This survey presents a comprehensive review of vari-ous methods and algorithms related to passing-through control of multi-robot systems in cluttered environments.Numerous studies have investigated this area,and we id...This survey presents a comprehensive review of vari-ous methods and algorithms related to passing-through control of multi-robot systems in cluttered environments.Numerous studies have investigated this area,and we identify several avenues for enhancing existing methods.This survey describes some models of robots and commonly considered control objec-tives,followed by an in-depth analysis of four types of algo-rithms that can be employed for passing-through control:leader-follower formation control,multi-robot trajectory planning,con-trol-based methods,and virtual tube planning and control.Fur-thermore,we conduct a comparative analysis of these tech-niques and provide some subjective and general evaluations.展开更多
The rapid advancement of Industry 4.0 has revolutionized manufacturing,shifting production from centralized control to decentralized,intelligent systems.Smart factories are now expected to achieve high adaptability an...The rapid advancement of Industry 4.0 has revolutionized manufacturing,shifting production from centralized control to decentralized,intelligent systems.Smart factories are now expected to achieve high adaptability and resource efficiency,particularly in mass customization scenarios where production schedules must accommodate dynamic and personalized demands.To address the challenges of dynamic task allocation,uncertainty,and realtime decision-making,this paper proposes Pathfinder,a deep reinforcement learning-based scheduling framework.Pathfinder models scheduling data through three key matrices:execution time(the time required for a job to complete),completion time(the actual time at which a job is finished),and efficiency(the performance of executing a single job).By leveraging neural networks,Pathfinder extracts essential features from these matrices,enabling intelligent decision-making in dynamic production environments.Unlike traditional approaches with fixed scheduling rules,Pathfinder dynamically selects from ten diverse scheduling rules,optimizing decisions based on real-time environmental conditions.To further enhance scheduling efficiency,a specialized reward function is designed to support dynamic task allocation and real-time adjustments.This function helps Pathfinder continuously refine its scheduling strategy,improving machine utilization and minimizing job completion times.Through reinforcement learning,Pathfinder adapts to evolving production demands,ensuring robust performance in real-world applications.Experimental results demonstrate that Pathfinder outperforms traditional scheduling approaches,offering improved coordination and efficiency in smart factories.By integrating deep reinforcement learning,adaptable scheduling strategies,and an innovative reward function,Pathfinder provides an effective solution to the growing challenges of multi-robot job scheduling in mass customization environments.展开更多
For the process of multi-robot collaboration to lift the same lifted object by flexible cables,the existing collision detection algorithm of cables between the environmental obstacles has the problem of misjudgment an...For the process of multi-robot collaboration to lift the same lifted object by flexible cables,the existing collision detection algorithm of cables between the environmental obstacles has the problem of misjudgment and omission.In this work,the collision detection of cable vector was studied,and the purpose of collision detection was realized by algorithm.Considering the characteristics of cables themselves,based on oriented bounding box theory,the cable optimization model and environmental obstacle model were established,and a new basic geometric collision detection model was proposed.Then a fast cable vector collision detection algorithm and an optimization principle were proposed.Finally,the rationality of the cable collision detection model and the effectiveness of the proposed algorithm were verified by simulation.Simulation results show that the proposed method can meet the requirements of the fast detection and the accuracy in complex virtual environment.The results lay a foundation for obstacle avoidance motion planning of system.展开更多
The overall performance of multi-robot collaborative systems is significantly affected by the multi-robot task allocation.To improve the effectiveness,robustness,and safety of multi-robot collaborative systems,a multi...The overall performance of multi-robot collaborative systems is significantly affected by the multi-robot task allocation.To improve the effectiveness,robustness,and safety of multi-robot collaborative systems,a multimodal multi-objective evolutionary algorithm based on deep reinforcement learning is proposed in this paper.The improved multimodal multi-objective evolutionary algorithm is used to solve multi-robot task allo-cation problems.Moreover,a deep reinforcement learning strategy is used in the last generation to provide a high-quality path for each assigned robot via an end-to-end manner.Comparisons with three popular multimodal multi-objective evolutionary algorithms on three different scenarios of multi-robot task allocation problems are carried out to verify the performance of the proposed algorithm.The experimental test results show that the proposed algorithm can generate sufficient equivalent schemes to improve the availability and robustness of multi-robot collaborative systems in uncertain environments,and also produce the best scheme to improve the overall task execution efficiency of multi-robot collaborative systems.展开更多
