Research And Application Of The Intelligent Collaboration Algorithms

In recent years,with the advent of the intelligent era,the application of artificial intelligence algorithms has gradually become a hot research topic in various fields.As one of the important contents in artificial intelligence,intelligent collaboration attempts to make multiple agents have efficient collaboration capabilities by studying the advanced intelligent algorithms.The research on intelligent collaboration algorithms brings new challenges to the development of artificial intelligence,modern optimization theories,and machine learning.At the same time,these new theoretical and technological developments can further expand and improve their application fields.The core of intelligent collaboration is that multi-agent achieve optimal overall performance through effective collaboration control.This thesis comprehensively utilizes the theory of computational intelligence to study the key technical problems in intelligent collaboraton system,which are collision avoidance,collaborative task assignment and collaborative path planning etc.This provides technical support for future intelligent collaboration control,and has strong research significance and practical value.The main research content of this thesis includes the following four parts:(1)Collision avoidance under dynamic environment.Based on the traditional reinforcement learning,an improved reinforcement learning algorithm is proposed for the real-time collision avoidance problem in dynamic environments.The principle of the proposed algorithm is also analyzed.By considering the moving direction of the dynamic obstacle in the definition of state space,the ambiguity caused by update of Q values is eliminated.In order to increase the diversity of optimal solution,the greedy strategy and “near target” strategy are combined to select the next action in strategy selection.In addition,we redefine the reward function to make it more reasonable and allow the robot to reach the target as quickly as possible and apply genetic algorithm to optimize it.Through these improvements,the success rate of the proposed algorithm is greatly improved compared with the traditional reinforcement learning algorithm.(2)Multi-agent defense and attack.On basis of deep reinforcement learning,an efficient multi-agent coordination algorithm is presented and verified in the defense and attack problem.In view of the fact that the traditional reinforcement learning algorithm can not solve the continuous space problem,this thesis builds the model that includes continuous state space,continuous action space and corresponding reward function.Then different learning algorithms are applied to train the model and new environments are generated to test the learning effects.The presented algorithm not only can effectively solve the situation of attack agents with ruled movements,but also has high efficiency for intelligent attack angents.In addition,we also compare with other learning algorithms to further illustrate the performance of the proposed algorithm.(3)Collaborative task assignment.Through the in-depth analysis of the cross entropy algorithm in machine learning,a multi-UAV cooperative task allocation algorithm based on cross entropy is proposed.The cross entropy method is initially applied to the estimation of small probability events in complex networks,and the collaborative task assignment problem is essentially a combinatorial optimization problem.In this paper,we describe the relationship between them,and give the formula derivation of the cross entropy method for combinatorial optimization problems.In the algorithm,we first determine the number of feasible allocation schemes according to the number of resources required by the task,and then propose the overall scheme evaluation criteria,and the optimal allocation scheme with the highest score is obtained through applying the cross entropy method.(4)Collaborative path planning.In this thesis,various types of threats and their model are considered differently,and the environment is constructed by Voronoi diagram method.In the initial path planning of multi-UAV,the classical ant colony algorithm is improved by redefining the heuristic information function and pheromone update method.In order to satisfy the actual flight ability of the planned path,a k-degree smoothing algorithm is proposed to smooth the initial path.Besides,the theoretical analysis of smoothing principle is also given.Based on the smoothing algorithm,a multi-UAV collaboration strategy is proposed to meat the demand of multiple UAVs to reach the destination at the same time,or reach the destination within an acceptable time interval,which can effectively solve multi-UAV collaborative path planning problem.