Self-localization is a fundamental requirement for the mobile robot. Robot usually contains a large number of dif- ferent sensors, which provide the information of robot localization, and all the sensor information sh...Self-localization is a fundamental requirement for the mobile robot. Robot usually contains a large number of dif- ferent sensors, which provide the information of robot localization, and all the sensor information should be considered for the optimal location. Kalman filter is efficient to realize the information fusion. Used as an efficient sensor fusion algorithm, Kalman filter is an advanced filtering technique which can reduce errors of the position and orientation of the sensors. Kalman filter has been paied much attention to robot automation and solutions to solve uncertainties such as robot localization, navigation, following, tracking, motion control, estimation and prediction. The paper briefly describes Kalman filter theory, and establishes a simple mathematical model based on muti-sensor mobile robot. Meanwhile, Kalman filter is used in robot self-localization by simulations, and it is demonstrated by simulations that Kalman filter is effective.展开更多
An on-the-fly,self-localization system is developed for mobile robot which is operative in a 3D environment with elaborative 3D landmarks.The robot estimates its pose recursively through a MAP estimator that incorpora...An on-the-fly,self-localization system is developed for mobile robot which is operative in a 3D environment with elaborative 3D landmarks.The robot estimates its pose recursively through a MAP estimator that incorporates the information collected from odometry and unidirectional camera.We build the nonlinear models for these two sensors and maintain that the uncertainty manipulation of robot motion and inaccurate sensor measurements should be embedded and tracked throughout our system.We describe the uncertainty framework in a probabilistic geometry viewpoint and use unscented transform to propagate the uncertainty,which undergoes the given nonlinear functions.Considering the processing power of our robot,image features are extracted in the vicinity of corresponding projected features.In addition,data associations are evaluated by statistical distance.Finally,a series of systematic experiments are conducted to prove the reliable and accurate performance of our system.展开更多
A practical serf-localization scheme for mobile robots is proposed and implemented by utilizing sonar sensors. Specifically, the localization problem is solved by employing Monte Carlo method with a new mechanism prop...A practical serf-localization scheme for mobile robots is proposed and implemented by utilizing sonar sensors. Specifically, the localization problem is solved by employing Monte Carlo method with a new mechanism proposed to calculate the samples＇ weights; the convergence and veracity of the sample set are guaranteed by the designed resampling and scattering process. The proposed serf-localization algorithm is fully implemented on a specific mobile robot system, and experimental results illustrate that it provides an efficient solution for the kidnapped problem.展开更多
Self-localization is one of the most important aspects for using mobile robots in unstructured environments. In this paper, the authors introduce a new approach for a self-localization and navigation unit for mobile p...Self-localization is one of the most important aspects for using mobile robots in unstructured environments. In this paper, the authors introduce a new approach for a self-localization and navigation unit for mobile platforms in extraterrestrial environments, based on the authors＂ successful results in self-localization of forestry machines on earth. The presented approach is developed from a highly modular concept, which allows a simple but efficient adaption to specific applications by just substituting some scenario dependent components. In this paper, the authors will explain the general concept and the terrestrial implementation so far. On this basis, the authors will demonstrate and discuss the necessary adaptions to the general concept in order to handle the different conditions on extraterrestrial surfaces.展开更多
Single-cell biomechanics and electrophysiology measuring tools have transformed biological research over the last few decades,which enabling a comprehensive and nuanced understanding of cellular behavior and function....Single-cell biomechanics and electrophysiology measuring tools have transformed biological research over the last few decades,which enabling a comprehensive and nuanced understanding of cellular behavior and function.Despite their high-quality information content,these single-cell measuring techniques suffer from laborious manual processing by highly skilled workers and extremely low throughput(tens of cells per day).Recently,numerous researchers have automated the measurement of cell mechanical and electrical signals through robotic localization and control processes.While these efforts have demonstrated promising progress,critical challenges persist,including human dependency,learning complexity,in-situ measurement,and multidimensional signal acquisition.To identify key limitations and highlight emerging opportunities for innovation,in this review,we comprehensively summarize the key steps of robotic technologies in single-cell biomechanics and electrophysiology.We also discussed the prospects and challenges of robotics and automation in biological research.By bridging gaps between engineering,biology,and data science,this work aims to stimulate interdisciplinary research and accelerate the translation of robotic single-cell technologies into practical applications in the life sciences and medical fields.展开更多
