A robotic planning system using the technique of expert system for multirobotic coordi-nated motion with collision-avoidance has been developed.Its general architecture and theroles of its components are introduced in...A robotic planning system using the technique of expert system for multirobotic coordi-nated motion with collision-avoidance has been developed.Its general architecture and theroles of its components are introduced in this paper.The task planning diagram of this sys-tem is also briefly explained.A mechanism for multirobotic planning has been proposed.Two examples of traffic control system,i.e.two-robot coordinated pathfinding system withcollision-avoidance have been demonstrated.In order to avoid the collision,some controlstrategies are applied.The results of this planning system are valuable and helpful for plan-ning the multirohotic coordinated motion with collision-avoidance.展开更多
The layered control architecture is designed for the need of the multirobot intelligent team formation.There are three levels:the cooperation task level,the coordination behavior level and the action planning level.Th...The layered control architecture is designed for the need of the multirobot intelligent team formation.There are three levels:the cooperation task level,the coordination behavior level and the action planning level.The cooperation task level uses the potential grid method,which improves the safety of the path and reduces the calculation complexity.The coordination behavior level uses the reinforcement learning which can strengthen the robots’ intelligence.The action planning level uses the fuzzy planning methods to realize the action matching.The communication model transfers the message in different level.This architecture shows not only the independence and the intelligence of the single robot but also the cooperation and the coordination among the robots.In each level,the task is distributed reasonably and clearly.Finally the feasibility of the architecture is verified further in the simulation of the experiment.The expansibility of the architecture is good and the architecture can be used in the similar system.展开更多
For real-time and distributed features of multi-robot system,the strategy of combining the improved artificial potential field method and the rules based on priority is proposed to study the collision avoidance planni...For real-time and distributed features of multi-robot system,the strategy of combining the improved artificial potential field method and the rules based on priority is proposed to study the collision avoidance planning in multi-robot systems. The improved artificial potential field based on simulated annealing algorithm satisfactorily overcomes the drawbacks of traditional artificial potential field method,so that robots can find a local collision-free path in the complex environment. According to the movement vector trail of robots,collisions between robots can be detected,thereby the collision avoidance rules can be obtained. Coordination between robots by the priority based rules improves the real-time property of multi-robot system. The combination of these two methods can help a robot to find a collision-free path from a starting point to the goal quickly in an environment with many obstacles. The feasibility of the proposed method is validated in the VC-based simulated environment.展开更多
In the RoboSot category, no central station or over head camera is allowed. Therefore several issues such as sensors, mechanisms and CPU boards must be taken into consideration before building an autonomous and intell...In the RoboSot category, no central station or over head camera is allowed. Therefore several issues such as sensors, mechanisms and CPU boards must be taken into consideration before building an autonomous and intelligent system for this category. Each robot must have appropriate sensors and must be able to communicate with other robots to have timely information concerning the ball and robot position in the field. Computing power also becomes and improtant factor in implementing intelligence and autonomy onboard. These issues are discussed. Then the robot structure of the Dreams Come True(DCT) from KAIST is introduced. Each robot has a Pentium Π single board computer, and Ethernet card, and USB camera and an omni directional mobile mechanism.展开更多
This paper addresses the problem of coordinating multiple mobile robots in searching for and capturing a mobile target,with the aim of reducing the capture time.Compared with the previous algorithms,we assume that the...This paper addresses the problem of coordinating multiple mobile robots in searching for and capturing a mobile target,with the aim of reducing the capture time.Compared with the previous algorithms,we assume that the target can be detected by any robot and captured successfully by two or more robots.In this paper,we assume that each robot has a limited communication range.We maintain the robots within a mobile network to guarantee the successful capture.In addition,the motion of the target is modeled and incorporated into directing the motion of the robots to reduce the capture time.A coordination algorithm considering both aspects is proposed.This algorithm can greatly reduce the expected time of capturing the mobile target.Finally,we validate the algorithm by the simulations and experiments.展开更多
