN-FRL: A Visual-Inertial-LiDAR Dataset for Aerial Autonomous Navigation and Mapping","authors":"Ravindu G Thalagala, Oscar De Silva, Awantha Jayasiri, Arthur Gubbels, George KI Mann, Raymond G Gosine","doi":"10.1177/02783649241238358","DOIUrl":"https://doi.org/10.1177/02783649241238358","url":null,"abstract":"This paper presents a unique outdoor aerial visual-inertial-LiDAR dataset captured using a multi-sensor payload to promote the global navigation satellite system (GNSS)-denied navigation research. The dataset features flight distances ranging from 300 m to 5 km, collected using a DJI-M600 hexacopter drone and the National Research Council (NRC) Bell412 Advanced Systems Research Aircraft (ASRA). The dataset consists of hardware-synchronized monocular images, inertial measurement unit (IMU) measurements, 3D light detection and ranging (LiDAR) point-clouds, and high-precision real-time kinematic (RTK)-GNSS based ground truth. Nine data sequences were collected as robot operating system (ROS) bags over 100 mins of outdoor environment footage ranging from urban areas, highways, airports, hillsides, prairies, and waterfronts. The dataset was collected to facilitate the development of visual-inertial-LiDAR odometry and mapping algorithms, visual-inertial navigation algorithms, object detection, segmentation, and landing zone detection algorithms based on real-world drone and full-scale helicopter data. All the data sequences contain raw sensor measurements, hardware timestamps, and spatio-temporally aligned ground truth. The intrinsic and extrinsic calibrations of the sensors are also provided, along with raw calibration datasets. A performance summary of state-of-the-art methods applied on the data sequences is also provided.","PeriodicalId":501362,"journal":{"name":"The International Journal of Robotics Research","volume":"72 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140613907","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-15DOI: 10.1177/02783649241246847
Lukas Bernreiter, Shehryar Khattak, Lionel Ott, Roland Siegwart, Marco Hutter, Cesar Cadena
The exploration of large-scale unknown environments can benefit from the deployment of multiple robots for collaborative mapping. Each robot explores a section of the environment and communicates onboard pose estimates and maps to a central server to build an optimized global multi-robot map. Naturally, inconsistencies can arise between onboard and server estimates due to onboard odometry drift, failures, or degeneracies. The mapping server can correct and overcome such failure cases using computationally expensive operations such as inter-robot loop closure detection and multi-modal mapping. However, the individual robots do not benefit from the collaborative map if the mapping server provides no feedback. Although server updates from the multi-robot map can greatly alleviate the robotic mission strategically, most existing work lacks them, due to their associated computational and bandwidth-related costs. Motivated by this challenge, this paper proposes a novel collaborative mapping framework that enables global mapping consistency among robots and the mapping server. In particular, we propose graph spectral analysis, at different spatial scales, to detect structural differences between robot and server graphs, and to generate necessary constraints for the individual robot pose graphs. Our approach specifically finds the nodes that correspond to the drift’s origin rather than the nodes where the error becomes too large. We thoroughly analyze and validate our proposed framework using several real-world multi-robot field deployments where we show improvements of the onboard system up to 90% and can recover the onboard estimation from localization failures and even from the degeneracies within its estimation.
