The development of intelligent algorithms for controlling autonomous mobile robots in real-time activities has increased dramatically in recent years.However,conventional intelligent algorithms currently fail to accur...The development of intelligent algorithms for controlling autonomous mobile robots in real-time activities has increased dramatically in recent years.However,conventional intelligent algorithms currently fail to accurately predict unexpected obstacles involved in tour paths and thereby suffer from inefficient tour trajectories.The present study addresses these issues by proposing a potential field integrated pruned adaptive resonance theory(PPART)neural network for effectively managing the touring process of autonomous mobile robots in real-time.The proposed system is implemented using the AlphaBot platform,and the performance of the system is evaluated according to the obstacle prediction accuracy,path detection accuracy,time-lapse,tour length,and the overall accuracy of the system.The proposed system provide a very high obstacle prediction accuracy of 99.61%.Accordingly,the proposed tour planning design effectively predicts unexpected obstacles in the environment and thereby increases the overall efficiency of tour navigation.展开更多
This paper presents a distributed scheme with limited communications, aiming to achieve cooperative motion control for multiple omnidirectional mobile manipulators(MOMMs).The proposed scheme extends the existing singl...This paper presents a distributed scheme with limited communications, aiming to achieve cooperative motion control for multiple omnidirectional mobile manipulators(MOMMs).The proposed scheme extends the existing single-agent motion control to cater to scenarios involving the cooperative operation of MOMMs. Specifically, squeeze-free cooperative load transportation is achieved for the end-effectors of MOMMs by incorporating cooperative repetitive motion planning(CRMP), while guiding each individual to desired poses. Then, the distributed scheme is formulated as a time-varying quadratic programming(QP) and solved online utilizing a noise-tolerant zeroing neural network(NTZNN). Theoretical analysis shows that the NTZNN model converges globally to the optimal solution of QP in the presence of noise. Finally, the effectiveness of the control design is demonstrated by numerical simulations and physical platform experiments.展开更多
Aiming at the dimension disaster problem, poor model generalization ability and deadlock problem in special obstacles environment caused by the increase of state information in the local path planning process of mobil...Aiming at the dimension disaster problem, poor model generalization ability and deadlock problem in special obstacles environment caused by the increase of state information in the local path planning process of mobile robot, this paper proposed a Double BP Q-learning algorithm based on the fusion of Double Q-learning algorithm and BP neural network. In order to solve the dimensional disaster problem, two BP neural network fitting value functions with the same network structure were used to replace the two <i>Q</i> value tables in Double Q-Learning algorithm to solve the problem that the <i>Q</i> value table cannot store excessive state information. By adding the mechanism of priority experience replay and using the parameter transfer to initialize the model parameters in different environments, it could accelerate the convergence rate of the algorithm, improve the learning efficiency and the generalization ability of the model. By designing specific action selection strategy in special environment, the deadlock state could be avoided and the mobile robot could reach the target point. Finally, the designed Double BP Q-learning algorithm was simulated and verified, and the probability of mobile robot reaching the target point in the parameter update process was compared with the Double Q-learning algorithm under the same condition of the planned path length. The results showed that the model trained by the improved Double BP Q-learning algorithm had a higher success rate in finding the optimal or sub-optimal path in the dense discrete environment, besides, it had stronger model generalization ability, fewer redundant sections, and could reach the target point without entering the deadlock zone in the special obstacles environment.展开更多
