Path planning of Uninhabited Aerial Vehicle(UAV) is a complicated global optimum problem.In the paper,an improved Gravitational Search Algorithm(GSA) was proposed to solve the path planning problem.Gravitational Search Algorithm(GSA) is a newly presented under the inspiration of the Newtonian gravity,and it is easy to fall local best.On the basis of introducing the idea of memory and social information of Particle Swarm Optimization(PSO),a novel moving strategy in the searching space was designed,which can improve the quality of the optimal solution.Subsequently,a weighted value was assigned to inertia mass of every agent in each iteration process to accelerate the convergence speed of the search.Particle position was updated according to the selection rules of survival of the fittest.In this way,the population is always moving in the direction of the optimal solution.The feasibility and effectiveness of our improved GSA approach was verified by comparative experimental results with PSO,basic GSA and two other GSA models.
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.
DUAN HaiBin & LIU SenQi National Key Laboratory of Science and Technology on Holistic Flight Control,School of Automation Science and Electrical Engineering,Beijing University of Aeronautics and Astronautics,Beijing 100191,China
Bio-inspired intelligence is in the spotlight in the field of international artificial intelligence,and unmanned combat aerial vehicle(UCAV),owing to its potential to perform dangerous,repetitive tasks in remote and hazardous,is very promising for the technological leadership of the nation and essential for improving the security of society.On the basis of introduction of bioinspired intelligence and UCAV,a series of new development thoughts on UCAV control are proposed,including artificial brain based high-level autonomous control for UCAV,swarm intelligence based cooperative control for multiple UCAVs,hy-brid swarm intelligence and Bayesian network based situation assessment under complicated combating environments, bio-inspired hardware based high-level autonomous control for UCAV,and meta-heuristic intelligence based heterogeneous cooperative control for multiple UCAVs and unmanned combat ground vehicles(UCGVs).The exact realization of the proposed new development thoughts can enhance the effectiveness of combat,while provide a series of novel breakthroughs for the intelligence,integration and advancement of future UCAV systems.
DUAN HaiBin 1 ,SHAO Shan 2 ,SU BingWei 3 &ZHANG Lei 41 State Key Laboratory of Science and Technology on Holistic Flight Control,School of Automation Science and Electrical Engineering, Beijing University of Aeronautics and Astronautics,Beijing 100191,China
Multiple unmanned air vehicles(UAVs)/unmanned ground vehicles(UGVs) heterogeneous cooperation provides a new breakthrough for the effective application of UAV and UGV.On the basis of introduction of UAV/UGV mathematical model,the characteristics of heterogeneous flocking is analyzed in detail.Two key issues are considered in multi-UGV subgroups,which are Reynolds Rule and Virtual Leader(VL).Receding Horizon Control(RHC) with Particle Swarm Optimization(PSO) is proposed for multiple UGVs flocking,and velocity vector control approach is adopted for multiple UAVs flocking.Then,multiple UAVs and UGVs heterogeneous tracking can be achieved by these two approaches.The feasibility and effectiveness of our proposed method are verified by comparative experiments with artificial potential field method.