{"title":"A framework for collaborative multi-robot mapping using spectral graph wavelets","authors":"Lukas Bernreiter, Shehryar Khattak, Lionel Ott, Roland Siegwart, Marco Hutter, Cesar Cadena","doi":"10.1177/02783649241246847","DOIUrl":"https://doi.org/10.1177/02783649241246847","url":null,"abstract":"The exploration of large-scale unknown environments can benefit from the deployment of multiple robots for collaborative mapping. Each robot explores a section of the environment and communicates onboard pose estimates and maps to a central server to build an optimized global multi-robot map. Naturally, inconsistencies can arise between onboard and server estimates due to onboard odometry drift, failures, or degeneracies. The mapping server can correct and overcome such failure cases using computationally expensive operations such as inter-robot loop closure detection and multi-modal mapping. However, the individual robots do not benefit from the collaborative map if the mapping server provides no feedback. Although server updates from the multi-robot map can greatly alleviate the robotic mission strategically, most existing work lacks them, due to their associated computational and bandwidth-related costs. Motivated by this challenge, this paper proposes a novel collaborative mapping framework that enables global mapping consistency among robots and the mapping server. In particular, we propose graph spectral analysis, at different spatial scales, to detect structural differences between robot and server graphs, and to generate necessary constraints for the individual robot pose graphs. Our approach specifically finds the nodes that correspond to the drift’s origin rather than the nodes where the error becomes too large. We thoroughly analyze and validate our proposed framework using several real-world multi-robot field deployments where we show improvements of the onboard system up to 90% and can recover the onboard estimation from localization failures and even from the degeneracies within its estimation.","PeriodicalId":501362,"journal":{"name":"The International Journal of Robotics Research","volume":"25 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140589980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-12DOI: 10.1177/02783649241246557
Mikhail Koptev, Nadia Figueroa, Aude Billard
Dynamical system (DS) based motion planning offers collision-free motion, with closed-loop reactivity thanks to their analytical expression. It ensures that obstacles are not penetrated by reshaping a nominal DS through matrix modulation, which is constructed using continuously differentiable obstacle representations. However, state-of-the-art approaches may suffer from local minima induced by non-convex obstacles, thus failing to scale to complex, high-dimensional joint spaces. On the other hand, sampling-based Model Predictive Control (MPC) techniques provide feasible collision-free paths in joint-space, yet are limited to quasi-reactive scenarios due to computational complexity that grows cubically with space dimensionality and horizon length. To control the robot in the cluttered environment with moving obstacles, and to generate feasible and highly reactive collision-free motion in robots’ joint space, we present an approach for modulating joint-space DS using sampling-based MPC. Specifically, a nominal DS representing an unconstrained desired joint space motion to a target is locally deflected with obstacle-tangential velocity components navigating the robot around obstacles and avoiding local minima. Such tangential velocity components are constructed from receding horizon collision-free paths generated asynchronously by the sampling-based MPC. Notably, the MPC is not required to run constantly, but only activated when the local minima is detected. The approach is validated in simulation and real-world experiments on a 7-DoF robot demonstrating the capability of avoiding concave obstacles, while maintaining local attractor stability in both quasi-static and highly dynamic cluttered environments.
{"title":"Reactive collision-free motion generation in joint space via dynamical systems and sampling-based MPC","authors":"Mikhail Koptev, Nadia Figueroa, Aude Billard","doi":"10.1177/02783649241246557","DOIUrl":"https://doi.org/10.1177/02783649241246557","url":null,"abstract":"Dynamical system (DS) based motion planning offers collision-free motion, with closed-loop reactivity thanks to their analytical expression. It ensures that obstacles are not penetrated by reshaping a nominal DS through matrix modulation, which is constructed using continuously differentiable obstacle representations. However, state-of-the-art approaches may suffer from local minima induced by non-convex obstacles, thus failing to scale to complex, high-dimensional joint spaces. On the other hand, sampling-based Model Predictive Control (MPC) techniques provide feasible collision-free paths in joint-space, yet are limited to quasi-reactive scenarios due to computational complexity that grows cubically with space dimensionality and horizon length. To control the robot in the cluttered environment with moving obstacles, and to generate feasible and highly reactive collision-free motion in robots’ joint space, we present an approach for modulating joint-space DS using sampling-based MPC. Specifically, a nominal DS representing an unconstrained desired joint space motion to a target is locally deflected with obstacle-tangential velocity components navigating the robot around obstacles and avoiding local minima. Such tangential velocity components are constructed from receding horizon collision-free paths generated asynchronously by the sampling-based MPC. Notably, the MPC is not required to run constantly, but only activated when the local minima is detected. The approach is validated in simulation and real-world experiments on a 7-DoF robot demonstrating the capability of avoiding concave obstacles, while maintaining local attractor stability in both quasi-static and highly dynamic cluttered environments.","PeriodicalId":501362,"journal":{"name":"The International Journal of Robotics Research","volume":"213 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140589703","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-03DOI: 10.1177/02783649241242136