Recently,the path planning problem may be considered one of the most interesting researched topics in autonomous robotics.That is why finding a safe path in a cluttered environment for a mobile robot is a significant ...Recently,the path planning problem may be considered one of the most interesting researched topics in autonomous robotics.That is why finding a safe path in a cluttered environment for a mobile robot is a significant requisite.A promising route planning for mobile robots on one side saves time and,on the other side,reduces the wear and tear on the robot,saving the capital investment.Numerous route planning methods for the mobile robot have been developed and applied.According to our best knowledge,no method offers an optimum solution among the existing methods.Particle Swarm Optimization(PSO),a numerical optimization method based on the mobility of virtual particles in a multidimensional space,is considered one of the best algorithms for route planning under constantly changing environmental circumstances.Among the researchers,reactive methods are increasingly common and extensively used for the training of neural networks in order to have efficient route planning for mobile robots.This paper proposes a PSO Weighted Grey Wolf Optimization(PSOWGWO)algorithm.PSOWGWO is a hybrid algorithm based on enhanced Grey Wolf Optimization(GWO)with weights.In order to measure the statistical efficiency of the proposed algorithm,Wilcoxon rank-sum and ANOVA statistical tests are applied.The experimental results demonstrate a 25%to 45%enhancement in terms of Area Under Curve(AUC).Moreover,superior performance in terms of data size,path planning time,and accuracy is demonstrated over other state-of-the-art techniques.展开更多
Mobile anchors are widely used for localization in WSNs.However,special properties over 3D terrains limit the implementation of them.In this paper,a novel 3D localization algorithm is proposed,called 3 DT-PP,which uti...Mobile anchors are widely used for localization in WSNs.However,special properties over 3D terrains limit the implementation of them.In this paper,a novel 3D localization algorithm is proposed,called 3 DT-PP,which utilizes path planning of mobile anchors over complex 3 D terrains,and simulations based upon the model of mountain surface network are conducted.The simulation results show that the algorithm decreases the position error by about 91%,8.7%and lowers calculation overhead by about 75%,1.3%,than the typical state-of-the-art localization algorithm(i.e.,'MDS-MAP','Landscape-3D').Thus,our algorithm is more potential in practical WSNs which are the characteristic of limited energy and 3D deployment.展开更多
针对扩容、增加微蜂窝、站型调整和天线调整等硬件的方法解决网络拥塞问题成本高等困难,分析了全球移动通讯系统(GSM:Global System for Mobile Communications)无线网络参数对拥塞影响,利用网络参数调整等网络优化手段缓解网络中无线...针对扩容、增加微蜂窝、站型调整和天线调整等硬件的方法解决网络拥塞问题成本高等困难,分析了全球移动通讯系统(GSM:Global System for Mobile Communications)无线网络参数对拥塞影响,利用网络参数调整等网络优化手段缓解网络中无线信道拥塞的问题,并通过模拟方法验证了网络参数调整对无线信道拥塞的改善程度。仿真结果表明,合理地调整无线网络参数既可以降低网络拥塞、均衡话务量,还能较好地改善话务拥塞问题。展开更多
扫频仪是近年来在移动通信网络规划与优化中广泛应用的新型测量仪器,针对GSM(global system of mobile communication)与TD-SCDMA(time division-synchronous code division multiple acess)双网融合优化的新需求,研究了双模扫频仪技术...扫频仪是近年来在移动通信网络规划与优化中广泛应用的新型测量仪器,针对GSM(global system of mobile communication)与TD-SCDMA(time division-synchronous code division multiple acess)双网融合优化的新需求,研究了双模扫频仪技术,介绍了其空时频三维盲检测的系统模型、工作流程以及测量速度分析、兼顾接收机与测量仪器双重特性的关键算法,论述了多通道射频前端与基带电路的硬件设计方案,并给出了主要功能与性能指标的测试结果和应用实例。研制的GSM/TD-SCDMA双模五频段扫频仪,可同时进行GSM与TD-SCDMA两个网络的测量,相比传统的单模路测设备,不仅提高了效能,而且降低了成本,结构更紧凑。展开更多
文摘The development of intelligent algorithms for controlling autonomous mobile robots in real-time activities has increased dramatically in recent years.However,conventional intelligent algorithms currently fail to accurately predict unexpected obstacles involved in tour paths and thereby suffer from inefficient tour trajectories.The present study addresses these issues by proposing a potential field integrated pruned adaptive resonance theory(PPART)neural network for effectively managing the touring process of autonomous mobile robots in real-time.The proposed system is implemented using the AlphaBot platform,and the performance of the system is evaluated according to the obstacle prediction accuracy,path detection accuracy,time-lapse,tour length,and the overall accuracy of the system.The proposed system provide a very high obstacle prediction accuracy of 99.61%.Accordingly,the proposed tour planning design effectively predicts unexpected obstacles in the environment and thereby increases the overall efficiency of tour navigation.
基金supported in part by the National Natural Science Foundation of China (62373065,61873304,62173048,62106023)the Innovation and Entrepreneurship Talent funding Project of Jilin Province(2022QN04)+1 种基金the Changchun Science and Technology Project (21ZY41)the Open Research Fund of National Mobile Communications Research Laboratory,Southeast University (2024D09)。
文摘This paper presents a distributed scheme with limited communications, aiming to achieve cooperative motion control for multiple omnidirectional mobile manipulators(MOMMs).The proposed scheme extends the existing single-agent motion control to cater to scenarios involving the cooperative operation of MOMMs. Specifically, squeeze-free cooperative load transportation is achieved for the end-effectors of MOMMs by incorporating cooperative repetitive motion planning(CRMP), while guiding each individual to desired poses. Then, the distributed scheme is formulated as a time-varying quadratic programming(QP) and solved online utilizing a noise-tolerant zeroing neural network(NTZNN). Theoretical analysis shows that the NTZNN model converges globally to the optimal solution of QP in the presence of noise. Finally, the effectiveness of the control design is demonstrated by numerical simulations and physical platform experiments.
