Luke F Gockowski,Charles Xiao,Amy Hao,Yangying Zhu,Bolin Liao,Megan T Valentine,Elliot W Hawkes
Actuation is a key challenge for the field of soft robotics. One method of actuation, thermally driven liquid-to-vapor phase change heat engines, is particularly compelling due to its high forces, large strokes, and relative simplicity. However, this form of actuation suffers from a very low efficiency, making its impact for practical applications limited. Here we apply thermodynamic analysis to these phase-change actuators to identify major inefficiencies and offer three key insights that soft roboticists can leverage to improve efficiency: (1) maximize the ratio of input power to heat loss, (2) operate at an intermediate temperature, and (3) maximize volumetric expansion. We confirm the validity of these insights via benchtop experiments and show efficiencies nearly two orders of magnitude higher than previously reported. We demonstrate the usefulness of these insights by applying them to the design and construction of a compliant roller powered directly by sunlight and capable of rolling every 16 s. Our results guide the design of more efficient phase-change actuators for soft robots and more generally, demonstrate the potential of applying thermodynamic analysis to improve the efficiency of soft actuators.
{"title":"Improving the Efficiency of Soft Phase-Change Actuators Using Thermodynamic Analysis.","authors":"Luke F Gockowski,Charles Xiao,Amy Hao,Yangying Zhu,Bolin Liao,Megan T Valentine,Elliot W Hawkes","doi":"10.1089/soro.2024.0139","DOIUrl":"https://doi.org/10.1089/soro.2024.0139","url":null,"abstract":"Actuation is a key challenge for the field of soft robotics. One method of actuation, thermally driven liquid-to-vapor phase change heat engines, is particularly compelling due to its high forces, large strokes, and relative simplicity. However, this form of actuation suffers from a very low efficiency, making its impact for practical applications limited. Here we apply thermodynamic analysis to these phase-change actuators to identify major inefficiencies and offer three key insights that soft roboticists can leverage to improve efficiency: (1) maximize the ratio of input power to heat loss, (2) operate at an intermediate temperature, and (3) maximize volumetric expansion. We confirm the validity of these insights via benchtop experiments and show efficiencies nearly two orders of magnitude higher than previously reported. We demonstrate the usefulness of these insights by applying them to the design and construction of a compliant roller powered directly by sunlight and capable of rolling every 16 s. Our results guide the design of more efficient phase-change actuators for soft robots and more generally, demonstrate the potential of applying thermodynamic analysis to improve the efficiency of soft actuators.","PeriodicalId":48685,"journal":{"name":"Soft Robotics","volume":"48 1","pages":""},"PeriodicalIF":7.9,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144547750","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}
Soft robots with motion sensing can achieve motion feedback and monitor environmental changes. Motion sensing significantly expands the potential applications of soft robots in exploration and other fields. This study investigates an inchworm-like miniature soft robot capable of rapid locomotion and autonomous terrain-adaptive exploration. The motion is enabled by two key innovations: (1) a piezoelectric driving body that harnesses the substantial expansion-contraction deformation through an enhanced geometric nonlinearity model, overcoming limitations of conventional small-deformation theories, and (2) the adhesive forces produced by electroadhesive pads. This robot can move rapidly on various substrates, reaching a maximum speed of 1.93 body lengths per second. Additionally, the robot exhibits excellent load-bearing capacity and robustness, capable of pushing a payload of 6.8 g (8.35 times its weight of 0.814 g) and resisting strong external forces. The robot shows environmental adaptability in different terrains, such as crawling on rough terrains (including sandpaper, Ra = 10.8 μm), passing through a circular pipe with an inner diameter of 92 mm, descending a 5 mm step, ascending slopes with a 28° inclination, and traversing narrow gaps with a height of 11.5 mm (0.38 times the robot's maximum body height). Furthermore, the integration of an inertial measurement unit (IMU) system provides the robot with motion sensing capabilities, facilitating real-time position detection and environmental mapping.