Minwoo Jung, Wooseong Yang, Dongjae Lee, Hyeonjae Gil, Giseop Kim, Ayoung Kim
Place recognition is crucial for robot localization and loop closure in simultaneous localization and mapping (SLAM). Light Detection and Ranging (LiDAR), known for its robust sensing capabilities and measurement consistency even in varying illumination conditions, has become pivotal in various fields, surpassing traditional imaging sensors in certain applications. Among various types of LiDAR, spinning LiDARs are widely used, while non-repetitive scanning patterns have recently been utilized in robotics applications. Some LiDARs provide additional measurements such as reflectivity, Near Infrared (NIR), and velocity from Frequency modulated continuous wave (FMCW) LiDARs. Despite these advances, there is a lack of comprehensive datasets reflecting the broad spectrum of LiDAR configurations for place recognition. To tackle this issue, our paper proposes the HeLiPR dataset, curated especially for place recognition with heterogeneous LiDARs, embodying spatiotemporal variations. To the best of our knowledge, the HeLiPR dataset is the first heterogeneous LiDAR dataset supporting inter-LiDAR place recognition with both non-repetitive and spinning LiDARs, accommodating different field of view (FOV)s and varying numbers of rays. The dataset covers diverse environments, from urban cityscapes to high-dynamic freeways, over a month, enhancing adaptability and robustness across scenarios. Notably, HeLiPR dataset includes trajectories parallel to MulRan sequences, making it valuable for research in heterogeneous LiDAR place recognition and long-term studies. The dataset is accessible at https://sites.google.com/view/heliprdataset .
{"title":"HeLiPR: Heterogeneous LiDAR dataset for inter-LiDAR place recognition under spatiotemporal variations","authors":"Minwoo Jung, Wooseong Yang, Dongjae Lee, Hyeonjae Gil, Giseop Kim, Ayoung Kim","doi":"10.1177/02783649241242136","DOIUrl":"https://doi.org/10.1177/02783649241242136","url":null,"abstract":"Place recognition is crucial for robot localization and loop closure in simultaneous localization and mapping (SLAM). Light Detection and Ranging (LiDAR), known for its robust sensing capabilities and measurement consistency even in varying illumination conditions, has become pivotal in various fields, surpassing traditional imaging sensors in certain applications. Among various types of LiDAR, spinning LiDARs are widely used, while non-repetitive scanning patterns have recently been utilized in robotics applications. Some LiDARs provide additional measurements such as reflectivity, Near Infrared (NIR), and velocity from Frequency modulated continuous wave (FMCW) LiDARs. Despite these advances, there is a lack of comprehensive datasets reflecting the broad spectrum of LiDAR configurations for place recognition. To tackle this issue, our paper proposes the HeLiPR dataset, curated especially for place recognition with heterogeneous LiDARs, embodying spatiotemporal variations. To the best of our knowledge, the HeLiPR dataset is the first heterogeneous LiDAR dataset supporting inter-LiDAR place recognition with both non-repetitive and spinning LiDARs, accommodating different field of view (FOV)s and varying numbers of rays. The dataset covers diverse environments, from urban cityscapes to high-dynamic freeways, over a month, enhancing adaptability and robustness across scenarios. Notably, HeLiPR dataset includes trajectories parallel to MulRan sequences, making it valuable for research in heterogeneous LiDAR place recognition and long-term studies. The dataset is accessible at https://sites.google.com/view/heliprdataset .","PeriodicalId":501362,"journal":{"name":"The International Journal of Robotics Research","volume":"49 10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140590135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-27DOI: 10.1177/02783649241239077
Marcus Hoerger, Hanna Kurniawati, Alberto Elfes
Motion planning under uncertainty is essential for reliable robot operation. Despite substantial advances over the past decade, the problem remains difficult for systems with complex dynamics. Most state-of-the-art methods perform search that relies on a large number of forward simulations. For systems with complex dynamics, this generally requires costly numerical integrations, which significantly slows down the planning process. Linearization-based methods have been proposed that can alleviate the above problem. However, it is not clear how linearization affects the quality of the generated motion strategy, and when such simplifications are admissible. To answer these questions, we propose a non-linearity measure, called Statistical-distance-based Non-linearity Measure (SNM), that can identify where linearization is beneficial and where it should be avoided. We show that when the problem is framed as the Partially Observable Markov Decision Process, the value difference between the optimal strategy for the original model and the linearized model can be upper-bounded by a function linear in SNM. Comparisons with an existing measure on various scenarios indicate that SNM is more suitable in estimating the effectiveness of linearization-based solvers. To test the applicability of SNM in motion planning, we propose a simple online planner that uses SNM as a heuristic to switch between a general and a linearization-based solver. Results on a car-like robot with second order dynamics and 4-DOFs and 7-DOFs torque-controlled manipulators indicate that SNM can appropriately decide if and when a linearization-based solver should be used.