Underwater pipeline inspection plays a vital role in the proactive maintenance and management of critical marine infrastructure and subaquatic systems.However,the inspection of underwater pipelines presents a challeng...Underwater pipeline inspection plays a vital role in the proactive maintenance and management of critical marine infrastructure and subaquatic systems.However,the inspection of underwater pipelines presents a challenge due to factors such as light scattering,absorption,restricted visibility,and ambient noise.The advancement of deep learning has introduced powerful techniques for processing large amounts of unstructured and imperfect data collected from underwater environments.This study evaluated the efficacy of the You Only Look Once(YOLO)algorithm,a real-time object detection and localization model based on convolutional neural networks,in identifying and classifying various types of pipeline defects in underwater settings.YOLOv8,the latest evolution in the YOLO family,integrates advanced capabilities,such as anchor-free detection,a cross-stage partial network backbone for efficient feature extraction,and a feature pyramid network+path aggregation network neck for robust multi-scale object detection,which make it particularly well-suited for complex underwater environments.Due to the lack of suitable open-access datasets for underwater pipeline defects,a custom dataset was captured using a remotely operated vehicle in a controlled environment.This application has the following assets available for use.Extensive experimentation demonstrated that YOLOv8 X-Large consistently outperformed other models in terms of pipe defect detection and classification and achieved a strong balance between precision and recall in identifying pipeline cracks,rust,corners,defective welds,flanges,tapes,and holes.This research establishes the baseline performance of YOLOv8 for underwater defect detection and showcases its potential to enhance the reliability and efficiency of pipeline inspection tasks in challenging underwater environments.展开更多
Strategically coupling nanoparticle hybrids and internal thermosensitive molecular switches establishes an innovative paradigm for constructing micro/nanoscale-reconfigurable robots,facilitating energyefficient CO_(2)...Strategically coupling nanoparticle hybrids and internal thermosensitive molecular switches establishes an innovative paradigm for constructing micro/nanoscale-reconfigurable robots,facilitating energyefficient CO_(2) management in life-support systems of confined space.Here,a micro/nano-reconfigurable robot is constructed from the CO_(2) molecular hunters,temperature-sensitive molecular switch,solar photothermal conversion,and magnetically-driven function engines.The molecular hunters within the molecular extension state can capture 6.19 mmol g^(−1) of CO_(2) to form carbamic acid and ammonium bicarbonate.Interestingly,the molecular switch of the robot activates a molecular curling state that facilitates CO_(2) release through nano-reconfiguration,which is mediated by the temperature-sensitive curling of Pluronic F127 molecular chains during the photothermal desorption.Nano-reconfiguration of robot alters the amino microenvironment,including increasing surface electrostatic potential of the amino group and decreasing overall lowest unoccupied molecular orbital energy level.This weakened the nucleophilic attack ability of the amino group toward the adsorption product derivatives,thereby inhibiting the side reactions that generate hard-to-decompose urea structures,achieving the lowest regeneration temperature of 55℃ reported to date.The engine of the robot possesses non-contact magnetically-driven micro-reconfiguration capability to achieve efficient photothermal regeneration while avoiding local overheating.Notably,the robot successfully prolonged the survival time of mice in the sealed container by up to 54.61%,effectively addressing the issue of carbon suffocation in confined spaces.This work significantly enhances life-support systems for deep-space exploration,while stimulating innovations in sustainable carbon management technologies for terrestrial extreme environments.展开更多
At present,energy consumption is one of the main bottlenecks in autonomous mobile robot development.To address the challenge of high energy consumption in path planning for autonomous mobile robots navigating unknown ...At present,energy consumption is one of the main bottlenecks in autonomous mobile robot development.To address the challenge of high energy consumption in path planning for autonomous mobile robots navigating unknown and complex environments,this paper proposes an Attention-Enhanced Dueling Deep Q-Network(ADDueling DQN),which integrates a multi-head attention mechanism and a prioritized experience replay strategy into a Dueling-DQN reinforcement learning framework.A multi-objective reward function,centered on energy efficiency,is designed to comprehensively consider path length,terrain slope,motion smoothness,and obstacle avoidance,enabling optimal low-energy trajectory generation in 3D space from the source.The incorporation of a multihead attention mechanism allows the model to dynamically focus on energy-critical state features—such as slope gradients and obstacle density—thereby significantly improving its ability to recognize and avoid energy-intensive paths.Additionally,the prioritized experience replay mechanism accelerates learning from key decision-making experiences,suppressing inefficient exploration and guiding the policy toward low-energy solutions more rapidly.The effectiveness of the proposed path planning algorithm is validated through simulation experiments conducted in multiple off-road scenarios.Results demonstrate that AD-Dueling DQN consistently achieves the lowest average energy consumption across all tested environments.Moreover,the proposed method exhibits faster convergence and greater training stability compared to baseline algorithms,highlighting its global optimization capability under energy-aware objectives in complex terrains.This study offers an efficient and scalable intelligent control strategy for the development of energy-conscious autonomous navigation systems.展开更多