{"title":"An Inchworm-Inspired Fast-Moving Micro Flexible Robot for Autonomous Terrain-Adaptive Exploration.","authors":"Yingzhi Wang,Jiaquan Xu,Ziwen Tang,Yejia Wu,Qian Zhang,Hong Ding,Jin Xie","doi":"10.1089/soro.2025.0004","DOIUrl":"https://doi.org/10.1089/soro.2025.0004","url":null,"abstract":"Soft robots with motion sensing can achieve motion feedback and monitor environmental changes. Motion sensing significantly expands the potential applications of soft robots in exploration and other fields. This study investigates an inchworm-like miniature soft robot capable of rapid locomotion and autonomous terrain-adaptive exploration. The motion is enabled by two key innovations: (1) a piezoelectric driving body that harnesses the substantial expansion-contraction deformation through an enhanced geometric nonlinearity model, overcoming limitations of conventional small-deformation theories, and (2) the adhesive forces produced by electroadhesive pads. This robot can move rapidly on various substrates, reaching a maximum speed of 1.93 body lengths per second. Additionally, the robot exhibits excellent load-bearing capacity and robustness, capable of pushing a payload of 6.8 g (8.35 times its weight of 0.814 g) and resisting strong external forces. The robot shows environmental adaptability in different terrains, such as crawling on rough terrains (including sandpaper, Ra = 10.8 μm), passing through a circular pipe with an inner diameter of 92 mm, descending a 5 mm step, ascending slopes with a 28° inclination, and traversing narrow gaps with a height of 11.5 mm (0.38 times the robot's maximum body height). Furthermore, the integration of an inertial measurement unit (IMU) system provides the robot with motion sensing capabilities, facilitating real-time position detection and environmental mapping.","PeriodicalId":48685,"journal":{"name":"Soft Robotics","volume":"2 1","pages":""},"PeriodicalIF":7.9,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144521498","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}
Jilei Liu,Zhiyin Xu,Jinyu Lu,Jiangjun Hou,Xun Gu,Jiarong Wu,Na Li
Soft spherical tensegrity robots exhibit many desirable properties, including impact resistance and extreme lightweight, which give them strong potential for operation in complex environments such as search and rescue missions and space exploration. However, existing spherical tensegrity robots are still unable to achieve 100% exploration in unknown and complex terrains. In this study, we present a 10-bar soft spherical tensegrity robot based on dodecahedron tensegrity (TR-10) with multiple movement gaits. It can generate a rolling motion by actively changing the length of the internal drive module, and the MATLAB dynamic model is established for simulation. The multi-objective optimization method is used to obtain the driving strategies for various basic gaits of the TR-10. The generated movement paths, formed by combining gaits, can fully cover the map. At the same time, the method for determining the rolling axis is proposed, which can enable the robot to roll to the target point along the optimal path. Finally, we fabricated the TR-10 prototype capable of a wireless-controlled rolling motion. By comparing the simulation and experimental results of the basic gaits and movement paths, the effectiveness of the proposed method is verified. In addition, we also compare it with the classical 6-bar 24-cable tensegrity robot, and the results show that our proposed TR-10 can complete different paths with shorter distances and smaller offsets.