This paper investigates the problem of decentralized multi-robot cooperative localization.This problem involves collaboratively estimating the poses of a group of robots with respect to a common reference coordinate s...This paper investigates the problem of decentralized multi-robot cooperative localization.This problem involves collaboratively estimating the poses of a group of robots with respect to a common reference coordinate system using robot-to-robot relative measurements and intermittent absolute measurements in a distributed manner.To address this problem,we present a decentralized fusion method that enables batch updating to handle relative measurements from multiple robots simultaneously.This method can improve both the accuracy and computational efficiency of cooperative localization.To reduce communication costs and reliance on connectivity,we do not maintain the inter-robot state correlations.Instead,we adopt a covariance intersection(CI)technique to design an upper bound that replaces unknown joint correlations.We propose an optimization method to determine a tight upper bound for the correlations in the joint update.The consistency and convergence of our proposed algorithm is theoretically analyzed.Furthermore,we conduct Monte Carlo numerical simulations and real-world experiments to demonstrate that the proposed method outperforms existing approaches in terms of both accuracy and consistency.展开更多
This work proposes an online collaborative hunting strategy for multi-robot systems based on obstacle-avoiding Voronoi cells in a complex dynamic environment. This involves firstly designing the construction method us...This work proposes an online collaborative hunting strategy for multi-robot systems based on obstacle-avoiding Voronoi cells in a complex dynamic environment. This involves firstly designing the construction method using a support vector machine(SVM) based on the definition of buffered Voronoi cells(BVCs). Based on the safe collision-free region of the robots, the boundary weights between the robots and the obstacles are dynamically updated such that the robots are tangent to the buffered Voronoi safety areas without intersecting with the obstacles. Then, the robots are controlled to move within their own buffered Voronoi safety area to achieve collision-avoidance with other robots and obstacles. The next step involves proposing a hunting method that optimizes collaboration between the pursuers and evaders. Some hunting points are generated and distributed evenly around a circle. Next, the pursuers are assigned to match the optimal points based on the Hungarian algorithm.Then, a hunting controller is designed to improve the containment capability and minimize containment time based on collision risk. Finally, simulation results have demonstrated that the proposed cooperative hunting method is more competitive in terms of time and travel distance.展开更多
The multi-robot coordinated lifting system is an unconstrained system with a rigid and flexible coupling.The deformation of the flexible rope causes errors in the movement trajectory of the lifting system.Based on the...The multi-robot coordinated lifting system is an unconstrained system with a rigid and flexible coupling.The deformation of the flexible rope causes errors in the movement trajectory of the lifting system.Based on the kinematic and dynamic analysis of the lifting system,the elastic catenary mod-el considering the elasticity and mass of the flexible rope is established,and the effect of the deform-ation of the flexible rope on the position and posture of the suspended object is analyzed.According to the deformation of flexible rope,a real-time trajectory compensation method is proposed based on the compensation principle of position and posture.Under the lifting task of the low-speed move-ment,this is compared with that of the system which neglects the deformation of the flexible rope.The trajectoy of the lifting system considering the deformation of flexible rope.The results show that the mass and elasticity of the flexible rope can not be neglected.Meanwhile,the proposed trajectory compensation method can improve the movement accuracy of the lifting system,which verifies the ef-fectiveness of this compensation method.The research results provide the basis for trajectory plan-ning and coordinated control of the lifting system。展开更多
This paper described a new method to plan out welding paths for multiple robots in virtual manufacturing environment. We first distribute welding tasks and priority for multi robots, and then apply corresponding behav...This paper described a new method to plan out welding paths for multiple robots in virtual manufacturing environment. We first distribute welding tasks and priority for multi robots, and then apply corresponding behavior rules to help to plan out welding paths for robots collision free, which is a base fixed problem. Finally, we testify the algorithm to be practical in virtual environment, and output robot programs to direct production process. This new way will help us to find a new development method for multiple robots path planning.展开更多
As a cross-cutting field between ocean development and multi-robot system(MRS),the underwater multi-robot system(UMRS)has gained increasing attention from researchers and engineers in recent decades.In this paper,we p...As a cross-cutting field between ocean development and multi-robot system(MRS),the underwater multi-robot system(UMRS)has gained increasing attention from researchers and engineers in recent decades.In this paper,we present a comprehensive survey of cooperation issues,one of the key components of UMRS,from the perspective of the emergence of new functions.More specifically,we categorize the cooperation in terms of task-space,motion-space,measurement-space,as well as their combination.Further,we analyze the architecture of UMRS from three aspects,i.e.,the performance of the individual underwater robot,the new functions of underwater robots,and the technical approaches of MRS.To conclude,we have discussed related promising directions for future research.This survey provides valuable insight into the reasonable utilization of UMRS to attain diverse underwater tasks in complex ocean application scenarios.展开更多
