Multiple unmanned air/ground vehicles heterogeneous cooperation is a novel and challenging filed.Heterogeneous cooperative techniques can widen the application fields of unmanned air or ground vehicles,and enhance the...Multiple unmanned air/ground vehicles heterogeneous cooperation is a novel and challenging filed.Heterogeneous cooperative techniques can widen the application fields of unmanned air or ground vehicles,and enhance the effectiveness of implementing detection,search and rescue tasks.This paper mainly focused on the key issues in multiple unmanned air/ground vehicles heterogeneous cooperation,including heterogeneous flocking,formation control,formation stability,network control,and actual applications.The main problems and future directions in this field were also analyzed in detail.These innovative technologies can significantly enhance the effectiveness of implementing complicated tasks,which definitely provide a series of novel breakthroughs for the intelligence,integration and advancement of future robot systems.展开更多
This paper studies the problem of using multiple unmanned air vehicles (UAVs) to search for moving targets with sensing capabilities. When multiple UAVs (multi-UAV) search for a number of moving targets in the mission...This paper studies the problem of using multiple unmanned air vehicles (UAVs) to search for moving targets with sensing capabilities. When multiple UAVs (multi-UAV) search for a number of moving targets in the mission area, the targets can intermittently obtain the position information of the UAVs from sensing devices, and take appropriate actions to increase the distance between themselves and the UAVs. Aiming at this problem, an environment model is established using the search map, and the updating method of the search map is extended by considering the sensing capabilities of the moving targets. A multi-UAV search path planning optimization model based on the model predictive control (MPC) method is constructed, and a hybrid particle swarm optimization algorithm with a crossover operator is designed to solve the model. Simulation results show that the proposed method can effectively improve the cooperative search efficiency and can find more targets per unit time compared with the coverage search method and the random search method.展开更多
基金supported by the National Natural Science Foundation of China (Grant Nos.60975072 and 60604009)the Program for New Century Excellent Talents in University of China (Grant No.NCET-10-0021)the Beijing NOVA Program Foundation (Grant No.2007A017)
文摘Multiple unmanned air/ground vehicles heterogeneous cooperation is a novel and challenging filed.Heterogeneous cooperative techniques can widen the application fields of unmanned air or ground vehicles,and enhance the effectiveness of implementing detection,search and rescue tasks.This paper mainly focused on the key issues in multiple unmanned air/ground vehicles heterogeneous cooperation,including heterogeneous flocking,formation control,formation stability,network control,and actual applications.The main problems and future directions in this field were also analyzed in detail.These innovative technologies can significantly enhance the effectiveness of implementing complicated tasks,which definitely provide a series of novel breakthroughs for the intelligence,integration and advancement of future robot systems.
基金supported by the National Natural Science Foundation of China(7140104871671059)the National Natural Science Funds of China for Innovative Research Groups(71521001)
文摘This paper studies the problem of using multiple unmanned air vehicles (UAVs) to search for moving targets with sensing capabilities. When multiple UAVs (multi-UAV) search for a number of moving targets in the mission area, the targets can intermittently obtain the position information of the UAVs from sensing devices, and take appropriate actions to increase the distance between themselves and the UAVs. Aiming at this problem, an environment model is established using the search map, and the updating method of the search map is extended by considering the sensing capabilities of the moving targets. A multi-UAV search path planning optimization model based on the model predictive control (MPC) method is constructed, and a hybrid particle swarm optimization algorithm with a crossover operator is designed to solve the model. Simulation results show that the proposed method can effectively improve the cooperative search efficiency and can find more targets per unit time compared with the coverage search method and the random search method.