{"title":"Rolling Motion of Bar-Driven Soft Spherical Tensegrity Robot Based on Dodecahedron.","authors":"Jilei Liu,Zhiyin Xu,Jinyu Lu,Jiangjun Hou,Xun Gu,Jiarong Wu,Na Li","doi":"10.1089/soro.2024.0126","DOIUrl":"https://doi.org/10.1089/soro.2024.0126","url":null,"abstract":"Soft spherical tensegrity robots exhibit many desirable properties, including impact resistance and extreme lightweight, which give them strong potential for operation in complex environments such as search and rescue missions and space exploration. However, existing spherical tensegrity robots are still unable to achieve 100% exploration in unknown and complex terrains. In this study, we present a 10-bar soft spherical tensegrity robot based on dodecahedron tensegrity (TR-10) with multiple movement gaits. It can generate a rolling motion by actively changing the length of the internal drive module, and the MATLAB dynamic model is established for simulation. The multi-objective optimization method is used to obtain the driving strategies for various basic gaits of the TR-10. The generated movement paths, formed by combining gaits, can fully cover the map. At the same time, the method for determining the rolling axis is proposed, which can enable the robot to roll to the target point along the optimal path. Finally, we fabricated the TR-10 prototype capable of a wireless-controlled rolling motion. By comparing the simulation and experimental results of the basic gaits and movement paths, the effectiveness of the proposed method is verified. In addition, we also compare it with the classical 6-bar 24-cable tensegrity robot, and the results show that our proposed TR-10 can complete different paths with shorter distances and smaller offsets.","PeriodicalId":48685,"journal":{"name":"Soft Robotics","volume":"57 1","pages":""},"PeriodicalIF":7.9,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144146192","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}
Proprioception in soft robots is essential for enabling autonomous behaviors, allowing them to navigate and interact safely in unstructured environments. Previous sensorization-based shape reconstruction methods, which often rely on machine learning techniques, have limitations in their broad applicability for different robotic systems and environments. In this work, we present a shape reconstruction scheme enabled by sparsely distributed soft strain sensors on the surfaces of soft robots, combined with a model-based reconstruction framework. Our approach utilizes miniaturized stretchable capacitive strain sensors with large stretchability and low hysteresis, which can be easily attached to soft robot surfaces for accurate local strain measurements. These measurements are fed into an optimization algorithm with embedded mechanical constraints. Our approach can predict all deformation modes in a soft bar with a maximum displacement error of less than 4% of the bar length and accurately reconstruct the shapes of soft pneumatic grippers during grasping actions. Additional reconstructions of a bioinspired arm in complex contact scenarios further demonstrate the versatility of our approach. This shape reconstruction scheme using distributed strain sensors offers a convenient and broadly applicable solution for enhancing proprioception in soft robots.
{"title":"Model-Based 3D Shape Reconstruction of Soft Robots via Distributed Strain Sensing.","authors":"Liangshu Liu,Xinghao Huang,Xiaoci Zhang,Baiyu Zhang,Hao Xu,Vedanshee Mihir Trivedi,Kenneth Liu,Zoheb Shaikh,Hangbo Zhao","doi":"10.1089/soro.2024.0158","DOIUrl":"https://doi.org/10.1089/soro.2024.0158","url":null,"abstract":"Proprioception in soft robots is essential for enabling autonomous behaviors, allowing them to navigate and interact safely in unstructured environments. Previous sensorization-based shape reconstruction methods, which often rely on machine learning techniques, have limitations in their broad applicability for different robotic systems and environments. In this work, we present a shape reconstruction scheme enabled by sparsely distributed soft strain sensors on the surfaces of soft robots, combined with a model-based reconstruction framework. Our approach utilizes miniaturized stretchable capacitive strain sensors with large stretchability and low hysteresis, which can be easily attached to soft robot surfaces for accurate local strain measurements. These measurements are fed into an optimization algorithm with embedded mechanical constraints. Our approach can predict all deformation modes in a soft bar with a maximum displacement error of less than 4% of the bar length and accurately reconstruct the shapes of soft pneumatic grippers during grasping actions. Additional reconstructions of a bioinspired arm in complex contact scenarios further demonstrate the versatility of our approach. This shape reconstruction scheme using distributed strain sensors offers a convenient and broadly applicable solution for enhancing proprioception in soft robots.","PeriodicalId":48685,"journal":{"name":"Soft Robotics","volume":"11 1","pages":""},"PeriodicalIF":7.9,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144122195","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}
This article introduces the design and development of a modular soft robot capable of performing multiple movement modes. The core unit module features a four-chamber soft structure, separated by a cross-shaped thin plate. By selectively applying pneumatic pressure to different chambers and changing connector configurations, the robot achieves diverse modular configurations and movement modes, enabling it to adapt to various environments. To address the challenges posed by the material's nonlinear behavior and its infinite degrees of freedom, a three-dimensional spatial mathematical modeling approach is proposed. This method, grounded in classical plate theory and the chained composite model, establishes a static model for the soft robot's spatial bending motion with constant curvature. In addition, a single-controller framework based on a central pattern generator is developed to facilitate the generation of multiple movement gaits. By tuning parameters such as oscillator phase, frequency, load factor, and amplitude, the controller can generate a wide range of movement patterns. To validate the proposed theoretical and experimental models, we developed a pneumatic control platform that demonstrated the robot's multimodal locomotion capabilities through systematic testing in terrains with varying complexity.