Multi-robot systems can be applied to patrol a concerned environment for security purposes.According to different goals,this work reviews the existing researches in a multi-robot patrolling field from the perspectives...Multi-robot systems can be applied to patrol a concerned environment for security purposes.According to different goals,this work reviews the existing researches in a multi-robot patrolling field from the perspectives of regular and adversarial patrolling.Regular patrolling requires robots to visit important locations as frequently as possible and a series of deterministic strategies are proposed,while adversarial one focuses on unpredictable robots’moving patterns to maximize adversary detection probability.Under each category,a systematic survey is done including problem statements and modeling,patrolling objectives and evaluation criteria,and representative patrolling strategies and approaches.Existing problems and open questions are presented accordingly.展开更多
Nowadays, robots generally have a variety of capabilities, which often form a coalition replacing human to work in dangerous environment, such as rescue, exploration, etc. In these operating conditions, the energy sup...Nowadays, robots generally have a variety of capabilities, which often form a coalition replacing human to work in dangerous environment, such as rescue, exploration, etc. In these operating conditions, the energy supply of robots usually cannot be guaranteed. If the energy resources of some robots are consumed too fast, the number of the future tasks of the coalition will be affected. This paper will develop a novel task allocation method based on Gini coefficient to make full use of limited energy resources of multi-robot system to maximize the number of tasks. At the same time, considering resources consumption,we incorporate the market-based allocation mechanism into our Gini coefficient-based method and propose a hybrid method,which can flexibly optimize the task completion number and the resource consumption according to the application contexts.Experiments show that the multi-robot system with limited energy resources can accomplish more tasks by the proposed Gini coefficient-based method, and the hybrid method can be dynamically adaptive to changes of the work environment and realize the dual optimization goals.展开更多
This paper proposes a new methodology to optimize trajectory of the path for multi-robots using improved gravitational search algorithm(IGSA) in clutter environment. Classical GSA has been improved in this paper based...This paper proposes a new methodology to optimize trajectory of the path for multi-robots using improved gravitational search algorithm(IGSA) in clutter environment. Classical GSA has been improved in this paper based on the communication and memory characteristics of particle swarm optimization(PSO). IGSA technique is incorporated into the multi-robot system in a dynamic framework, which will provide robust performance, self-deterministic cooperation, and coping with an inhospitable environment. The robots in the team make independent decisions, coordinate, and cooperate with each other to accomplish a common goal using the developed IGSA. A path planning scheme has been developed using IGSA to optimally obtain the succeeding positions of the robots from the existing position in the proposed environment. Finally, the analytical and experimental results of the multi-robot path planning were compared with those obtained by IGSA, GSA and differential evolution(DE) in a similar environment. The simulation and the Khepera environment result show outperforms of IGSA as compared to GSA and DE with respect to the average total trajectory path deviation, average uncovered trajectory target distance and energy optimization in terms of rotation.展开更多
A common assumption of coverage path planning research is a static environment.Such environments require only a single visit to each area to achieve coverage.However,some real-world environments are characterised by t...A common assumption of coverage path planning research is a static environment.Such environments require only a single visit to each area to achieve coverage.However,some real-world environments are characterised by the presence of unexpected,dynamic obstacles.They require areas to be revisited periodically to maintain an accurate coverage map,as well as reactive obstacle avoidance.This paper proposes a novel swarmbased control algorithm for multi-robot exploration and repeated coverage in environments with unknown,dynamic obstacles.The algorithm combines two elements:frontier-led swarming for driving exploration by a group of robots,and pheromone-based stigmergy for controlling repeated coverage while avoiding obstacles.We tested the performance of our approach on heterogeneous and homogeneous groups of mobile robots in different environments.We measure both repeated coverage performance and obstacle avoidance ability.Through a series of comparison experiments,we demonstrate that our proposed strategy has superior performance to recently presented multi-robot repeated coverage methodologies.展开更多
