The research progress of swarm robotics is reviewed in details. The swarm robotics inspired from nature is a combination of swarm intelligence and robotics, which shows a great potential in several aspects. First of a...The research progress of swarm robotics is reviewed in details. The swarm robotics inspired from nature is a combination of swarm intelligence and robotics, which shows a great potential in several aspects. First of all, the cooperation of nature swarm and swarm intelligence are briefly introduced, and the special features of the swarm robotics are summarized compared to a single robot and other multi-individual systems. Then the modeling methods for swarm robotics are described by a list of several widely used swarm robotics entity projects and simulation platforms. Finally, as a main part of this paper, the current research on the swarm robotic algorithms are presented in detail, including cooperative control mechanisms in swarm robotics for flocking, navigating and searching applications.展开更多
ICRA is the IEEE Robotics and Automation Society’s flagship conference and the premier international forum for robotics researchers to present and discuss their work.The conference will include plenary sessions,contr...ICRA is the IEEE Robotics and Automation Society’s flagship conference and the premier international forum for robotics researchers to present and discuss their work.The conference will include plenary sessions,contributed paper sessions,workshops and tutorial sessions,forums,videos,exhibitions,and robot challenges.展开更多
Aiming to solve the steering instability and hysteresis of agricultural robots in the process of movement,a fusion PID control method of particle swarm optimization(PSO)and genetic algorithm(GA)was proposed.The fusion...Aiming to solve the steering instability and hysteresis of agricultural robots in the process of movement,a fusion PID control method of particle swarm optimization(PSO)and genetic algorithm(GA)was proposed.The fusion algorithm took advantage of the fast optimization ability of PSO to optimize the population screening link of GA.The Simulink simulation results showed that the convergence of the fitness function of the fusion algorithm was accelerated,the system response adjustment time was reduced,and the overshoot was almost zero.Then the algorithm was applied to the steering test of agricultural robot in various scenes.After modeling the steering system of agricultural robot,the steering test results in the unloaded suspended state showed that the PID control based on fusion algorithm reduced the rise time,response adjustment time and overshoot of the system,and improved the response speed and stability of the system,compared with the artificial trial and error PID control and the PID control based on GA.The actual road steering test results showed that the PID control response rise time based on the fusion algorithm was the shortest,about 4.43 s.When the target pulse number was set to 100,the actual mean value in the steady-state regulation stage was about 102.9,which was the closest to the target value among the three control methods,and the overshoot was reduced at the same time.The steering test results under various scene states showed that the PID control based on the proposed fusion algorithm had good anti-interference ability,it can adapt to the changes of environment and load and improve the performance of the control system.It was effective in the steering control of agricultural robot.This method can provide a reference for the precise steering control of other robots.展开更多
Traditional sampling-based path planning algorithms,such as the rapidly-exploring random tree star(RRT^(*)),encounter critical limitations in unstructured orchard environments,including low sampling efficiency in narr...Traditional sampling-based path planning algorithms,such as the rapidly-exploring random tree star(RRT^(*)),encounter critical limitations in unstructured orchard environments,including low sampling efficiency in narrow passages,slow convergence,and high computational costs.To address these challenges,this paper proposes a novel hybrid global path planning algorithm integrating Gaussian sampling and quadtree optimization(RRT^(*)-GSQ).This methodology aims to enhance path planning by synergistically combining a Gaussian mixture sampling strategy to improve node generation in critical regions,an adaptive step-size and direction optimization mechanism for enhanced obstacle avoidance,a Quadtree-AABB collision detection framework to lower computational complexity,and a dynamic iteration control strategy for more efficient convergence.In obstacle-free and obstructed scenarios,compared with the conventional RRT^(*),the proposed algorithm reduced the number of node evaluations by 67.57%and 62.72%,and decreased the search time by 79.72%and 78.52%,respectively.In path tracking tests,the proposed algorithm achieved substantial reductions in RMSE of the final path compared to the conventional RRT^(*).Specifically,the lateral RMSE was reduced by 41.5%in obstacle-free environments and 59.3%in obstructed environments,while the longitudinal RMSE was reduced by 57.2%and 58.5%,respectively.Furthermore,the maximum absolute errors in both lateral and longitudinal directions were constrained within 0.75 m.Field validation experiments in an operational orchard confirmed the algorithm's practical effectiveness,showing reductions in the mean tracking error of 47.6%(obstacle-free)and 58.3%(with obstructed),alongside a 5.1%and 7.2%shortening of the path length compared to the baseline method.The proposed algorithm effectively enhances path planning efficiency and navigation accuracy for robots,presenting a superior solution for high-precision autonomous navigation of agricultural robots in orchard environments and holding significant value for engineering applications.展开更多