A unilateral self-locking mechanism(USM) was proposed to increase the tractive ability of the inchworm in-pipe robots for pipeline inspection.The USM was basically composed of a cam,a torsional spring and an axis.The ...A unilateral self-locking mechanism(USM) was proposed to increase the tractive ability of the inchworm in-pipe robots for pipeline inspection.The USM was basically composed of a cam,a torsional spring and an axis.The self-locking and virtual work principles were applied to studying the basic self-locking condition of the USM.In order to make the cooperation between the crutch and telescopic mechanism more harmonical,the unlocking time of the USM was calculated.A set of parameters were selected to build a virtual model and fabricate a prototype.Both the simulation and performance experiments were carried out in a pipe with a nominal inside diameter of 160 mm.The results show that USM enables the robot to move quickly in one way,and in the other way it helps the robot get self-locking with the pipe wall.The traction of the inchworm robot can rise to 1.2 kN,beyond the limitation of friction of 0.497 kN.展开更多
The follow-up application of underwater wireless sensor network is influenced by accuracy of self-localization of nodes. The self-localization of nodes is discussed in this paper. First of all, nodes of underwater wir...The follow-up application of underwater wireless sensor network is influenced by accuracy of self-localization of nodes. The self-localization of nodes is discussed in this paper. First of all, nodes of underwater wireless sensor network are classified into several levels according to the accuracy of position of nodes and the levels are from the first to the fifth in accordance with accuracy of nodes from high to low respectively. Secondly, the level of anchor nodes can be known by those unknown nodes from the information given by the anchor nodes themselves, At the same time the unknown nodes are able to be located in the area controlled by the first level of anchor nodes that are as the aggregation. Then the positioning algorithm is designed correspondingly in accordance with the accuracy level of nodes. Finally, the positioning algorithm is simulated and analyzed. The result shows that the unknown nodes can be located effectively by hierarchical control.展开更多
When a group of mobile agents track a target,they can locate themselves and the target in a cooperative manner.To maximize the group advantage,a parallel integration strategy of cooperative target-localization(CTL)and...When a group of mobile agents track a target,they can locate themselves and the target in a cooperative manner.To maximize the group advantage,a parallel integration strategy of cooperative target-localization(CTL)and cooperative self-localization(CSL)is designed.Firstly,a global cost function containing the agents’positions and the target’s position is established.Secondly,along with the agents’positions being re-estimated during CTL,the Utransform is employed to propagate the error covariance of the position estimations among the agents.The simulation results show that,the proposal exploits more information for locating the target and the agents than the cases where CTL and CSL run separately,and the global optimal position estimations of the agents and the target are obtained.展开更多
This paper develops a new algorithm for sensor network self-localization, which is an enhanced version of the existing Crocco’s method in [11]. The algorithm explores the noisy time of flight (TOF) measurements that ...This paper develops a new algorithm for sensor network self-localization, which is an enhanced version of the existing Crocco’s method in [11]. The algorithm explores the noisy time of flight (TOF) measurements that quantify the distances between sensor nodes to be localized and sources also at unknown positions. The newly proposed technique first obtains rough estimates of the sensor node and source positions, and then it refines the estimates via a least squares estimator (LSE). The LSE takes into account the geometrical constraints introduced by the desired global coordinate system to improve performance. Simulations show that the new technique offers superior localization accuracy over the original Crocco’s algorithm under small measurement noise condition.展开更多