Given limited terrain adaptability,most existing multirobot cooperative transportation systems(MRCTSs)mainly work on flat pavements,restricting their outdoor applications.The connectors'finite deformation capabili...Given limited terrain adaptability,most existing multirobot cooperative transportation systems(MRCTSs)mainly work on flat pavements,restricting their outdoor applications.The connectors'finite deformation capability and the control strategies'limitations are primarily responsible for this phenomenon.This study proposes a novel MRCTS based on tracked mobile robots(TMRs)to improve terrain adaptability and expand the application scenarios of MRCTSs.In structure design,we develop a novel 6-degree-of-freedom passive adaptive connector to link multiple TMRs and the transported object(the communal payload).In addition,the connector is set with sensors to measure the position and orientation of the robot with respect to the object for feedback control.In the control strategy,we present a virtual leader-physical follower collaborative paradigm.The leader robot is imaginary to describe the movement of the entire system and manage the follower robots.All the TMRs in the system act as follower robots to transport the object cooperatively.Having divided the whole control structure into the leader robot level and the follower robot level,we convert the motion control of the two kinds of robots to trajectory tracking control problems and propose a novel double closed-loop kinematics control framework.Furthermore,a control law satisfying saturation constraints is derived to ensure transportation stability.An adaptive control algorithm processes the wheelbase uncertainty of the TMR.Finally,we develop a prototype of the TMR-based MRCTS for experiments.In the trajectory tracking experiment,the developed MRCTS with the proposed control scheme can converge to the reference trajectory in the presence of initial tracking errors in a finite time.In the outdoor experiment,the proposed MRCTS consisting of four TMRs can successfully transport a payload weighing 60 kg on an uneven road with the single TMR's maximum load limited to 15 kg.The experimental results demonstrate the effectiveness of the structural design and control strategies of the TMR-based MRCTS.展开更多
Aiming at the k-winners-take-all(kWTA)operation,this paper proposes a gradient-based differential kWTA(GDk WTA)network.After obtaining the network,theorems and related proofs are provided to guarantee the exponential ...Aiming at the k-winners-take-all(kWTA)operation,this paper proposes a gradient-based differential kWTA(GDk WTA)network.After obtaining the network,theorems and related proofs are provided to guarantee the exponential convergence and noise resistance of the proposed GD-kWTA network.Then,numerical simulations are conducted to substantiate the preferable performance of the proposed network as compared with the traditional ones.Finally,the GD-k WTA network,backed with a consensus filter,is utilized as a robust control scheme for modeling the competition behavior in the multi-robot coordination,thereby further demonstrating its effectiveness and feasibility.展开更多
This paper introduces a pioneering dynamic system optimisation for multiagent(DySOMA)framework,revolutionising task scheduling in dynamic intelligent spaces with an emphasis on multirobot systems.The core of DySOMA is...This paper introduces a pioneering dynamic system optimisation for multiagent(DySOMA)framework,revolutionising task scheduling in dynamic intelligent spaces with an emphasis on multirobot systems.The core of DySOMA is an advanced auction-based algorithm coupled with a novel task preemption ranking mechanism,seamlessly integrated with an ontology knowledge graph that dynamically updates.This integration not only enhances the efficiency of task allocation among robots but also significantly improves the adaptability of the system to environmental changes.Compared to other advanced algorithms,the DySOMA algorithm shows significant performance improvements,with its RLB 26.8%higher than that of the best-performing Consensus-Based Parallel Auction and Execution(CBPAE)algorithm at 10 robots and 29.7%higher at 20 robots,demonstrating its superior capability in balancing task loads and optimising task completion times in larger,more complex environments.DySOMA sets a new benchmark for intelligent robot task scheduling,promising significant advancements in the autonomy and flexibility of robotic systems in complex evolving environments.展开更多
