Cooperative Decision And Control For Multiple UAVs Under Limited Communication

Cooperative decision and control is the key to realize the cooperative operation for multi-UAVs.However,due to limited communication conditions and complex dynamic environment,there are also some significant challenges for cooperative decision and control of multi-UAVs.For this reason,in the background of multi-UAVs cooperative search and attack mission in unknown environment,this dissertation focuses on the cooperative decision and control problem for multi-UAVs under communication constraints,in order to improve the ability of multi-UAVs to collaborate and efficiently perform complex dynamic tasks in a restricted communications environment.The main works of this dissertation are as follow:(1)A cooperative search and coverage algorithm with connectivity maintenance for multi-UAVs(which have limited communication ranges)is presented,which aim to minimize the search time,while finding more targets.The target probability map(TPM),the uncertain map(UM),digital pheromone map(DPM)and their calculating mechanisms are constituted.In the frame of distributed receding horizon optimizing,a path planning algorithm for the multi-UAVs cooperative search and coverage is designed.The movement of the UAVs is restricted by the potential fields to meet the requirements of avoiding collision and maintaining connectivity constraints.We develop a controllable revisit mechanism based on the DPM.This controllable revisit mechanism can concentrate the UAVs to revisit sub-areas that have a large target probability or high uncertainty.Moreover,we use the minimum spanning tree(MST)topology optimization strategy to obtain a tradeoff between the search coverage enhancement and the connectivity maintenance.The MST topology optimization strategy allows the redundant edges for a connected graph are not necessarily maintained,and hence gives more freedom for the UAVs to control their motion during search process.The simulation results show that,the controllable revisit mechanism could enhance the capacities of target capture and region coverage for the UAVs compared to the method that does not consider the controllable revisit mechanism.Compared with the full connected topology,the MST topology can relax the motion restrictions of UAVs and improve the efficiency of cooperative search operation.(2)A novel method for coalition formation of multi-UAVs in cooperative search and attack in unknown environment is presented,which aim to form a sub-group of UAVs(called a coalition)to attack a found target.The coalition formation model is built on basis of the minimum attacking time and the minimum coalition size with satisfying resources and simultaneous strikes requirements.Considering limited communication ranges and communication delays,a mechanism to find potential coalition members within a maximum number of hops over a dynamic network is developed.A multistage sub-optimal coalition formation algorithm(MSOCFA)that has low computational complexity is proposed to solve the optimization problem of coalition formation.The Monte-Carlo simulation results show that,(1)when communication delay is less,finding potential coalition members deep in the network is beneficial,but when the communication delay is significant,it is better to select coalition members within immediate neighborhood.(2)Increasing the number of maximum allowed hops for message and decreasing the hop delays will improve success rate in coalition formation.In addition,compared with particle swarm optimization algorithm(PSO),the MSOCFA can be used in unknown environment,and has less computational time.(3)A novel multi-UAVs cooperative target tracking algorithm based on co-optimization of communication and sensing strategy is presented,which can generate information-gathering trajectories considering limited communication ranges and packet loss.A packet-erasure channel model is used to describe the realistic wireless communication links,in which the probability of a successful information transmission is modeled as a function of the signal to noise ratio(SNR).The Fisher information matrix(FIM)is used to quantify the information gained in target tracking.A scalar metric is used for trajectories panning over a finite time horizon.This scalar metric is a utility function of the expected information gain and the probability of a successful information transmission.With the combining of the sensing and communication into a utility function,the co-optimization of communication and sensing is reflected in the tradeoffs between maximizing information gained and increasing the probability of a successful transmission with the base station.The simulation results show that the proposal approach could effectively improve estimation performance compared to method that does not consider communication optimization.(4)At last,the simulation platform of multi-UAVs cooperatively searching and attacking several targets under communication limitations is designed.A distributed autonomous coordination strategy is proposed which is based on the Finite-State Machine(FSM).We provide procedures to track and attack a maneuvering target using multi-UAVs with limited sensor ranges.The simulation of the cooperative search and attack process is carried out in the simulation platform,and the simulation results sufficiently validate the rationality of the distributed autonomous coordination strategy and the proposed algorithms.