Wing design is a critical factor in the aerodynamic performance of flapping-wing(FW)robots.Inspired by the natural wing structures of insects,bats,and birds,we explored how bio-mimetic wing vein morphologies,combined ...Wing design is a critical factor in the aerodynamic performance of flapping-wing(FW)robots.Inspired by the natural wing structures of insects,bats,and birds,we explored how bio-mimetic wing vein morphologies,combined with a bio-inspired double wing clap-and-fling mechanism,affect thrust generation.This study focused on increasing vertical force and payload capacity.Through systematic experimentation with various vein configurations and structural designs,we developed innovative wings optimized for thrust production.Comprehensive tests were conducted to measure aerodynamic forces,power consumption,and wing kinematics across a range of flapping frequencies.Additionally,wings with different aspect ratios,a key factor in wing design,were fabricated and extensively evaluated.The study also examined the role of bio-inspired vein layouts on wing flexibility,a critical component in improving flight efficiency.Our findings demonstrate that the newly developed wing design led to a 20%increase in thrust,achieving up to 30 g-force(gf).This research sheds light on the clap-and-fling effect and establishes a promising framework for bio-inspired wing design,offering significant improvements in both performance and payload capacity for FW robots.展开更多
Embodied intelligent systems integrate perception,control,and decision-making within physical agents,and have become a cornerstone of modern aerospace,autonomous driving,and cooperative robotic applications.When opera...Embodied intelligent systems integrate perception,control,and decision-making within physical agents,and have become a cornerstone of modern aerospace,autonomous driving,and cooperative robotic applications.When operating in uncertain and dynamic environments,such systems must address challenges arising from incomplete sensing,unpredictable maneuvers,communication constraints,disturbances,and evolving network structures.展开更多
[Objective]There are several critical challenges in automated safflower harvesting,particularly the inefficiencies in path planning,suboptimal route quality,and limited decision-making capability under dynamic and com...[Objective]There are several critical challenges in automated safflower harvesting,particularly the inefficiencies in path planning,suboptimal route quality,and limited decision-making capability under dynamic and complex environments.To solve these issues,the problem was formulated as a three-dimensional traveling salesman problem and an enhanced reinforcement learning model named actor-critic reinforcement learning pointer network(AC-RL-PtrNet)was proposed,specifically designed for deployment on intelligent safflower picking robots in agricultural settings.[Methods]First,to address the inherent limitations of conventional attention mechanisms in dynamic environments with complex spatial structures,an enhanced attention module was proposed based on the dynamic exponential moving average framework.By combining multi-head attention,spatial distance encoding,and adaptive exponential smoothing,the improved design allowed the model to better capture long-range dependencies and spatial context among safflowers.Meanwhile,to minimize computational cost while preserving inference quality,a structured pruning approach was adopted,which selectively removed redundant connections in the long short-term memory gates and fully connected layers.In parallel,the critic network was redesigned to improve learning stability and accuracy.This was achieved through the inclusion of batch normalization,residual feature aggregation,and a multi-layer value estimation head,all of which contributed to a tighter actorcritic synergy during policy training.[Results and Discussions]To quantitatively assess the impact of each component,ablation experiments were conducted across various configurations.The results confirmed that each module contributed distinct benefits,while their combination yielded the highest improvements in both planning precision and inference efficiency.This coordinated actor-critic design effectively enhanced both trajectory quality and decision stability,which were critical in sequential robotic picking tasks.Experimental results also demonstrated that,compared with traditional swarm intelligence algorithms particle swarm optimization(PSO),ant colony optimization(ACO),and non-dominated sorting genetic algorithm,the proposed AC-RL-PtrNet model achieved a planning time improvement ranging from-2.63%to 61.87%on the 25-target dataset and from 22.93%to 59.1%on the 31-target dataset.Meanwhile,the optimized paths were significantly shortened across different planning instances,indicating robust generalization capability under varied problem scales.Furthermore,field experiments provided concrete validation of the model's practical applicability.When deployed on a mobile picking robot in real safflower fields,the AC-RL-PtrNet achieved a 9.56%reduction in path length and 5.43%time saved for a 25-target picking task,and a 20.17%path reduction and 29.70%time saving for a 31-target scenario involving a different safflower variety.Overall,these results all indicated that the proposed method exhibited significant advantages in enhancing path planning efficiency and optimizing path quality.[Conclusions]This study offers a practical solution for achieving efficient and robust automatic picking by safflower picking robots and provides new insights into solving 3D combinatorial optimization problems.展开更多