Large language models(LLMs)have undergone significant expansion and have been increasingly integrated across various domains.Notably,in the realm of robot task planning,LLMs harness their advanced reasoning and langua...Large language models(LLMs)have undergone significant expansion and have been increasingly integrated across various domains.Notably,in the realm of robot task planning,LLMs harness their advanced reasoning and language comprehension capabilities to formulate precise and efficient action plans based on natural language instructions.However,for embodied tasks,where robots interact with complex environments,textonly LLMs often face challenges due to a lack of compatibility with robotic visual perception.This study provides a comprehensive overview of the emerging integration of LLMs and multimodal LLMs into various robotic tasks.Additionally,we propose a framework that utilizes multimodal GPT-4V to enhance embodied task planning through the combination of natural language instructions and robot visual perceptions.Our results,based on diverse datasets,indicate that GPT-4V effectively enhances robot performance in embodied tasks.This extensive survey and evaluation of LLMs and multimodal LLMs across a variety of robotic tasks enriches the understanding of LLM-centric embodied intelligence and provides forward-looking insights towards bridging the gap in Human-Robot-Environment interaction.展开更多
Wireless millirobots engineered to infiltrate intricate vascular networks within living organisms,particularly within constricted and confined spaces,hold immense promise for the future of medical treatments.However,w...Wireless millirobots engineered to infiltrate intricate vascular networks within living organisms,particularly within constricted and confined spaces,hold immense promise for the future of medical treatments.However,with their multifaceted and intricate designs,some robots often grapple with motion and functionality issues when confronted with tight spaces characterized by small cross-sectional dimensions.In this study,drawing inspiration from the high aspect ratio and undulating swimming patterns of snakes,a millimeter-scale,snake-like robot was designed and fabricated via a combination of extrusion-based four-dimensional(4D)printing and magnetic-responsive intelligent functional inks.A sophisticated motion control strategy was also developed,which enables the robots to perform various dynamic movements,such as undulating swimming,precise turns,graceful circular motions,and coordinated cluster movements,under diverse magnetic field variations.As a potential application,the snake robot can navigate and release drugs in a model coronary intervention vessel with tortuous channels and fluid filling.The novel design and promising applications of this snake robot are invaluable tools in future medical surgeries and interventions.展开更多
OBJECTIVE:To propose an automatic acupuncture robot system for performing acupuncture operations.METHODS:The acupuncture robot system consists of three components:automatic acupoint localization,acupuncture manipulati...OBJECTIVE:To propose an automatic acupuncture robot system for performing acupuncture operations.METHODS:The acupuncture robot system consists of three components:automatic acupoint localization,acupuncture manipulations,and De Qi sensation detection.The OptiTrack motion capture system is used to locate acupoints,which are then translated into coordinates in the robot control system.A flexible collaborative robot with an intelligent gripper is then used to perform acupuncture manipulations with high precision.In addition,a De Qi sensation detection system is proposed to evaluate the effect of acupuncture.To verify the stability of the designed acupuncture robot,acupoints'coordinates localized by the acupuncture robot are compared with the Gold Standard labeled by a professional acupuncturist using significant level tests.RESULTS:Through repeated experiments for eight acupoints,the acupuncture robot achieved a positioning error within 3.3 mm,which is within the allowable range of needle extraction and acupoint insertion.During needle insertion,the robot arm followed the prescribed trajectory with a mean deviation distance of 0.02 mm and a deviation angle of less than 0.15°.The results of the lifting thrusting operation in the Xingzhen process show that the mean acupuncture depth error of the designed acupuncture robot is approximately 2 mm,which is within the recommended depth range for the Xingzhen operation.In addition,the average detection accuracy of the De Qi keywords is 94.52%,which meets the requirements of acupuncture effect testing for different dialects.CONCLUSION:The proposed acupuncture robot system streamlines the acupuncture process,increases efficiency,and reduces practitioner fatigue,while also allowing for the quantification of acupuncture manipulations and evaluation of therapeutic effects.The development of an acupuncture robot system has the potential to revolutionize low back pain treatment and improve patient outcomes.展开更多
基金Research Fund for the Doctoral Program of Higher Education of China(No.20123718120007)
文摘Self-localization is a fundamental requirement for the mobile robot. Robot usually contains a large number of dif- ferent sensors, which provide the information of robot localization, and all the sensor information should be considered for the optimal location. Kalman filter is efficient to realize the information fusion. Used as an efficient sensor fusion algorithm, Kalman filter is an advanced filtering technique which can reduce errors of the position and orientation of the sensors. Kalman filter has been paied much attention to robot automation and solutions to solve uncertainties such as robot localization, navigation, following, tracking, motion control, estimation and prediction. The paper briefly describes Kalman filter theory, and establishes a simple mathematical model based on muti-sensor mobile robot. Meanwhile, Kalman filter is used in robot self-localization by simulations, and it is demonstrated by simulations that Kalman filter is effective.