The problem of allocating a number of exploration tasks to a team of mobile robots in dynamic environments was studied. The team mission is to visit several distributed targets. The path cost of target is proportional...The problem of allocating a number of exploration tasks to a team of mobile robots in dynamic environments was studied. The team mission is to visit several distributed targets. The path cost of target is proportional to the distance that a robot has to move to visit the target. The team objective is to minimize the average path cost of target over all targets. Finding an optimal allocation is strongly NP-hard. The proposed algorithm can produce a near-optimal solution to it. The allocation can be cast in terms of a multi-round single-item auction by which robots bid on targets. In each auction round, one target is assigned to a robot that produces the lowest path cost of the target. The allocated targets form a forest where each tree corresponds a robot’s exploring targets set. Each robot constructs an exploring path through depth-first search in its target tree. The time complexity of the proposed algorithm is polynomial. Simulation experiments show that the allocating method is valid.展开更多
The robotics industry has seen rapid development in recent years due to the Corona Virus Disease 2019.With the development of sensors and smart devices,factories and enterprises have accumulated a large amount of data...The robotics industry has seen rapid development in recent years due to the Corona Virus Disease 2019.With the development of sensors and smart devices,factories and enterprises have accumulated a large amount of data in their daily production,which creates extremely favorable conditions for robots to perform machine learning.However,in recent years,people’s awareness of data privacy has been increasing,leading to the inability to circulate data between different enterprises,resulting in the emergence of data silos.The emergence of federated learning provides a feasible solution to this problem,and the combination of federated learning and multi-robot systems can break down data silos and improve the overall performance of robots.However,as scholars have studied more deeply,they found that federated learning has very limited privacy protection.Therefore,how to protect data privacy from infringement remains an important issue.In this paper,we first give a brief introduction to the current development of multi-robot and federated learning;second,we review three aspects of privacy protection methods commonly used,privacy protection methods for multi-robot,and Other Problems Faced by Multi-robot Systems,focusing on method comparisons and challenges;and finally draw conclusions and predict possible future research directions.展开更多
data is gathered to describe the relative location and relative motion state of the robots, which in turn forms the beginning stage of the fuzzy rule. The artificial immune algorithm optimizes and generates the rule d...data is gathered to describe the relative location and relative motion state of the robots, which in turn forms the beginning stage of the fuzzy rule. The artificial immune algorithm optimizes and generates the rule data base and adaptive design considers factors in measuring the hunting efficiency. The optimized rules are applied to the hunting task and the results show that the algorithm can effectively actualize hunting of multiple mobile robots.展开更多
An improved genetic algorithm is proposed to solve the problem of bad real-time performance or inability to get a global optimal/better solution when applying single-item auction （SIA） method or combinatorial auctio...An improved genetic algorithm is proposed to solve the problem of bad real-time performance or inability to get a global optimal/better solution when applying single-item auction （SIA） method or combinatorial auction method to multi-robot task allocation. The genetic algorithm based combinatorial auction （GACA） method which combines the basic-genetic algorithm with a new concept of ringed chromosome is used to solve the winner determination problem （WDP） of combinatorial auction. The simulation experiments are conducted in OpenSim, a multi-robot simulator. The results show that GACA can get a satisfying solution in a reasonable shot time, and compared with SIA or parthenogenesis algorithm combinatorial auction （PGACA） method, it is the simplest and has higher search efficiency, also, GACA can get a global better/optimal solution and satisfy the high real-time requirement of multi-robot task allocation.展开更多
This paper provides theoretical foundation for the problem of localization in multi-robot formations. Sufficient and necessary conditions for completely localizing a formation of mobile robots/vehicles in SE(2) based ...This paper provides theoretical foundation for the problem of localization in multi-robot formations. Sufficient and necessary conditions for completely localizing a formation of mobile robots/vehicles in SE(2) based on distributed sensor networks and graph rigidity are proposed. A method for estimating the quality of localizations via a linearized weighted least-squares algorithm is presented, which considers incomplete and noisy sensory information. The approach in this paper had been implemented in a multi-robot system of five car-like robots equipped with omni-directional cameras and IEEE 802.11b wireless network.展开更多
文摘This survey presents a comprehensive review of vari-ous methods and algorithms related to passing-through control of multi-robot systems in cluttered environments.Numerous studies have investigated this area,and we identify several avenues for enhancing existing methods.This survey describes some models of robots and commonly considered control objec-tives,followed by an in-depth analysis of four types of algo-rithms that can be employed for passing-through control:leader-follower formation control,multi-robot trajectory planning,con-trol-based methods,and virtual tube planning and control.Fur-thermore,we conduct a comparative analysis of these tech-niques and provide some subjective and general evaluations.