The advent of parametric design has resulted in a marked increase in the complexity of building.Unfortunately,traditional construction methods make it difficult to meet the needs.Therefore,construction robots have bec...The advent of parametric design has resulted in a marked increase in the complexity of building.Unfortunately,traditional construction methods make it difficult to meet the needs.Therefore,construction robots have become a pivotal production tool in this context.Since the arm span of a single robot usually does not exceed 3 meters,it is not competent for producing large-scale building components.Accordingly,the extension of the robot,s working range is often achieved by external axes.Nevertheless,the coupling control of external axes and robots and their kinematic solution have become key challenges.The primary technical difficulties include customized construction robots,automatic solutions for external axes,fixed axis joints,and specific motion mode control.This paper proposes solutions to these difficulties,introduces the relevant basic concepts and algorithms in detail,and encapsulates these robotics principles and algorithm processes into the Grasshopper plug-in commonly used by architects to form the FURobot software platform.This platform effectively solves the above problems,lowers the threshold for architects,and improves production efficiency.The effectiveness of the algorithm and software in this paper is verified through simulation experiments.展开更多
Reusable and flexible capturing of space debris is highly required in future aerospace technologies.A tendon-actuated flexible robotic arm is therefore proposed for capturing floating targets in this paper.Firstly,an ...Reusable and flexible capturing of space debris is highly required in future aerospace technologies.A tendon-actuated flexible robotic arm is therefore proposed for capturing floating targets in this paper.Firstly,an accurate dynamic model of the flexible robotic arm is established by using the absolute nodal coordinate formulation(ANCF)in the framework of the arbitrary Lagrangian-Eulerian(ALE)description and the natural coordinate formulation(NCF).The contact and self-contact dynamics of the flexible robotic arm when bending and grasping an object are considered via a fast contact detection approach.Then,the dynamic simulations of the flexible robotic arm for capturing floating targets are carried out to study the influence of the position,size,and mass of the target object on the grasping performance.Finally,a principle prototype of the tendon-actuated flexible robotic arm is manufactured to validate the dynamic model.The corresponding grasping experiments for objects of various shapes are also conducted to illustrate the excellent performance of the flexible robotic arm.展开更多
基金Sponsored by National Natural Science Foundation of China under Grant( 61170057,60875080)
文摘The research progress of swarm robotics is reviewed in details. The swarm robotics inspired from nature is a combination of swarm intelligence and robotics, which shows a great potential in several aspects. First of all, the cooperation of nature swarm and swarm intelligence are briefly introduced, and the special features of the swarm robotics are summarized compared to a single robot and other multi-individual systems. Then the modeling methods for swarm robotics are described by a list of several widely used swarm robotics entity projects and simulation platforms. Finally, as a main part of this paper, the current research on the swarm robotic algorithms are presented in detail, including cooperative control mechanisms in swarm robotics for flocking, navigating and searching applications.
文摘ICRA is the IEEE Robotics and Automation Society’s flagship conference and the premier international forum for robotics researchers to present and discuss their work.The conference will include plenary sessions,contributed paper sessions,workshops and tutorial sessions,forums,videos,exhibitions,and robot challenges.
文摘Aiming to solve the steering instability and hysteresis of agricultural robots in the process of movement,a fusion PID control method of particle swarm optimization(PSO)and genetic algorithm(GA)was proposed.The fusion algorithm took advantage of the fast optimization ability of PSO to optimize the population screening link of GA.The Simulink simulation results showed that the convergence of the fitness function of the fusion algorithm was accelerated,the system response adjustment time was reduced,and the overshoot was almost zero.Then the algorithm was applied to the steering test of agricultural robot in various scenes.After modeling the steering system of agricultural robot,the steering test results in the unloaded suspended state showed that the PID control based on fusion algorithm reduced the rise time,response adjustment time and overshoot of the system,and improved the response speed and stability of the system,compared with the artificial trial and error PID control and the PID control based on GA.The actual road steering test results showed that the PID control response rise time based on the fusion algorithm was the shortest,about 4.43 s.When the target pulse number was set to 100,the actual mean value in the steady-state regulation stage was about 102.9,which was the closest to the target value among the three control methods,and the overshoot was reduced at the same time.The steering test results under various scene states showed that the PID control based on the proposed fusion algorithm had good anti-interference ability,it can adapt to the changes of environment and load and improve the performance of the control system.It was effective in the steering control of agricultural robot.This method can provide a reference for the precise steering control of other robots.