{"title":"Non-linearity Measure for POMDP-based Motion Planning","authors":"Marcus Hoerger, Hanna Kurniawati, Alberto Elfes","doi":"10.1177/02783649241239077","DOIUrl":"https://doi.org/10.1177/02783649241239077","url":null,"abstract":"Motion planning under uncertainty is essential for reliable robot operation. Despite substantial advances over the past decade, the problem remains difficult for systems with complex dynamics. Most state-of-the-art methods perform search that relies on a large number of forward simulations. For systems with complex dynamics, this generally requires costly numerical integrations, which significantly slows down the planning process. Linearization-based methods have been proposed that can alleviate the above problem. However, it is not clear how linearization affects the quality of the generated motion strategy, and when such simplifications are admissible. To answer these questions, we propose a non-linearity measure, called Statistical-distance-based Non-linearity Measure (SNM), that can identify where linearization is beneficial and where it should be avoided. We show that when the problem is framed as the Partially Observable Markov Decision Process, the value difference between the optimal strategy for the original model and the linearized model can be upper-bounded by a function linear in SNM. Comparisons with an existing measure on various scenarios indicate that SNM is more suitable in estimating the effectiveness of linearization-based solvers. To test the applicability of SNM in motion planning, we propose a simple online planner that uses SNM as a heuristic to switch between a general and a linearization-based solver. Results on a car-like robot with second order dynamics and 4-DOFs and 7-DOFs torque-controlled manipulators indicate that SNM can appropriately decide if and when a linearization-based solver should be used.","PeriodicalId":501362,"journal":{"name":"The International Journal of Robotics Research","volume":"47 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140313704","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-22DOI: 10.1177/02783649241238766
Junhong Xu, Kai Yin, Jason M. Gregory, Kris Hauser, Lantao Liu
Navigation safety is critical for many autonomous systems such as self-driving vehicles in an urban environment. It requires an explicit consideration of boundary constraints that describe the borders of any infeasible, non-navigable, or unsafe regions. We propose a principled boundary-aware safe stochastic planning framework with promising results. Our method generates a value function that can strictly distinguish the state values between free (safe) and non-navigable (boundary) spaces in the continuous state, naturally leading to a safe boundary-aware policy. At the core of our solution lies a seamless integration of finite elements and kernel-based functions, where the finite elements allow us to characterize safety-critical states’ borders accurately, and the kernel-based function speeds up computation for the non-safety-critical states. The proposed method was evaluated through extensive simulations and demonstrated safe navigation behaviors in mobile navigation tasks. Additionally, we demonstrate that our approach can maneuver safely and efficiently in cluttered real-world environments using a ground vehicle with strong external disturbances, such as navigating on a slippery floor and against external human intervention.