基金supported by National Natural Science Foundation of China under Grant No.62372110Fujian Provincial Natural Science of Foundation under Grants 2023J02008,2024H0009.
文摘The rapid advancement of Industry 4.0 has revolutionized manufacturing,shifting production from centralized control to decentralized,intelligent systems.Smart factories are now expected to achieve high adaptability and resource efficiency,particularly in mass customization scenarios where production schedules must accommodate dynamic and personalized demands.To address the challenges of dynamic task allocation,uncertainty,and realtime decision-making,this paper proposes Pathfinder,a deep reinforcement learning-based scheduling framework.Pathfinder models scheduling data through three key matrices:execution time(the time required for a job to complete),completion time(the actual time at which a job is finished),and efficiency(the performance of executing a single job).By leveraging neural networks,Pathfinder extracts essential features from these matrices,enabling intelligent decision-making in dynamic production environments.Unlike traditional approaches with fixed scheduling rules,Pathfinder dynamically selects from ten diverse scheduling rules,optimizing decisions based on real-time environmental conditions.To further enhance scheduling efficiency,a specialized reward function is designed to support dynamic task allocation and real-time adjustments.This function helps Pathfinder continuously refine its scheduling strategy,improving machine utilization and minimizing job completion times.Through reinforcement learning,Pathfinder adapts to evolving production demands,ensuring robust performance in real-world applications.Experimental results demonstrate that Pathfinder outperforms traditional scheduling approaches,offering improved coordination and efficiency in smart factories.By integrating deep reinforcement learning,adaptable scheduling strategies,and an innovative reward function,Pathfinder provides an effective solution to the growing challenges of multi-robot job scheduling in mass customization environments.
基金the National Natural Science Foundation of China(Nos.51265021 and 51965032)the Gansu Provincial Department of Education:Excellent Graduate Student“Innovation Star"Project(No.2022CXZX-571)。
文摘For the process of multi-robot collaboration to lift the same lifted object by flexible cables,the existing collision detection algorithm of cables between the environmental obstacles has the problem of misjudgment and omission.In this work,the collision detection of cable vector was studied,and the purpose of collision detection was realized by algorithm.Considering the characteristics of cables themselves,based on oriented bounding box theory,the cable optimization model and environmental obstacle model were established,and a new basic geometric collision detection model was proposed.Then a fast cable vector collision detection algorithm and an optimization principle were proposed.Finally,the rationality of the cable collision detection model and the effectiveness of the proposed algorithm were verified by simulation.Simulation results show that the proposed method can meet the requirements of the fast detection and the accuracy in complex virtual environment.The results lay a foundation for obstacle avoidance motion planning of system.
基金the Shanghai Pujiang Program (No.22PJD030),the National Natural Science Foundation of China (Nos.61603244 and 71904116)the National Natural Science Foundation of China-Shandong Joint Fund (No.U2006228)。
文摘The overall performance of multi-robot collaborative systems is significantly affected by the multi-robot task allocation.To improve the effectiveness,robustness,and safety of multi-robot collaborative systems,a multimodal multi-objective evolutionary algorithm based on deep reinforcement learning is proposed in this paper.The improved multimodal multi-objective evolutionary algorithm is used to solve multi-robot task allo-cation problems.Moreover,a deep reinforcement learning strategy is used in the last generation to provide a high-quality path for each assigned robot via an end-to-end manner.Comparisons with three popular multimodal multi-objective evolutionary algorithms on three different scenarios of multi-robot task allocation problems are carried out to verify the performance of the proposed algorithm.The experimental test results show that the proposed algorithm can generate sufficient equivalent schemes to improve the availability and robustness of multi-robot collaborative systems in uncertain environments,and also produce the best scheme to improve the overall task execution efficiency of multi-robot collaborative systems.