基金Supported by National Natural Science Foundation of China(60605023,60775048)Specialized Research Fund for the Doctoral Program of Higher Education(20060141006)
文摘An on-the-fly,self-localization system is developed for mobile robot which is operative in a 3D environment with elaborative 3D landmarks.The robot estimates its pose recursively through a MAP estimator that incorporates the information collected from odometry and unidirectional camera.We build the nonlinear models for these two sensors and maintain that the uncertainty manipulation of robot motion and inaccurate sensor measurements should be embedded and tracked throughout our system.We describe the uncertainty framework in a probabilistic geometry viewpoint and use unscented transform to propagate the uncertainty,which undergoes the given nonlinear functions.Considering the processing power of our robot,image features are extracted in the vicinity of corresponding projected features.In addition,data associations are evaluated by statistical distance.Finally,a series of systematic experiments are conducted to prove the reliable and accurate performance of our system.
基金Supported by the National Natural Science Foundation of China (No. 60875055)Natural Science Foundation of Tianjin (No. 07JCY-BJC05400)Program for New Century Excellent Talents in University (No. NCET-06-0210)
文摘A practical serf-localization scheme for mobile robots is proposed and implemented by utilizing sonar sensors. Specifically, the localization problem is solved by employing Monte Carlo method with a new mechanism proposed to calculate the samples＇ weights; the convergence and veracity of the sample set are guaranteed by the designed resampling and scattering process. The proposed serf-localization algorithm is fully implemented on a specific mobile robot system, and experimental results illustrate that it provides an efficient solution for the kidnapped problem.
文摘Self-localization is one of the most important aspects for using mobile robots in unstructured environments. In this paper, the authors introduce a new approach for a self-localization and navigation unit for mobile platforms in extraterrestrial environments, based on the authors＂ successful results in self-localization of forestry machines on earth. The presented approach is developed from a highly modular concept, which allows a simple but efficient adaption to specific applications by just substituting some scenario dependent components. In this paper, the authors will explain the general concept and the terrestrial implementation so far. On this basis, the authors will demonstrate and discuss the necessary adaptions to the general concept in order to handle the different conditions on extraterrestrial surfaces.
基金the National Natural Science Foundation of China[62525301,62127811,62433019]the New Cornerstone Science Foundation through the XPLORER PRIZEthe financial support by the China Postdoctoral Science Foundation[GZB20240797].
文摘Single-cell biomechanics and electrophysiology measuring tools have transformed biological research over the last few decades,which enabling a comprehensive and nuanced understanding of cellular behavior and function.Despite their high-quality information content,these single-cell measuring techniques suffer from laborious manual processing by highly skilled workers and extremely low throughput(tens of cells per day).Recently,numerous researchers have automated the measurement of cell mechanical and electrical signals through robotic localization and control processes.While these efforts have demonstrated promising progress,critical challenges persist,including human dependency,learning complexity,in-situ measurement,and multidimensional signal acquisition.To identify key limitations and highlight emerging opportunities for innovation,in this review,we comprehensively summarize the key steps of robotic technologies in single-cell biomechanics and electrophysiology.We also discussed the prospects and challenges of robotics and automation in biological research.By bridging gaps between engineering,biology,and data science,this work aims to stimulate interdisciplinary research and accelerate the translation of robotic single-cell technologies into practical applications in the life sciences and medical fields.