This paper introduces a pioneering dynamic system optimisation for multiagent(DysOMA)framework,revolutionising task scheduling in dynamic intelligent spaces with an emphasis on multirobot systems.The core of DysOMA is...This paper introduces a pioneering dynamic system optimisation for multiagent(DysOMA)framework,revolutionising task scheduling in dynamic intelligent spaces with an emphasis on multirobot systems.The core of DysOMA is an advanced auctionbased algorithm coupled with a novel task preemption ranking mechanism,seamlessly integrated with an ontology knowledge graph that dynamically updates.This integration not only enhances the efficiency of task allocation among robots but also significantly improves the adaptability of the system to environmental changes.Compared to other advanced algorithms,the DySOMA algorithm shows significant performance improvements,with its RLB 26.8%higher than that of the best-performing Consensus-Based Parallel Auction and Execution(CBPAE)algorithm at 10 robots and 29.7%higher at 20 robots,demonstrating its superior capability in balancing task loads and optimising task completion times in larger,more complex environments.DysOMA sets a new benchmark for intelligent robot task scheduling,promising significant advancements in the autonomy and flexibility of robotic systems in complex evolving environments.展开更多
With the continuous development of robotics and artificial intelligence,robots are being increasingly used in various applications.For traditional navigation algorithms,such as Dijkstra and A*,many dynamic scenarios i...With the continuous development of robotics and artificial intelligence,robots are being increasingly used in various applications.For traditional navigation algorithms,such as Dijkstra and A*,many dynamic scenarios in life are difficult to cope with.To solve the navigation problem of complex dynamic scenes,we present an improved reinforcement-learning-based algorithm for local path planning that allows it to perform well even when more dynamic obstacles are present.The method applies the gmapping algorithm as the upper layer input and uses reinforcement learning methods as the output.The algorithm enhances the robots’ability to actively avoid obstacles while retaining the adaptability of traditional methods.展开更多
文摘A robotic planning system using the technique of expert system for multirobotic coordi-nated motion with collision-avoidance has been developed.Its general architecture and theroles of its components are introduced in this paper.The task planning diagram of this sys-tem is also briefly explained.A mechanism for multirobotic planning has been proposed.Two examples of traffic control system,i.e.two-robot coordinated pathfinding system withcollision-avoidance have been demonstrated.In order to avoid the collision,some controlstrategies are applied.The results of this planning system are valuable and helpful for plan-ning the multirohotic coordinated motion with collision-avoidance.
基金Sponsored by the Scientific Research Foundation of Beijing Normal University and the Grants from the National Postdocteral Foundation of China.
文摘The layered control architecture is designed for the need of the multirobot intelligent team formation.There are three levels:the cooperation task level,the coordination behavior level and the action planning level.The cooperation task level uses the potential grid method,which improves the safety of the path and reduces the calculation complexity.The coordination behavior level uses the reinforcement learning which can strengthen the robots’ intelligence.The action planning level uses the fuzzy planning methods to realize the action matching.The communication model transfers the message in different level.This architecture shows not only the independence and the intelligence of the single robot but also the cooperation and the coordination among the robots.In each level,the task is distributed reasonably and clearly.Finally the feasibility of the architecture is verified further in the simulation of the experiment.The expansibility of the architecture is good and the architecture can be used in the similar system.
基金Sponsored by the Science Foundation for Youths of Heilongjiang province (Grant No.QC08C05)
文摘For real-time and distributed features of multi-robot system,the strategy of combining the improved artificial potential field method and the rules based on priority is proposed to study the collision avoidance planning in multi-robot systems. The improved artificial potential field based on simulated annealing algorithm satisfactorily overcomes the drawbacks of traditional artificial potential field method,so that robots can find a local collision-free path in the complex environment. According to the movement vector trail of robots,collisions between robots can be detected,thereby the collision avoidance rules can be obtained. Coordination between robots by the priority based rules improves the real-time property of multi-robot system. The combination of these two methods can help a robot to find a collision-free path from a starting point to the goal quickly in an environment with many obstacles. The feasibility of the proposed method is validated in the VC-based simulated environment.