文摘This paper investigates the problem of decentralized multi-robot cooperative localization.This problem involves collaboratively estimating the poses of a group of robots with respect to a common reference coordinate system using robot-to-robot relative measurements and intermittent absolute measurements in a distributed manner.To address this problem,we present a decentralized fusion method that enables batch updating to handle relative measurements from multiple robots simultaneously.This method can improve both the accuracy and computational efficiency of cooperative localization.To reduce communication costs and reliance on connectivity,we do not maintain the inter-robot state correlations.Instead,we adopt a covariance intersection(CI)technique to design an upper bound that replaces unknown joint correlations.We propose an optimization method to determine a tight upper bound for the correlations in the joint update.The consistency and convergence of our proposed algorithm is theoretically analyzed.Furthermore,we conduct Monte Carlo numerical simulations and real-world experiments to demonstrate that the proposed method outperforms existing approaches in terms of both accuracy and consistency.
基金supported by the National Natural Science Foundation of China (62273007,61973023)Project of Cultivation for Young Top-motch Talents of Beijing Municipal Institutions (BPHR202203032)。
文摘This work proposes an online collaborative hunting strategy for multi-robot systems based on obstacle-avoiding Voronoi cells in a complex dynamic environment. This involves firstly designing the construction method using a support vector machine(SVM) based on the definition of buffered Voronoi cells(BVCs). Based on the safe collision-free region of the robots, the boundary weights between the robots and the obstacles are dynamically updated such that the robots are tangent to the buffered Voronoi safety areas without intersecting with the obstacles. Then, the robots are controlled to move within their own buffered Voronoi safety area to achieve collision-avoidance with other robots and obstacles. The next step involves proposing a hunting method that optimizes collaboration between the pursuers and evaders. Some hunting points are generated and distributed evenly around a circle. Next, the pursuers are assigned to match the optimal points based on the Hungarian algorithm.Then, a hunting controller is designed to improve the containment capability and minimize containment time based on collision risk. Finally, simulation results have demonstrated that the proposed cooperative hunting method is more competitive in terms of time and travel distance.
基金the National Natural Science Foundation of China(No.51965032)the Natural Science Foundation of Gansu Province of China(No.22JR5RA319)+1 种基金the Science and Technology Foundation of Gansu Province of China(No.21YF5WA060)the Excellent Doctoral Student Foundation of Gansu Province of China(No.23JRRA842).
文摘The multi-robot coordinated lifting system is an unconstrained system with a rigid and flexible coupling.The deformation of the flexible rope causes errors in the movement trajectory of the lifting system.Based on the kinematic and dynamic analysis of the lifting system,the elastic catenary mod-el considering the elasticity and mass of the flexible rope is established,and the effect of the deform-ation of the flexible rope on the position and posture of the suspended object is analyzed.According to the deformation of flexible rope,a real-time trajectory compensation method is proposed based on the compensation principle of position and posture.Under the lifting task of the low-speed move-ment,this is compared with that of the system which neglects the deformation of the flexible rope.The trajectoy of the lifting system considering the deformation of flexible rope.The results show that the mass and elasticity of the flexible rope can not be neglected.Meanwhile,the proposed trajectory compensation method can improve the movement accuracy of the lifting system,which verifies the ef-fectiveness of this compensation method.The research results provide the basis for trajectory plan-ning and coordinated control of the lifting system。
基金Natural Science Foundation of China (No.5 98895 0 5 )
文摘This paper described a new method to plan out welding paths for multiple robots in virtual manufacturing environment. We first distribute welding tasks and priority for multi robots, and then apply corresponding behavior rules to help to plan out welding paths for robots collision free, which is a base fixed problem. Finally, we testify the algorithm to be practical in virtual environment, and output robot programs to direct production process. This new way will help us to find a new development method for multiple robots path planning.
基金This work was supported in part by the National Natural Science Foundation of China(U1909206,61725305,61903007,62073196)in part by the S&T Program of Hebei(F2020203037).