{"title":"Design and Motion Analysis of a Soft Modular Robot for Diverse Environments.","authors":"Yu Zhang,Yu Li,Dongbao Sui,Lingkai Luan,Tianjiao Zheng,Zongwei Zhang,Sikai Zhao,Fuyue Zhang,Dongjie Li,Yanhe Zhu","doi":"10.1089/soro.2025.0002","DOIUrl":"https://doi.org/10.1089/soro.2025.0002","url":null,"abstract":"This article introduces the design and development of a modular soft robot capable of performing multiple movement modes. The core unit module features a four-chamber soft structure, separated by a cross-shaped thin plate. By selectively applying pneumatic pressure to different chambers and changing connector configurations, the robot achieves diverse modular configurations and movement modes, enabling it to adapt to various environments. To address the challenges posed by the material's nonlinear behavior and its infinite degrees of freedom, a three-dimensional spatial mathematical modeling approach is proposed. This method, grounded in classical plate theory and the chained composite model, establishes a static model for the soft robot's spatial bending motion with constant curvature. In addition, a single-controller framework based on a central pattern generator is developed to facilitate the generation of multiple movement gaits. By tuning parameters such as oscillator phase, frequency, load factor, and amplitude, the controller can generate a wide range of movement patterns. To validate the proposed theoretical and experimental models, we developed a pneumatic control platform that demonstrated the robot's multimodal locomotion capabilities through systematic testing in terrains with varying complexity.","PeriodicalId":48685,"journal":{"name":"Soft Robotics","volume":"124 1","pages":""},"PeriodicalIF":7.9,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143932841","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}
Tianze Hao,Yue Ma,Jingjing Feng,Songtao Liu,Jutao Wang
Dealing with grasping tasks in unstructured environments, existing soft grippers often exhibit a lack of static stability, while rigid-soft hybrid grippers display limited compliance due to the fixed connections at the joints. To address the challenge of balancing static stability and flexible adaptability, this study designs and implements a bioinspired hybrid gripper combining soft and rigid elements. The gripper draws inspiration from the collateral ligaments and joint capsule structures of human fingers. It employs a tendon-driven mechanism that ensures high static stability while enabling a large range of flexion movements and some degree of deflection, mimicking the dynamic bending of a human finger. Experimental results demonstrate that the hybrid fingers excel in terms of static stability, working range, and output force. Notably, under conditions of extensor tendon pretension, the fingers exhibit finer motion toward the fingertips. The dual-finger gripper performs exceptionally well in various grasping tasks, stably grasping objects of different shapes and weights, such as the Evolved Grasp Analysis Dataset and common daily items. This study offers a novel and straightforward design approach for the development of bioinspired fingers and high-performance robots, holding broad application prospects.
