This paper proposed an improved artificial physics(AP)method to solve the autonomous navigation problem for multiple unmanned aerial vehicles(UAVs)/unmanned ground vehicles(UGVs)heterogeneous coordination in the three...This paper proposed an improved artificial physics(AP)method to solve the autonomous navigation problem for multiple unmanned aerial vehicles(UAVs)/unmanned ground vehicles(UGVs)heterogeneous coordination in the three-dimensional space.The basic AP method has a shortcoming of easily plunging into a local optimal solution,which can result in navigation fails.To avoid the local optimum,we improved the AP method with a random scheme.In the improved AP method,random forces are used to make heterogeneous multi-UAVs/UGVs escape from local optimum and achieve global optimum.Experimental results showed that the improved AP method can achieve smoother trajectories and smaller time consumption than the basic AP method and basic potential field method(PFM).展开更多
This paper proposed a modified artificial physics(AP)method to solve the autonomous navigation problem for mobile robots in complex environments.The basic AP method tends to cause oscillations in the presence of obsta...This paper proposed a modified artificial physics(AP)method to solve the autonomous navigation problem for mobile robots in complex environments.The basic AP method tends to cause oscillations in the presence of obstacles and in narrow passages,which can result in time consumption.To alleviate oscillation,we modified the AP method using the Levenbery-Marquardt(LM)algorithm.In the modified AP method,we altered the original directions of AP forces to the Newton direction,and adjust the parameter by the LM algorithm.A series of comparative experimental results show that the modified AP method can achieve smoother trajectories with less time consumption.This demonstrates the feasibility and effectiveness of our proposed approach.展开更多
In this paper, a new paradigm named parallel distance is presented to measure the data information in parallel driving system. As an example, the core variables in the parallel driving system are measured and evaluate...In this paper, a new paradigm named parallel distance is presented to measure the data information in parallel driving system. As an example, the core variables in the parallel driving system are measured and evaluated in the parallel distance framework. First, the parallel driving 3.0 system included control and management platform, intelligent vehicle platform and remote-control platform is introduced. Then,Markov chain(MC) is utilized to model the transition probability matrix of control commands in these systems. Furthermore, to distinguish the control variables in artificial and physical driving conditions, different distance calculation methods are enumerated to specify the differences between the virtual and real signals. By doing this, the real system can be guided and the virtual system can be im-proved. Finally, simulation results exhibit the merits and multiple applications of the proposed parallel distance framework.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.61273054,60975072)the National Basic Research Program of China("973" Project)(Grant No.2013CB035503)+3 种基金the Program for New Century Excellent Talents in University of China(Grant No.NCET-10-0021)the Top-Notch Young Talents Program of Chinathe Fundamental Research Funds for the Central Universities of Chinathe Aeronautical Foundation of China(Grant No.20115151019)
文摘This paper proposed an improved artificial physics(AP)method to solve the autonomous navigation problem for multiple unmanned aerial vehicles(UAVs)/unmanned ground vehicles(UGVs)heterogeneous coordination in the three-dimensional space.The basic AP method has a shortcoming of easily plunging into a local optimal solution,which can result in navigation fails.To avoid the local optimum,we improved the AP method with a random scheme.In the improved AP method,random forces are used to make heterogeneous multi-UAVs/UGVs escape from local optimum and achieve global optimum.Experimental results showed that the improved AP method can achieve smoother trajectories and smaller time consumption than the basic AP method and basic potential field method(PFM).
基金supported by the National Natural Science Foundation of China(Grant Nos.61273054 and 61333004)the National Key Basic Research Program of China(Grant No.2014CB046401)+2 种基金the Program for New Century Excellent Talents in University of China(Grant No.NCET-10-0021)the Top-Notch Young Talents Program of China,Graduate Innovation Foundation for Beihang University(Grant No.YCSJ-01-201206)Aeronautical Foundation of China(Grant No.20135851042)
文摘This paper proposed a modified artificial physics(AP)method to solve the autonomous navigation problem for mobile robots in complex environments.The basic AP method tends to cause oscillations in the presence of obstacles and in narrow passages,which can result in time consumption.To alleviate oscillation,we modified the AP method using the Levenbery-Marquardt(LM)algorithm.In the modified AP method,we altered the original directions of AP forces to the Newton direction,and adjust the parameter by the LM algorithm.A series of comparative experimental results show that the modified AP method can achieve smoother trajectories with less time consumption.This demonstrates the feasibility and effectiveness of our proposed approach.
基金supported in part by the National Natural Science Foundation of China(61533019,91720000)Beijing Municipal Science and Technology Commission(Z181100008918007)the Intel Collaborative Research Institute for Intelligent and Automated Connected Vehicles(ICRI-IACV)。
文摘In this paper, a new paradigm named parallel distance is presented to measure the data information in parallel driving system. As an example, the core variables in the parallel driving system are measured and evaluated in the parallel distance framework. First, the parallel driving 3.0 system included control and management platform, intelligent vehicle platform and remote-control platform is introduced. Then,Markov chain(MC) is utilized to model the transition probability matrix of control commands in these systems. Furthermore, to distinguish the control variables in artificial and physical driving conditions, different distance calculation methods are enumerated to specify the differences between the virtual and real signals. By doing this, the real system can be guided and the virtual system can be im-proved. Finally, simulation results exhibit the merits and multiple applications of the proposed parallel distance framework.