文摘As a cross-cutting field between ocean development and multi-robot system(MRS),the underwater multi-robot system(UMRS)has gained increasing attention from researchers and engineers in recent decades.In this paper,we present a comprehensive survey of cooperation issues,one of the key components of UMRS,from the perspective of the emergence of new functions.More specifically,we categorize the cooperation in terms of task-space,motion-space,measurement-space,as well as their combination.Further,we analyze the architecture of UMRS from three aspects,i.e.,the performance of the individual underwater robot,the new functions of underwater robots,and the technical approaches of MRS.To conclude,we have discussed related promising directions for future research.This survey provides valuable insight into the reasonable utilization of UMRS to attain diverse underwater tasks in complex ocean application scenarios.
基金supported in part by the International Collaborative Project of the Shanghai Committee of Science and Technology(16510711100)National Natural Science Foundation of China(61603090,61806051)+2 种基金the Fundamental Research Funds for the Central Universities(2232017D-08,2232017D-13)Shanghai Sailing Program(17YF1426100)by FDCT(Fundo para o Desenvolvimento das Ciencias e da Tecnologia)(119/2014/A3)
文摘Multi-robot systems can be applied to patrol a concerned environment for security purposes.According to different goals,this work reviews the existing researches in a multi-robot patrolling field from the perspectives of regular and adversarial patrolling.Regular patrolling requires robots to visit important locations as frequently as possible and a series of deterministic strategies are proposed,while adversarial one focuses on unpredictable robots’moving patterns to maximize adversary detection probability.Under each category,a systematic survey is done including problem statements and modeling,patrolling objectives and evaluation criteria,and representative patrolling strategies and approaches.Existing problems and open questions are presented accordingly.
基金supported by the National High Technology Research and Development Program of China(863 Program)(2015AA015403)the National Natural Science Foundation of China(61404069,61401185)the Project of Education Department of Liaoning Province(LJYL052)
文摘Nowadays, robots generally have a variety of capabilities, which often form a coalition replacing human to work in dangerous environment, such as rescue, exploration, etc. In these operating conditions, the energy supply of robots usually cannot be guaranteed. If the energy resources of some robots are consumed too fast, the number of the future tasks of the coalition will be affected. This paper will develop a novel task allocation method based on Gini coefficient to make full use of limited energy resources of multi-robot system to maximize the number of tasks. At the same time, considering resources consumption,we incorporate the market-based allocation mechanism into our Gini coefficient-based method and propose a hybrid method,which can flexibly optimize the task completion number and the resource consumption according to the application contexts.Experiments show that the multi-robot system with limited energy resources can accomplish more tasks by the proposed Gini coefficient-based method, and the hybrid method can be dynamically adaptive to changes of the work environment and realize the dual optimization goals.
文摘This paper proposes a new methodology to optimize trajectory of the path for multi-robots using improved gravitational search algorithm(IGSA) in clutter environment. Classical GSA has been improved in this paper based on the communication and memory characteristics of particle swarm optimization(PSO). IGSA technique is incorporated into the multi-robot system in a dynamic framework, which will provide robust performance, self-deterministic cooperation, and coping with an inhospitable environment. The robots in the team make independent decisions, coordinate, and cooperate with each other to accomplish a common goal using the developed IGSA. A path planning scheme has been developed using IGSA to optimally obtain the succeeding positions of the robots from the existing position in the proposed environment. Finally, the analytical and experimental results of the multi-robot path planning were compared with those obtained by IGSA, GSA and differential evolution(DE) in a similar environment. The simulation and the Khepera environment result show outperforms of IGSA as compared to GSA and DE with respect to the average total trajectory path deviation, average uncovered trajectory target distance and energy optimization in terms of rotation.
基金supported by the DEFENCE SCIENCE&TECHNOLOGY GROUP(DSTG)(9729)The Commonwealth of Australia supported this research through a Defence Science Partnerships agreement with the Australian Defence Science and Technology Group。
文摘A common assumption of coverage path planning research is a static environment.Such environments require only a single visit to each area to achieve coverage.However,some real-world environments are characterised by the presence of unexpected,dynamic obstacles.They require areas to be revisited periodically to maintain an accurate coverage map,as well as reactive obstacle avoidance.This paper proposes a novel swarmbased control algorithm for multi-robot exploration and repeated coverage in environments with unknown,dynamic obstacles.The algorithm combines two elements:frontier-led swarming for driving exploration by a group of robots,and pheromone-based stigmergy for controlling repeated coverage while avoiding obstacles.We tested the performance of our approach on heterogeneous and homogeneous groups of mobile robots in different environments.We measure both repeated coverage performance and obstacle avoidance ability.Through a series of comparison experiments,we demonstrate that our proposed strategy has superior performance to recently presented multi-robot repeated coverage methodologies.