文摘In the RoboSot category, no central station or over head camera is allowed. Therefore several issues such as sensors, mechanisms and CPU boards must be taken into consideration before building an autonomous and intelligent system for this category. Each robot must have appropriate sensors and must be able to communicate with other robots to have timely information concerning the ball and robot position in the field. Computing power also becomes and improtant factor in implementing intelligence and autonomy onboard. These issues are discussed. Then the robot structure of the Dreams Come True(DCT) from KAIST is introduced. Each robot has a Pentium Π single board computer, and Ethernet card, and USB camera and an omni directional mobile mechanism.
基金supported by the National Natural Science Foundation of China(No.60434030)
文摘This paper addresses the problem of coordinating multiple mobile robots in searching for and capturing a mobile target,with the aim of reducing the capture time.Compared with the previous algorithms,we assume that the target can be detected by any robot and captured successfully by two or more robots.In this paper,we assume that each robot has a limited communication range.We maintain the robots within a mobile network to guarantee the successful capture.In addition,the motion of the target is modeled and incorporated into directing the motion of the robots to reduce the capture time.A coordination algorithm considering both aspects is proposed.This algorithm can greatly reduce the expected time of capturing the mobile target.Finally,we validate the algorithm by the simulations and experiments.
基金supported by the National Natural Science Foundation of China(Grant No.52175237)Beijing Municipal Science and Technology Commission,China(Grant No.Z211100004021022).
文摘Given limited terrain adaptability,most existing multirobot cooperative transportation systems(MRCTSs)mainly work on flat pavements,restricting their outdoor applications.The connectors'finite deformation capability and the control strategies'limitations are primarily responsible for this phenomenon.This study proposes a novel MRCTS based on tracked mobile robots(TMRs)to improve terrain adaptability and expand the application scenarios of MRCTSs.In structure design,we develop a novel 6-degree-of-freedom passive adaptive connector to link multiple TMRs and the transported object(the communal payload).In addition,the connector is set with sensors to measure the position and orientation of the robot with respect to the object for feedback control.In the control strategy,we present a virtual leader-physical follower collaborative paradigm.The leader robot is imaginary to describe the movement of the entire system and manage the follower robots.All the TMRs in the system act as follower robots to transport the object cooperatively.Having divided the whole control structure into the leader robot level and the follower robot level,we convert the motion control of the two kinds of robots to trajectory tracking control problems and propose a novel double closed-loop kinematics control framework.Furthermore,a control law satisfying saturation constraints is derived to ensure transportation stability.An adaptive control algorithm processes the wheelbase uncertainty of the TMR.Finally,we develop a prototype of the TMR-based MRCTS for experiments.In the trajectory tracking experiment,the developed MRCTS with the proposed control scheme can converge to the reference trajectory in the presence of initial tracking errors in a finite time.In the outdoor experiment,the proposed MRCTS consisting of four TMRs can successfully transport a payload weighing 60 kg on an uneven road with the single TMR's maximum load limited to 15 kg.The experimental results demonstrate the effectiveness of the structural design and control strategies of the TMR-based MRCTS.
基金supported in part by the National Natural Science Foundation of China(62176109)the Natural Science Foundation of Gansu Province(21JR7RA531)+6 种基金the Tibetan Information Processing and Machine Translation Key Laboratory of Qinghai Province(2021-Z-003)the CAS“Light of West China”Programthe Natural Science Foundation of Chongqing(China)(cstc2020jcyjzdxm X0028)the Chongqing Entrepreneurship and Innovation Support Program for Overseas Returnees(CX2021100)the Supercomputing Center of Lanzhou Universitythe Science and Technology Project of Chengguan District of Lanzhou(2021JSCX0014)the Education Department of Gansu Province:Excellent Graduate Student“Innovation Star”Project(2021CXZX-122)。
文摘Aiming at the k-winners-take-all(kWTA)operation,this paper proposes a gradient-based differential kWTA(GDk WTA)network.After obtaining the network,theorems and related proofs are provided to guarantee the exponential convergence and noise resistance of the proposed GD-kWTA network.Then,numerical simulations are conducted to substantiate the preferable performance of the proposed network as compared with the traditional ones.Finally,the GD-k WTA network,backed with a consensus filter,is utilized as a robust control scheme for modeling the competition behavior in the multi-robot coordination,thereby further demonstrating its effectiveness and feasibility.