文摘Underwater pipeline inspection plays a vital role in the proactive maintenance and management of critical marine infrastructure and subaquatic systems.However,the inspection of underwater pipelines presents a challenge due to factors such as light scattering,absorption,restricted visibility,and ambient noise.The advancement of deep learning has introduced powerful techniques for processing large amounts of unstructured and imperfect data collected from underwater environments.This study evaluated the efficacy of the You Only Look Once(YOLO)algorithm,a real-time object detection and localization model based on convolutional neural networks,in identifying and classifying various types of pipeline defects in underwater settings.YOLOv8,the latest evolution in the YOLO family,integrates advanced capabilities,such as anchor-free detection,a cross-stage partial network backbone for efficient feature extraction,and a feature pyramid network+path aggregation network neck for robust multi-scale object detection,which make it particularly well-suited for complex underwater environments.Due to the lack of suitable open-access datasets for underwater pipeline defects,a custom dataset was captured using a remotely operated vehicle in a controlled environment.This application has the following assets available for use.Extensive experimentation demonstrated that YOLOv8 X-Large consistently outperformed other models in terms of pipe defect detection and classification and achieved a strong balance between precision and recall in identifying pipeline cracks,rust,corners,defective welds,flanges,tapes,and holes.This research establishes the baseline performance of YOLOv8 for underwater defect detection and showcases its potential to enhance the reliability and efficiency of pipeline inspection tasks in challenging underwater environments.
基金supported by the National Natural Science Foundation of China(22168008,22378085)the Guangxi Natural Science Foundation(2024GXNSFDA010053)+1 种基金the Technology Development Project of Guangxi Bossco Environmental Protection Technology Co.,Ltd(202100039)Innovation Project of Guangxi Graduate Education(YCBZ2024065).
文摘Strategically coupling nanoparticle hybrids and internal thermosensitive molecular switches establishes an innovative paradigm for constructing micro/nanoscale-reconfigurable robots,facilitating energyefficient CO_(2) management in life-support systems of confined space.Here,a micro/nano-reconfigurable robot is constructed from the CO_(2) molecular hunters,temperature-sensitive molecular switch,solar photothermal conversion,and magnetically-driven function engines.The molecular hunters within the molecular extension state can capture 6.19 mmol g^(−1) of CO_(2) to form carbamic acid and ammonium bicarbonate.Interestingly,the molecular switch of the robot activates a molecular curling state that facilitates CO_(2) release through nano-reconfiguration,which is mediated by the temperature-sensitive curling of Pluronic F127 molecular chains during the photothermal desorption.Nano-reconfiguration of robot alters the amino microenvironment,including increasing surface electrostatic potential of the amino group and decreasing overall lowest unoccupied molecular orbital energy level.This weakened the nucleophilic attack ability of the amino group toward the adsorption product derivatives,thereby inhibiting the side reactions that generate hard-to-decompose urea structures,achieving the lowest regeneration temperature of 55℃ reported to date.The engine of the robot possesses non-contact magnetically-driven micro-reconfiguration capability to achieve efficient photothermal regeneration while avoiding local overheating.Notably,the robot successfully prolonged the survival time of mice in the sealed container by up to 54.61%,effectively addressing the issue of carbon suffocation in confined spaces.This work significantly enhances life-support systems for deep-space exploration,while stimulating innovations in sustainable carbon management technologies for terrestrial extreme environments.
文摘At present,energy consumption is one of the main bottlenecks in autonomous mobile robot development.To address the challenge of high energy consumption in path planning for autonomous mobile robots navigating unknown and complex environments,this paper proposes an Attention-Enhanced Dueling Deep Q-Network(ADDueling DQN),which integrates a multi-head attention mechanism and a prioritized experience replay strategy into a Dueling-DQN reinforcement learning framework.A multi-objective reward function,centered on energy efficiency,is designed to comprehensively consider path length,terrain slope,motion smoothness,and obstacle avoidance,enabling optimal low-energy trajectory generation in 3D space from the source.The incorporation of a multihead attention mechanism allows the model to dynamically focus on energy-critical state features—such as slope gradients and obstacle density—thereby significantly improving its ability to recognize and avoid energy-intensive paths.Additionally,the prioritized experience replay mechanism accelerates learning from key decision-making experiences,suppressing inefficient exploration and guiding the policy toward low-energy solutions more rapidly.The effectiveness of the proposed path planning algorithm is validated through simulation experiments conducted in multiple off-road scenarios.Results demonstrate that AD-Dueling DQN consistently achieves the lowest average energy consumption across all tested environments.Moreover,the proposed method exhibits faster convergence and greater training stability compared to baseline algorithms,highlighting its global optimization capability under energy-aware objectives in complex terrains.This study offers an efficient and scalable intelligent control strategy for the development of energy-conscious autonomous navigation systems.