基金Project(A1420060159) supported by the National Basic Research of China projects(60234030 60404021) supported bythe National Natural Science Foundation of China
文摘The problem of allocating a number of exploration tasks to a team of mobile robots in dynamic environments was studied. The team mission is to visit several distributed targets. The path cost of target is proportional to the distance that a robot has to move to visit the target. The team objective is to minimize the average path cost of target over all targets. Finding an optimal allocation is strongly NP-hard. The proposed algorithm can produce a near-optimal solution to it. The allocation can be cast in terms of a multi-round single-item auction by which robots bid on targets. In each auction round, one target is assigned to a robot that produces the lowest path cost of the target. The allocated targets form a forest where each tree corresponds a robot’s exploring targets set. Each robot constructs an exploring path through depth-first search in its target tree. The time complexity of the proposed algorithm is polynomial. Simulation experiments show that the allocating method is valid.
基金the National Natural Science Foundation of China(No.62063006)to the Natural Science Foundation of Guangxi Province(No.2023GXNSFAA026025)+2 种基金to the Innovation Fund of Chinese Universities Industry-University-Research(ID:2021RYC06005)to the Research Project for Young and Middle-Aged Teachers in Guangxi Universities(ID:2020KY15013)to the Special Research Project of Hechi University(ID:2021GCC028).
文摘The robotics industry has seen rapid development in recent years due to the Corona Virus Disease 2019.With the development of sensors and smart devices,factories and enterprises have accumulated a large amount of data in their daily production,which creates extremely favorable conditions for robots to perform machine learning.However,in recent years,people’s awareness of data privacy has been increasing,leading to the inability to circulate data between different enterprises,resulting in the emergence of data silos.The emergence of federated learning provides a feasible solution to this problem,and the combination of federated learning and multi-robot systems can break down data silos and improve the overall performance of robots.However,as scholars have studied more deeply,they found that federated learning has very limited privacy protection.Therefore,how to protect data privacy from infringement remains an important issue.In this paper,we first give a brief introduction to the current development of multi-robot and federated learning;second,we review three aspects of privacy protection methods commonly used,privacy protection methods for multi-robot,and Other Problems Faced by Multi-robot Systems,focusing on method comparisons and challenges;and finally draw conclusions and predict possible future research directions.
基金Supported by the Liaoning Excellent Talents in University(No.LR2015045)Liaoning Province Natural Science Foundation(No.2015020010)
文摘data is gathered to describe the relative location and relative motion state of the robots, which in turn forms the beginning stage of the fuzzy rule. The artificial immune algorithm optimizes and generates the rule data base and adaptive design considers factors in measuring the hunting efficiency. The optimized rules are applied to the hunting task and the results show that the algorithm can effectively actualize hunting of multiple mobile robots.
基金Sponsored by Excellent Young Scholars Research Fund of Beijing Institute of Technology(00Y03-13)
文摘An improved genetic algorithm is proposed to solve the problem of bad real-time performance or inability to get a global optimal/better solution when applying single-item auction （SIA） method or combinatorial auction method to multi-robot task allocation. The genetic algorithm based combinatorial auction （GACA） method which combines the basic-genetic algorithm with a new concept of ringed chromosome is used to solve the winner determination problem （WDP） of combinatorial auction. The simulation experiments are conducted in OpenSim, a multi-robot simulator. The results show that GACA can get a satisfying solution in a reasonable shot time, and compared with SIA or parthenogenesis algorithm combinatorial auction （PGACA） method, it is the simplest and has higher search efficiency, also, GACA can get a global better/optimal solution and satisfy the high real-time requirement of multi-robot task allocation.
文摘This paper provides theoretical foundation for the problem of localization in multi-robot formations. Sufficient and necessary conditions for completely localizing a formation of mobile robots/vehicles in SE(2) based on distributed sensor networks and graph rigidity are proposed. A method for estimating the quality of localizations via a linearized weighted least-squares algorithm is presented, which considers incomplete and noisy sensory information. The approach in this paper had been implemented in a multi-robot system of five car-like robots equipped with omni-directional cameras and IEEE 802.11b wireless network.
