The Key Techniques Of Intelligent Perception And Formation Control For Multi-Agent

Reaserch on multi-agent system,including multi-robot and multi unmanned aerial vehicle(UAV),is getting more and more attention with the rapid development of computer,artificial intelligence(AI),control theory and communication,and has become an important research topic in related fileds at home and abroad.To promote the actual application of AI in unmanned vehicles,unmanned systems and other aspects,the basic theory of AI and robot research work fully integrated,will be the core content of the fourth industrial revolution.At the same time in the emerging filed of modernization and national defense,single robot can not satisfy the actual demand in the face of some of the more complex and require efficient parallel tasks,such as the collaboration of space UAV and ground combat robot.Multi-agent collaboration system research topic becomes the key to sovle the problem.We analyze and research the development history and research status of unmanned vehicle intelligent perception technology,deep learning technology and multi-agent collaboration.Aiming at the problem of intelligent perception and formation control,the overall scheme of multi-agent intelligent perception and formation platform is designed,which provides the research basis for the relevant theoretical algorithm verification from two aspects of hardware platform and software simulation.Secondly,to solve the problem of object recognition and tracking,object recognition algorithm of point cloud data tracking based on the establishment of the model database to specify object tracking,using support vector machine(SVM)and adaptive particle filtering algorithm,and multi-robot movement of the specified object recognition and formation tracking are realized.And then,the intelligent learning algorithm based on deep learning is studied,and the typical deep learning model,deep learning open source framework,deep reinforcement learning algotithm structure and development process are analyzed,and a robot navigation algorithm based on deep reinforcement learning is designed and implemented.Finally,according to the multi-agent coordinated formation,the formation control algorithm based on graph theory and potential filed function is studied,and the multi-agent formation tracking strategy is designed by combining the global rigid graph,the structure persistence graph and potential filed function.On this basis,the algorithm is tested and verified by using multi-agent intelligent perception and formation platform.