{"title":"Design and Implementation of a Soft-Rigid Hybrid Gripper with Bionic Ligaments and Joint Capsule.","authors":"Tianze Hao,Yue Ma,Jingjing Feng,Songtao Liu,Jutao Wang","doi":"10.1089/soro.2024.0095","DOIUrl":"https://doi.org/10.1089/soro.2024.0095","url":null,"abstract":"Dealing with grasping tasks in unstructured environments, existing soft grippers often exhibit a lack of static stability, while rigid-soft hybrid grippers display limited compliance due to the fixed connections at the joints. To address the challenge of balancing static stability and flexible adaptability, this study designs and implements a bioinspired hybrid gripper combining soft and rigid elements. The gripper draws inspiration from the collateral ligaments and joint capsule structures of human fingers. It employs a tendon-driven mechanism that ensures high static stability while enabling a large range of flexion movements and some degree of deflection, mimicking the dynamic bending of a human finger. Experimental results demonstrate that the hybrid fingers excel in terms of static stability, working range, and output force. Notably, under conditions of extensor tendon pretension, the fingers exhibit finer motion toward the fingertips. The dual-finger gripper performs exceptionally well in various grasping tasks, stably grasping objects of different shapes and weights, such as the Evolved Grasp Analysis Dataset and common daily items. This study offers a novel and straightforward design approach for the development of bioinspired fingers and high-performance robots, holding broad application prospects.","PeriodicalId":48685,"journal":{"name":"Soft Robotics","volume":"33 1","pages":""},"PeriodicalIF":7.9,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143862049","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}
Miniature robots are increasingly used in unstructured environments and require higher mobility, robustness, and multifunctionality. However, existing purely soft and rigid designs suffer from inherent defects, such as low load capacity and compliance, respectively, restricting their functionality and performance. Here, we report new soft-rigid hybrid miniature robots applying the tensegrity principle, inspired by biological organisms' remarkable multifunctionality through tensegrity micro-structures. The miniature robot's speed of 25.07 body lengths per second is advanced among published miniature robots and tensegrity robots. The design versatility is demonstrated by constructing three bio-inspired robots using miniature tensegrity joints. Due to its internal load-transfer mechanisms, the robot has self-adaptability, deformability, and high impact resistance (withstand dynamic load 143,868 times the robot weight), enabling the robot to navigate diverse barriers, pipelines, and channels. The robot can vary its stiffness to greatly improve load capacity and motion performance. We further demonstrate the potential biomedical applications, such as drug delivery, impurity removal, and remote heating achieved by integrating metal into the robot.
{"title":"Untethered Miniature Tensegrity Robot with Tunable Stiffness for High-Speed and Adaptive Locomotion.","authors":"Bingxing Chen,Zhiyu He,Fang Ye,Yi Yang,Wenhu Chen,Fuhui Ding,Dan Gao,Yi Zhao,Zongxing Lu,Chao Jia","doi":"10.1089/soro.2024.0178","DOIUrl":"https://doi.org/10.1089/soro.2024.0178","url":null,"abstract":"Miniature robots are increasingly used in unstructured environments and require higher mobility, robustness, and multifunctionality. However, existing purely soft and rigid designs suffer from inherent defects, such as low load capacity and compliance, respectively, restricting their functionality and performance. Here, we report new soft-rigid hybrid miniature robots applying the tensegrity principle, inspired by biological organisms' remarkable multifunctionality through tensegrity micro-structures. The miniature robot's speed of 25.07 body lengths per second is advanced among published miniature robots and tensegrity robots. The design versatility is demonstrated by constructing three bio-inspired robots using miniature tensegrity joints. Due to its internal load-transfer mechanisms, the robot has self-adaptability, deformability, and high impact resistance (withstand dynamic load 143,868 times the robot weight), enabling the robot to navigate diverse barriers, pipelines, and channels. The robot can vary its stiffness to greatly improve load capacity and motion performance. We further demonstrate the potential biomedical applications, such as drug delivery, impurity removal, and remote heating achieved by integrating metal into the robot.","PeriodicalId":48685,"journal":{"name":"Soft Robotics","volume":"20 1","pages":""},"PeriodicalIF":7.9,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143841125","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}
Ting Rang Ling,Bryan Jun Sheng Lee,Chee Pin Tan,Surya Girinatha Nurzaman,Mohammed Ayoub Juman
Despite the development of numerous soft grippers designed to handle deformable objects, hardness sensing remains a challenge, yet it is essential for various applications such as product selection or sorting, assessing fruit ripeness, or food quality control. This research introduces GripDepthSense3DNet, an innovative approach integrating 3D depth sensing with machine learning for accurate hardness sensing during grasping. Leveraging a dataset comprising of depth images of diverse objects undergoing deformation, the proposed novel network is trained to capture intricate spatial-temporal deformation features from a series of depth images. GripDepthSense3DNet outperforms state-of-the-art networks, exhibiting a commendable mean absolute percentage error of 0.46% for trained shapes and hardness. Specifically, the model achieves a reduction in parameters of approximately 94.8% compared to ResNet-50, with a training time that is around 92.9% shorter on equivalent hardware. Different depth ranges and intervals were studied to eventually arrive at an optimal configuration. Through dynamic tuning, the network's ability to seamlessly incorporate new shapes, new hardness, and even intricate arbitrary objects highlights the adaptability of the approach.