{"title":"Boundary-aware value function generation for safe stochastic motion planning","authors":"Junhong Xu, Kai Yin, Jason M. Gregory, Kris Hauser, Lantao Liu","doi":"10.1177/02783649241238766","DOIUrl":"https://doi.org/10.1177/02783649241238766","url":null,"abstract":"Navigation safety is critical for many autonomous systems such as self-driving vehicles in an urban environment. It requires an explicit consideration of boundary constraints that describe the borders of any infeasible, non-navigable, or unsafe regions. We propose a principled boundary-aware safe stochastic planning framework with promising results. Our method generates a value function that can strictly distinguish the state values between free (safe) and non-navigable (boundary) spaces in the continuous state, naturally leading to a safe boundary-aware policy. At the core of our solution lies a seamless integration of finite elements and kernel-based functions, where the finite elements allow us to characterize safety-critical states’ borders accurately, and the kernel-based function speeds up computation for the non-safety-critical states. The proposed method was evaluated through extensive simulations and demonstrated safe navigation behaviors in mobile navigation tasks. Additionally, we demonstrate that our approach can maneuver safely and efficiently in cluttered real-world environments using a ground vehicle with strong external disturbances, such as navigating on a slippery floor and against external human intervention.","PeriodicalId":501362,"journal":{"name":"The International Journal of Robotics Research","volume":"293 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140203268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-15DOI: 10.1177/02783649241236273
Shoudong Huang, Kris Hauser, Dylan A. Shell
{"title":"Selected papers from RSS2022","authors":"Shoudong Huang, Kris Hauser, Dylan A. Shell","doi":"10.1177/02783649241236273","DOIUrl":"https://doi.org/10.1177/02783649241236273","url":null,"abstract":"","PeriodicalId":501362,"journal":{"name":"The International Journal of Robotics Research","volume":"97 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140146497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-13DOI: 10.1177/02783649241238765
Xiaobo Liu, Xudong Han, Wei Hong, Fang Wan, Chaoyang Song
Proprioception is the “sixth sense” that detects limb postures with motor neurons. It requires a natural integration between the musculoskeletal systems and sensory receptors, which is challenging among modern robots that aim for lightweight, adaptive, and sensitive designs at low costs in mechanical design and algorithmic computation. Here, we present the Soft Polyhedral Network with an embedded vision for physical interactions, capable of adaptive kinesthesia and viscoelastic proprioception by learning kinetic features. This design enables passive adaptations to omni-directional interactions, visually captured by a miniature high-speed motion-tracking system embedded inside for proprioceptive learning. The results show that the soft network can infer real-time 6D forces and torques with accuracies of 0.25/0.24/0.35 N and 0.025/0.034/0.006 Nm in dynamic interactions. We also incorporate viscoelasticity in proprioception during static adaptation by adding a creep and relaxation modifier to refine the predicted results. The proposed soft network combines simplicity in design, omni-adaptation, and proprioceptive sensing with high accuracy, making it a versatile solution for robotics at a low material cost with more than one million use cycles for tasks such as sensitive and competitive grasping and touch-based geometry reconstruction. This study offers new insights into vision-based proprioception for soft robots in adaptive grasping, soft manipulation, and human-robot interaction.
运动感觉是通过运动神经元检测肢体姿势的 "第六感"。它需要肌肉骨骼系统和感觉受体之间的自然整合，这对于以低成本机械设计和算法计算为目标，追求轻量化、自适应和灵敏设计的现代机器人来说具有挑战性。在这里，我们介绍了具有嵌入式物理交互视觉的软多面体网络，它能够通过学习运动特征来实现自适应动觉和粘弹性本体感觉。这种设计能够被动地适应全方位的互动，并通过内部嵌入的微型高速运动跟踪系统进行视觉捕捉，从而实现本体感知学习。结果表明，在动态交互中，软网络可以实时推断出 6D 力和扭矩，精确度分别为 0.25/0.24/0.35 N 和 0.025/0.034/0.006 Nm。在静态适应过程中，我们还通过添加蠕变和松弛修改器，将粘弹性纳入本体感觉，以完善预测结果。所提出的软网络集设计简便性、全方位适应性和本体感知高精确度于一身，使其成为机器人技术的多功能解决方案，材料成本低，使用周期超过一百万次，可用于灵敏的竞争性抓取和基于触摸的几何重建等任务。这项研究为软机器人在自适应抓取、软操纵和人机交互方面基于视觉的本体感知提供了新的见解。
{"title":"Proprioceptive learning with soft polyhedral networks","authors":"Xiaobo Liu, Xudong Han, Wei Hong, Fang Wan, Chaoyang Song","doi":"10.1177/02783649241238765","DOIUrl":"https://doi.org/10.1177/02783649241238765","url":null,"abstract":"Proprioception is the “sixth sense” that detects limb postures with motor neurons. It requires a natural integration between the musculoskeletal systems and sensory receptors, which is challenging among modern robots that aim for lightweight, adaptive, and sensitive designs at low costs in mechanical design and algorithmic computation. Here, we present the Soft Polyhedral Network with an embedded vision for physical interactions, capable of adaptive kinesthesia and viscoelastic proprioception by learning kinetic features. This design enables passive adaptations to omni-directional