摘要
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).
This paper proposed an improved artificial physics （AP） method to solve the autonomous navigation problem for multiple un- manned 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.61273054,60975072)
the National Basic Research Program of China("973" Project)(Grant No.2013CB035503)
the Program for New Century Excellent Talents in University of China(Grant No.NCET-10-0021)
the Top-Notch Young Talents Program of China
the Fundamental Research Funds for the Central Universities of China
the Aeronautical Foundation of China(Grant No.20115151019)
关键词
无人地面车辆
物理方法
异构
人工
无人机
局部最优解
自主导航
无人飞行器
artificial physics, heterogeneous coordination, unmanned aerial vehicle （UAV）, unmanned ground vehicle （UGV）, au-tonomous navigation, obstacle avoidance