基金supported by the Natural Science Foundation of Shandong Province(No.ZR2024MF085)the Jinan Science and Technology Bureau(No.2021GXRC026)+1 种基金Young Scholars Program of Shandong University(No.2018WLJH71)the Fundamental Research Funds of Shandong University and the Taishan Scholar Foundation of Shandong Province.
文摘This paper introduces a pioneering dynamic system optimisation for multiagent(DySOMA)framework,revolutionising task scheduling in dynamic intelligent spaces with an emphasis on multirobot systems.The core of DySOMA is an advanced auction-based algorithm coupled with a novel task preemption ranking mechanism,seamlessly integrated with an ontology knowledge graph that dynamically updates.This integration not only enhances the efficiency of task allocation among robots but also significantly improves the adaptability of the system to environmental changes.Compared to other advanced algorithms,the DySOMA algorithm shows significant performance improvements,with its RLB 26.8%higher than that of the best-performing Consensus-Based Parallel Auction and Execution(CBPAE)algorithm at 10 robots and 29.7%higher at 20 robots,demonstrating its superior capability in balancing task loads and optimising task completion times in larger,more complex environments.DySOMA sets a new benchmark for intelligent robot task scheduling,promising significant advancements in the autonomy and flexibility of robotic systems in complex evolving environments.
基金supported by the Natural Science Foundation of Shandong Province(No.ZR2024MF085)the Jinan Science and Technology Bureau(No.2021GXRC026)+1 种基金Young Scholars Program ofShandong University(No.2018WLJH71)the Fundamental Research Funds of Shandong University and the Taishan Scholar Foundation of Shandong Province.
文摘This paper introduces a pioneering dynamic system optimisation for multiagent(DysOMA)framework,revolutionising task scheduling in dynamic intelligent spaces with an emphasis on multirobot systems.The core of DysOMA is an advanced auctionbased algorithm coupled with a novel task preemption ranking mechanism,seamlessly integrated with an ontology knowledge graph that dynamically updates.This integration not only enhances the efficiency of task allocation among robots but also significantly improves the adaptability of the system to environmental changes.Compared to other advanced algorithms,the DySOMA algorithm shows significant performance improvements,with its RLB 26.8%higher than that of the best-performing Consensus-Based Parallel Auction and Execution(CBPAE)algorithm at 10 robots and 29.7%higher at 20 robots,demonstrating its superior capability in balancing task loads and optimising task completion times in larger,more complex environments.DysOMA sets a new benchmark for intelligent robot task scheduling,promising significant advancements in the autonomy and flexibility of robotic systems in complex evolving environments.
基金supported in part by the National Key Research and Development Project of China(No.2019YFB2102500)the Natural Science Foundation of Hebei Province(No.F2018201115).
文摘With the continuous development of robotics and artificial intelligence,robots are being increasingly used in various applications.For traditional navigation algorithms,such as Dijkstra and A*,many dynamic scenarios in life are difficult to cope with.To solve the navigation problem of complex dynamic scenes,we present an improved reinforcement-learning-based algorithm for local path planning that allows it to perform well even when more dynamic obstacles are present.The method applies the gmapping algorithm as the upper layer input and uses reinforcement learning methods as the output.The algorithm enhances the robots’ability to actively avoid obstacles while retaining the adaptability of traditional methods.