基金National Natural Science Foundation of China(32301712)Natural Science Foundation of Jiangsu Province(BK20230548,BK20250876)+2 种基金Project of Faculty of Agricultural Equipment of Jiangsu University(NGXB20240203)A Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD-2023-87)Open Funding Project of the Key Laboratory of Modern Agricultural Equipment and Technology(Jiangsu University),Ministry of Education(MAET202101)。
文摘Traditional sampling-based path planning algorithms,such as the rapidly-exploring random tree star(RRT^(*)),encounter critical limitations in unstructured orchard environments,including low sampling efficiency in narrow passages,slow convergence,and high computational costs.To address these challenges,this paper proposes a novel hybrid global path planning algorithm integrating Gaussian sampling and quadtree optimization(RRT^(*)-GSQ).This methodology aims to enhance path planning by synergistically combining a Gaussian mixture sampling strategy to improve node generation in critical regions,an adaptive step-size and direction optimization mechanism for enhanced obstacle avoidance,a Quadtree-AABB collision detection framework to lower computational complexity,and a dynamic iteration control strategy for more efficient convergence.In obstacle-free and obstructed scenarios,compared with the conventional RRT^(*),the proposed algorithm reduced the number of node evaluations by 67.57%and 62.72%,and decreased the search time by 79.72%and 78.52%,respectively.In path tracking tests,the proposed algorithm achieved substantial reductions in RMSE of the final path compared to the conventional RRT^(*).Specifically,the lateral RMSE was reduced by 41.5%in obstacle-free environments and 59.3%in obstructed environments,while the longitudinal RMSE was reduced by 57.2%and 58.5%,respectively.Furthermore,the maximum absolute errors in both lateral and longitudinal directions were constrained within 0.75 m.Field validation experiments in an operational orchard confirmed the algorithm's practical effectiveness,showing reductions in the mean tracking error of 47.6%(obstacle-free)and 58.3%(with obstructed),alongside a 5.1%and 7.2%shortening of the path length compared to the baseline method.The proposed algorithm effectively enhances path planning efficiency and navigation accuracy for robots,presenting a superior solution for high-precision autonomous navigation of agricultural robots in orchard environments and holding significant value for engineering applications.
基金Nguyen Tat Thanh University,Ho Chi Minh City,Vietnam for supporting this study。
文摘Wing design is a critical factor in the aerodynamic performance of flapping-wing(FW)robots.Inspired by the natural wing structures of insects,bats,and birds,we explored how bio-mimetic wing vein morphologies,combined with a bio-inspired double wing clap-and-fling mechanism,affect thrust generation.This study focused on increasing vertical force and payload capacity.Through systematic experimentation with various vein configurations and structural designs,we developed innovative wings optimized for thrust production.Comprehensive tests were conducted to measure aerodynamic forces,power consumption,and wing kinematics across a range of flapping frequencies.Additionally,wings with different aspect ratios,a key factor in wing design,were fabricated and extensively evaluated.The study also examined the role of bio-inspired vein layouts on wing flexibility,a critical component in improving flight efficiency.Our findings demonstrate that the newly developed wing design led to a 20%increase in thrust,achieving up to 30 g-force(gf).This research sheds light on the clap-and-fling effect and establishes a promising framework for bio-inspired wing design,offering significant improvements in both performance and payload capacity for FW robots.
文摘Embodied intelligent systems integrate perception,control,and decision-making within physical agents,and have become a cornerstone of modern aerospace,autonomous driving,and cooperative robotic applications.When operating in uncertain and dynamic environments,such systems must address challenges arising from incomplete sensing,unpredictable maneuvers,communication constraints,disturbances,and evolving network structures.