interactions, visually captured by a miniature high-speed motion-tracking system embedded inside for proprioceptive learning. The results show that the soft network can infer real-time 6D forces and torques with accuracies of 0.25/0.24/0.35 N and 0.025/0.034/0.006 Nm in dynamic interactions. We also incorporate viscoelasticity in proprioception during static adaptation by adding a creep and relaxation modifier to refine the predicted results. The proposed soft network combines simplicity in design, omni-adaptation, and proprioceptive sensing with high accuracy, making it a versatile solution for robotics at a low material cost with more than one million use cycles for tasks such as sensitive and competitive grasping and touch-based geometry reconstruction. This study offers new insights into vision-based proprioception for soft robots in adaptive grasping, soft manipulation, and human-robot interaction.","PeriodicalId":501362,"journal":{"name":"The International Journal of Robotics Research","volume":"364 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140126732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-11DOI: 10.1177/02783649241237544
Aviv Adler, Oscar Mickelin, Ragesh K. Ramachandran, Gaurav S. Sukhatme, Sertac Karaman
When is heterogeneity in the composition of an autonomous robotic team beneficial and when is it detrimental? We investigate and answer this question in the context of a minimally viable model that examines the role of heterogeneous speeds in perimeter defense problems, where defenders share a total allocated speed budget. We consider two distinct problem settings and develop strategies based on dynamic programming and on local interaction rules. We present a theoretical analysis of both approaches and our results are extensively validated using simulations. Interestingly, our results demonstrate that the viability of heterogeneous teams depends on the amount of information available to the defenders. Moreover, our results suggest a universality property: across a wide range of problem parameters the optimal ratio of the speeds of the defenders remains nearly constant.
{"title":"The role of heterogeneity in autonomous perimeter defense problems","authors":"Aviv Adler, Oscar Mickelin, Ragesh K. Ramachandran, Gaurav S. Sukhatme, Sertac Karaman","doi":"10.1177/02783649241237544","DOIUrl":"https://doi.org/10.1177/02783649241237544","url":null,"abstract":"When is heterogeneity in the composition of an autonomous robotic team beneficial and when is it detrimental? We investigate and answer this question in the context of a minimally viable model that examines the role of heterogeneous speeds in perimeter defense problems, where defenders share a total allocated speed budget. We consider two distinct problem settings and develop strategies based on dynamic programming and on local interaction rules. We present a theoretical analysis of both approaches and our results are extensively validated using simulations. Interestingly, our results demonstrate that the viability of heterogeneous teams depends on the amount of information available to the defenders. Moreover, our results suggest a universality property: across a wide range of problem parameters the optimal ratio of the speeds of the defenders remains nearly constant.","PeriodicalId":501362,"journal":{"name":"The International Journal of Robotics Research","volume":"38 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140107649","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-09DOI: 10.1177/02783649241236204
Giuseppe Milazzo, Manuel G. Catalano, Antonio Bicchi, Giorgio Grioli
This study introduces an innovative design for a Variable Stiffness 3 Degrees of Freedom actuated wrist capable of actively and continuously adjusting its overall stiffness by modulating the active length of non-linear elastic elements. This modulation is akin to human muscular cocontraction and is achieved using only four motors. The mechanical configuration employed results in a compact and lightweight device with anthropomorphic characteristics, making it potentially suitable for applications such as prosthetics and humanoid robotics. This design aims to enhance performance in dynamic tasks, improve task adaptability, and ensure safety during interactions with both people and objects. The paper details the first hardware implementation of the proposed design, providing insights into the theoretical model, mechanical and electronic components, as well as the control architecture. System performance is assessed using a motion capture system. The results demonstrate that the prototype offers a broad range of motion ([55, −45]° for flexion/extension, ±48° for radial/ulnar deviation, and ±180° for pronation/supination) while having the capability to triple its stiffness. Furthermore, following proper calibration, the wrist posture can be reconstructed through multivariate linear regression using rotational encoders and the forward kinematic model. This reconstruction achieves an average Root Mean Square Error of 6.6°, with an R2 value of 0.93.
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