基金Project(2007AA04Z256) supported by the National High-Tech Research and Development Program of China
文摘A unilateral self-locking mechanism(USM) was proposed to increase the tractive ability of the inchworm in-pipe robots for pipeline inspection.The USM was basically composed of a cam,a torsional spring and an axis.The self-locking and virtual work principles were applied to studying the basic self-locking condition of the USM.In order to make the cooperation between the crutch and telescopic mechanism more harmonical,the unlocking time of the USM was calculated.A set of parameters were selected to build a virtual model and fabricate a prototype.Both the simulation and performance experiments were carried out in a pipe with a nominal inside diameter of 160 mm.The results show that USM enables the robot to move quickly in one way,and in the other way it helps the robot get self-locking with the pipe wall.The traction of the inchworm robot can rise to 1.2 kN,beyond the limitation of friction of 0.497 kN.
基金Funded by Department of Education of Zhejiang Province (No.Y201119307)
文摘The follow-up application of underwater wireless sensor network is influenced by accuracy of self-localization of nodes. The self-localization of nodes is discussed in this paper. First of all, nodes of underwater wireless sensor network are classified into several levels according to the accuracy of position of nodes and the levels are from the first to the fifth in accordance with accuracy of nodes from high to low respectively. Secondly, the level of anchor nodes can be known by those unknown nodes from the information given by the anchor nodes themselves, At the same time the unknown nodes are able to be located in the area controlled by the first level of anchor nodes that are as the aggregation. Then the positioning algorithm is designed correspondingly in accordance with the accuracy level of nodes. Finally, the positioning algorithm is simulated and analyzed. The result shows that the unknown nodes can be located effectively by hierarchical control.
文摘When a group of mobile agents track a target,they can locate themselves and the target in a cooperative manner.To maximize the group advantage,a parallel integration strategy of cooperative target-localization(CTL)and cooperative self-localization(CSL)is designed.Firstly,a global cost function containing the agents’positions and the target’s position is established.Secondly,along with the agents’positions being re-estimated during CTL,the Utransform is employed to propagate the error covariance of the position estimations among the agents.The simulation results show that,the proposal exploits more information for locating the target and the agents than the cases where CTL and CSL run separately,and the global optimal position estimations of the agents and the target are obtained.
文摘This paper develops a new algorithm for sensor network self-localization, which is an enhanced version of the existing Crocco’s method in [11]. The algorithm explores the noisy time of flight (TOF) measurements that quantify the distances between sensor nodes to be localized and sources also at unknown positions. The newly proposed technique first obtains rough estimates of the sensor node and source positions, and then it refines the estimates via a least squares estimator (LSE). The LSE takes into account the geometrical constraints introduced by the desired global coordinate system to improve performance. Simulations show that the new technique offers superior localization accuracy over the original Crocco’s algorithm under small measurement noise condition.
基金supported by National Natural Science Foundation of China(62376219 and 62006194)Foundational Research Project in Specialized Discipline(Grant No.G2024WD0146)Faculty Construction Project(Grant No.24GH0201148).
文摘Large language models(LLMs)have undergone significant expansion and have been increasingly integrated across various domains.Notably,in the realm of robot task planning,LLMs harness their advanced reasoning and language comprehension capabilities to formulate precise and efficient action plans based on natural language instructions.However,for embodied tasks,where robots interact with complex environments,textonly LLMs often face challenges due to a lack of compatibility with robotic visual perception.This study provides a comprehensive overview of the emerging integration of LLMs and multimodal LLMs into various robotic tasks.Additionally,we propose a framework that utilizes multimodal GPT-4V to enhance embodied task planning through the combination of natural language instructions and robot visual perceptions.Our results,based on diverse datasets,indicate that GPT-4V effectively enhances robot performance in embodied tasks.This extensive survey and evaluation of LLMs and multimodal LLMs across a variety of robotic tasks enriches the understanding of LLM-centric embodied intelligence and provides forward-looking insights towards bridging the gap in Human-Robot-Environment interaction.