基金Natural Science Foundation of Xinjiang Uygur Autonomous Region,China Under Grant(2023D01C190)National Science and Technology Major Project(2022ZD0115801)。
文摘[Objective]There are several critical challenges in automated safflower harvesting,particularly the inefficiencies in path planning,suboptimal route quality,and limited decision-making capability under dynamic and complex environments.To solve these issues,the problem was formulated as a three-dimensional traveling salesman problem and an enhanced reinforcement learning model named actor-critic reinforcement learning pointer network(AC-RL-PtrNet)was proposed,specifically designed for deployment on intelligent safflower picking robots in agricultural settings.[Methods]First,to address the inherent limitations of conventional attention mechanisms in dynamic environments with complex spatial structures,an enhanced attention module was proposed based on the dynamic exponential moving average framework.By combining multi-head attention,spatial distance encoding,and adaptive exponential smoothing,the improved design allowed the model to better capture long-range dependencies and spatial context among safflowers.Meanwhile,to minimize computational cost while preserving inference quality,a structured pruning approach was adopted,which selectively removed redundant connections in the long short-term memory gates and fully connected layers.In parallel,the critic network was redesigned to improve learning stability and accuracy.This was achieved through the inclusion of batch normalization,residual feature aggregation,and a multi-layer value estimation head,all of which contributed to a tighter actorcritic synergy during policy training.[Results and Discussions]To quantitatively assess the impact of each component,ablation experiments were conducted across various configurations.The results confirmed that each module contributed distinct benefits,while their combination yielded the highest improvements in both planning precision and inference efficiency.This coordinated actor-critic design effectively enhanced both trajectory quality and decision stability,which were critical in sequential robotic picking tasks.Experimental results also demonstrated that,compared with traditional swarm intelligence algorithms particle swarm optimization(PSO),ant colony optimization(ACO),and non-dominated sorting genetic algorithm,the proposed AC-RL-PtrNet model achieved a planning time improvement ranging from-2.63%to 61.87%on the 25-target dataset and from 22.93%to 59.1%on the 31-target dataset.Meanwhile,the optimized paths were significantly shortened across different planning instances,indicating robust generalization capability under varied problem scales.Furthermore,field experiments provided concrete validation of the model's practical applicability.When deployed on a mobile picking robot in real safflower fields,the AC-RL-PtrNet achieved a 9.56%reduction in path length and 5.43%time saved for a 25-target picking task,and a 20.17%path reduction and 29.70%time saving for a 31-target scenario involving a different safflower variety.Overall,these results all indicated that the proposed method exhibited significant advantages in enhancing path planning efficiency and optimizing path quality.[Conclusions]This study offers a practical solution for achieving efficient and robust automatic picking by safflower picking robots and provides new insights into solving 3D combinatorial optimization problems.
基金National Key R&D Program of China(Nos.2023YFC3806900,2022YFE0141400)。
文摘The advent of parametric design has resulted in a marked increase in the complexity of building.Unfortunately,traditional construction methods make it difficult to meet the needs.Therefore,construction robots have become a pivotal production tool in this context.Since the arm span of a single robot usually does not exceed 3 meters,it is not competent for producing large-scale building components.Accordingly,the extension of the robot,s working range is often achieved by external axes.Nevertheless,the coupling control of external axes and robots and their kinematic solution have become key challenges.The primary technical difficulties include customized construction robots,automatic solutions for external axes,fixed axis joints,and specific motion mode control.This paper proposes solutions to these difficulties,introduces the relevant basic concepts and algorithms in detail,and encapsulates these robotics principles and algorithm processes into the Grasshopper plug-in commonly used by architects to form the FURobot software platform.This platform effectively solves the above problems,lowers the threshold for architects,and improves production efficiency.The effectiveness of the algorithm and software in this paper is verified through simulation experiments.
基金funded by the"14th Five-Year Plan"Civil Aerospace Pre-research Project of China(Grant No.D010301).
文摘Reusable and flexible capturing of space debris is highly required in future aerospace technologies.A tendon-actuated flexible robotic arm is therefore proposed for capturing floating targets in this paper.Firstly,an accurate dynamic model of the flexible robotic arm is established by using the absolute nodal coordinate formulation(ANCF)in the framework of the arbitrary Lagrangian-Eulerian(ALE)description and the natural coordinate formulation(NCF).The contact and self-contact dynamics of the flexible robotic arm when bending and grasping an object are considered via a fast contact detection approach.Then,the dynamic simulations of the flexible robotic arm for capturing floating targets are carried out to study the influence of the position,size,and mass of the target object on the grasping performance.Finally,a principle prototype of the tendon-actuated flexible robotic arm is manufactured to validate the dynamic model.The corresponding grasping experiments for objects of various shapes are also conducted to illustrate the excellent performance of the flexible robotic arm.