{"title":"GripDepthSense3DNet: A Depth-Enabled Hardness Sensing Framework in Soft Robotic Grasping.","authors":"Ting Rang Ling,Bryan Jun Sheng Lee,Chee Pin Tan,Surya Girinatha Nurzaman,Mohammed Ayoub Juman","doi":"10.1089/soro.2024.0046","DOIUrl":"https://doi.org/10.1089/soro.2024.0046","url":null,"abstract":"Despite the development of numerous soft grippers designed to handle deformable objects, hardness sensing remains a challenge, yet it is essential for various applications such as product selection or sorting, assessing fruit ripeness, or food quality control. This research introduces GripDepthSense3DNet, an innovative approach integrating 3D depth sensing with machine learning for accurate hardness sensing during grasping. Leveraging a dataset comprising of depth images of diverse objects undergoing deformation, the proposed novel network is trained to capture intricate spatial-temporal deformation features from a series of depth images. GripDepthSense3DNet outperforms state-of-the-art networks, exhibiting a commendable mean absolute percentage error of 0.46% for trained shapes and hardness. Specifically, the model achieves a reduction in parameters of approximately 94.8% compared to ResNet-50, with a training time that is around 92.9% shorter on equivalent hardware. Different depth ranges and intervals were studied to eventually arrive at an optimal configuration. Through dynamic tuning, the network's ability to seamlessly incorporate new shapes, new hardness, and even intricate arbitrary objects highlights the adaptability of the approach.","PeriodicalId":48685,"journal":{"name":"Soft Robotics","volume":"113 1","pages":""},"PeriodicalIF":7.9,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143841127","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}
Daniel Feliu-Talegon,Yusuf Abdullahi Adamu,Anup Teejo Mathew,Abdulaziz Y Alkayas,Federico Renda
Soft robots and bioinspired systems have revolutionized robot design by incorporating flexibility and deformable materials inspired by nature's ingenious designs. Similar to many robotic applications, sensing and perception are paramount to enable soft robots to adeptly navigate the unpredictable real world, ensuring safe interactions with both humans and the environment. Despite recent progress, soft robot sensorization still faces significant challenges due to the virtual infinite degrees of freedom of the system and the need for efficient computational models capable of estimating valuable information from sensor data. In this article, we present a new model-based proprioceptive system for slender soft robots based on strain sensing and a strain-based modeling approach called Geometric Variable-Strain (GVS). We develop a flexible 2-Plate 6D strain sensor (Flex-2P6D) capable of measuring the 6 dimensions (6D) strain at specific points of the soft robot with an accuracy higher than 95%. Coupled with the GVS approach, the proposed methodology is able to directly measure the configuration variables and reconstruct complex robot shapes with very high accuracy, even in very challenging conditions. The sensors are embedded inside the soft body, which makes them also suitable for underwater operation and physical interaction with the environment. Something that we also demonstrate experimentally. We believe that our approach has the potential to be applied across a wide variety of applications, including observation and exploration missions, as well as human-robot interaction, where the states of the system are required for implementing precise closed-loop control and estimation methods.