文摘Aiming at the dimension disaster problem, poor model generalization ability and deadlock problem in special obstacles environment caused by the increase of state information in the local path planning process of mobile robot, this paper proposed a Double BP Q-learning algorithm based on the fusion of Double Q-learning algorithm and BP neural network. In order to solve the dimensional disaster problem, two BP neural network fitting value functions with the same network structure were used to replace the two <i>Q</i> value tables in Double Q-Learning algorithm to solve the problem that the <i>Q</i> value table cannot store excessive state information. By adding the mechanism of priority experience replay and using the parameter transfer to initialize the model parameters in different environments, it could accelerate the convergence rate of the algorithm, improve the learning efficiency and the generalization ability of the model. By designing specific action selection strategy in special environment, the deadlock state could be avoided and the mobile robot could reach the target point. Finally, the designed Double BP Q-learning algorithm was simulated and verified, and the probability of mobile robot reaching the target point in the parameter update process was compared with the Double Q-learning algorithm under the same condition of the planned path length. The results showed that the model trained by the improved Double BP Q-learning algorithm had a higher success rate in finding the optimal or sub-optimal path in the dense discrete environment, besides, it had stronger model generalization ability, fewer redundant sections, and could reach the target point without entering the deadlock zone in the special obstacles environment.
文摘Recently,the path planning problem may be considered one of the most interesting researched topics in autonomous robotics.That is why finding a safe path in a cluttered environment for a mobile robot is a significant requisite.A promising route planning for mobile robots on one side saves time and,on the other side,reduces the wear and tear on the robot,saving the capital investment.Numerous route planning methods for the mobile robot have been developed and applied.According to our best knowledge,no method offers an optimum solution among the existing methods.Particle Swarm Optimization(PSO),a numerical optimization method based on the mobility of virtual particles in a multidimensional space,is considered one of the best algorithms for route planning under constantly changing environmental circumstances.Among the researchers,reactive methods are increasingly common and extensively used for the training of neural networks in order to have efficient route planning for mobile robots.This paper proposes a PSO Weighted Grey Wolf Optimization(PSOWGWO)algorithm.PSOWGWO is a hybrid algorithm based on enhanced Grey Wolf Optimization(GWO)with weights.In order to measure the statistical efficiency of the proposed algorithm,Wilcoxon rank-sum and ANOVA statistical tests are applied.The experimental results demonstrate a 25%to 45%enhancement in terms of Area Under Curve(AUC).Moreover,superior performance in terms of data size,path planning time,and accuracy is demonstrated over other state-of-the-art techniques.
基金Supported by the Important National Science and Technology Specific Project of China(No.20112X03002-002-03)the National NatureScience Foundation of China(No.61133016,61163066)
文摘Mobile anchors are widely used for localization in WSNs.However,special properties over 3D terrains limit the implementation of them.In this paper,a novel 3D localization algorithm is proposed,called 3 DT-PP,which utilizes path planning of mobile anchors over complex 3 D terrains,and simulations based upon the model of mountain surface network are conducted.The simulation results show that the algorithm decreases the position error by about 91%,8.7%and lowers calculation overhead by about 75%,1.3%,than the typical state-of-the-art localization algorithm(i.e.,'MDS-MAP','Landscape-3D').Thus,our algorithm is more potential in practical WSNs which are the characteristic of limited energy and 3D deployment.
文摘针对扩容、增加微蜂窝、站型调整和天线调整等硬件的方法解决网络拥塞问题成本高等困难,分析了全球移动通讯系统(GSM:Global System for Mobile Communications)无线网络参数对拥塞影响,利用网络参数调整等网络优化手段缓解网络中无线信道拥塞的问题,并通过模拟方法验证了网络参数调整对无线信道拥塞的改善程度。仿真结果表明,合理地调整无线网络参数既可以降低网络拥塞、均衡话务量,还能较好地改善话务拥塞问题。
文摘扫频仪是近年来在移动通信网络规划与优化中广泛应用的新型测量仪器,针对GSM(global system of mobile communication)与TD-SCDMA(time division-synchronous code division multiple acess)双网融合优化的新需求,研究了双模扫频仪技术,介绍了其空时频三维盲检测的系统模型、工作流程以及测量速度分析、兼顾接收机与测量仪器双重特性的关键算法,论述了多通道射频前端与基带电路的硬件设计方案,并给出了主要功能与性能指标的测试结果和应用实例。研制的GSM/TD-SCDMA双模五频段扫频仪,可同时进行GSM与TD-SCDMA两个网络的测量,相比传统的单模路测设备,不仅提高了效能,而且降低了成本,结构更紧凑。