基金the National Natural Science Foundation of China(Nos.52105421 and 52373050)the Guangdong Provincial Natural Science Foundation,China(No.2022A1515011621)+1 种基金the Science and Technology Projects in Guangzhou,China(Nos.202102080330 and 2024A04J6446)the Fundamental Research Funds for the Central Universities,Sun Yat-sen University(No.22qntd0101).
文摘Wireless millirobots engineered to infiltrate intricate vascular networks within living organisms,particularly within constricted and confined spaces,hold immense promise for the future of medical treatments.However,with their multifaceted and intricate designs,some robots often grapple with motion and functionality issues when confronted with tight spaces characterized by small cross-sectional dimensions.In this study,drawing inspiration from the high aspect ratio and undulating swimming patterns of snakes,a millimeter-scale,snake-like robot was designed and fabricated via a combination of extrusion-based four-dimensional(4D)printing and magnetic-responsive intelligent functional inks.A sophisticated motion control strategy was also developed,which enables the robots to perform various dynamic movements,such as undulating swimming,precise turns,graceful circular motions,and coordinated cluster movements,under diverse magnetic field variations.As a potential application,the snake robot can navigate and release drugs in a model coronary intervention vessel with tortuous channels and fluid filling.The novel design and promising applications of this snake robot are invaluable tools in future medical surgeries and interventions.
基金Modernization of Traditional Chinese Medicine Project of National Key R&D Program of China:The construction of the theoretical system of Traditional Chinese Medicine nonpharmacological therapy based on body surface stimulation(2023YFC3502704)Sichuan Provincial Science and Technology Program Project:Research and Development of Chinese Medicine Intelligent Tongue Diagnosis Equipment for Digestive System Chinese Medicine Advantageous Diseases(2023YFS0327)+2 种基金Research and Development of Chinese Medicine Intelligent Detection System for Intestinal Functions(2024YFFK0044)Research and Application of Chinese Medicine Diagnosis and Treatment Program for Herpes Zoster Treated by Shu Pai Fire Acupuncture(2024YFFK0089)Major Research and Development Project of The China Academy of Chinese Medical Sciences Innovation:Construction and application of the theoretical research mode of Traditional Chinese Medicine diagnosis and treatment of modern diseases(CI2021A00104)。
文摘OBJECTIVE:To propose an automatic acupuncture robot system for performing acupuncture operations.METHODS:The acupuncture robot system consists of three components:automatic acupoint localization,acupuncture manipulations,and De Qi sensation detection.The OptiTrack motion capture system is used to locate acupoints,which are then translated into coordinates in the robot control system.A flexible collaborative robot with an intelligent gripper is then used to perform acupuncture manipulations with high precision.In addition,a De Qi sensation detection system is proposed to evaluate the effect of acupuncture.To verify the stability of the designed acupuncture robot,acupoints'coordinates localized by the acupuncture robot are compared with the Gold Standard labeled by a professional acupuncturist using significant level tests.RESULTS:Through repeated experiments for eight acupoints,the acupuncture robot achieved a positioning error within 3.3 mm,which is within the allowable range of needle extraction and acupoint insertion.During needle insertion,the robot arm followed the prescribed trajectory with a mean deviation distance of 0.02 mm and a deviation angle of less than 0.15°.The results of the lifting thrusting operation in the Xingzhen process show that the mean acupuncture depth error of the designed acupuncture robot is approximately 2 mm,which is within the recommended depth range for the Xingzhen operation.In addition,the average detection accuracy of the De Qi keywords is 94.52%,which meets the requirements of acupuncture effect testing for different dialects.CONCLUSION:The proposed acupuncture robot system streamlines the acupuncture process,increases efficiency,and reduces practitioner fatigue,while also allowing for the quantification of acupuncture manipulations and evaluation of therapeutic effects.The development of an acupuncture robot system has the potential to revolutionize low back pain treatment and improve patient outcomes.