{"title":"Advancing Soft Robot Proprioception Through 6D Strain Sensors Embedding.","authors":"Daniel Feliu-Talegon,Yusuf Abdullahi Adamu,Anup Teejo Mathew,Abdulaziz Y Alkayas,Federico Renda","doi":"10.1089/soro.2024.0017","DOIUrl":"https://doi.org/10.1089/soro.2024.0017","url":null,"abstract":"Soft robots and bioinspired systems have revolutionized robot design by incorporating flexibility and deformable materials inspired by nature's ingenious designs. Similar to many robotic applications, sensing and perception are paramount to enable soft robots to adeptly navigate the unpredictable real world, ensuring safe interactions with both humans and the environment. Despite recent progress, soft robot sensorization still faces significant challenges due to the virtual infinite degrees of freedom of the system and the need for efficient computational models capable of estimating valuable information from sensor data. In this article, we present a new model-based proprioceptive system for slender soft robots based on strain sensing and a strain-based modeling approach called Geometric Variable-Strain (GVS). We develop a flexible 2-Plate 6D strain sensor (Flex-2P6D) capable of measuring the 6 dimensions (6D) strain at specific points of the soft robot with an accuracy higher than 95%. Coupled with the GVS approach, the proposed methodology is able to directly measure the configuration variables and reconstruct complex robot shapes with very high accuracy, even in very challenging conditions. The sensors are embedded inside the soft body, which makes them also suitable for underwater operation and physical interaction with the environment. Something that we also demonstrate experimentally. We believe that our approach has the potential to be applied across a wide variety of applications, including observation and exploration missions, as well as human-robot interaction, where the states of the system are required for implementing precise closed-loop control and estimation methods.","PeriodicalId":48685,"journal":{"name":"Soft Robotics","volume":"10 1","pages":""},"PeriodicalIF":7.9,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142991836","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}
Soft magnetic robots have attracted extensive research interest recently due to their fast-transforming ability and programmability. Although the inherent softness of the matrix materials enables dexterity and safe interactions, the contradiction between the easy shape transformation of the soft matrices and load carrying capacity, as well as the difficulty of independently controllable motion of individual segments, severely limits its design space and application potentials. Herein, we have proposed a strategy to adjust the modulus of shape memory polymer composite embedded with hard magnetic particles by in situ Joule heating of printed circuit, which can reversibly change the stiffness from 4.1 GPa at 25°C to 10.9 MPa at 70°C. The stiffness tunable magnetic robots realize the compatibility of fast reversible shape transformation and high load carrying capacity. Furthermore, multiple separated Joule circuits are designed for the independently controllable motion of individual segments. The simulation of Joule heating and magnetic actuation is used to guide the design of devices. The concept of simultaneously programming magnetic anisotropy and stiffness proposed in this work greatly expands the design space and new applications of magnetic actuators, including soft grippers for heavy loads and bionic hand with independent motion of fingers.
{"title":"Stiffness Tunable Magnetic Actuators Based on Shape Memory Polymer/NdFeB Composite for Segmental Controllable Motion.","authors":"Weifan Zhou,Lei Fu,Yangyong Zhao,Hao Zhu,Shengzhao Li,Lu Peng,Jingyi Xu,Shihao Deng,Zhen Zhou,Tie Li,Ting Zhang","doi":"10.1089/soro.2023.0184","DOIUrl":"https://doi.org/10.1089/soro.2023.0184","url":null,"abstract":"Soft magnetic robots have attracted extensive research interest recently due to their fast-transforming ability and programmability. Although the inherent softness of the matrix materials enables dexterity and safe interactions, the contradiction between the easy shape transformation of the soft matrices and load carrying capacity, as well as the difficulty of independently controllable motion of individual segments, severely limits its design space and application potentials. Herein, we have proposed a strategy to adjust the modulus of shape memory polymer composite embedded with hard magnetic particles by in situ Joule heating of printed circuit, which can reversibly change the stiffness from 4.1 GPa at 25°C to 10.9 MPa at 70°C. The stiffness tunable magnetic robots realize the compatibility of fast reversible shape transformation and high load carrying capacity. Furthermore, multiple separated Joule circuits are designed for the independently controllable motion of individual segments. The simulation of Joule heating and magnetic actuation is used to guide the design of devices. The concept of simultaneously programming magnetic anisotropy and stiffness proposed in this work greatly expands the design space and new applications of magnetic actuators, including soft grippers for heavy loads and bionic hand with independent motion of fingers.","PeriodicalId":48685,"journal":{"name":"Soft Robotics","volume":"27 1","pages":""},"PeriodicalIF":7.9,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142989331","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：