Energy-saving Path Planning Based On Cooperative UAVs

Unmanned Aerial Vehicles(UAVs)has the advantages of low cost,high operating line,high mobility,low environmental requirements,etc.It is widely used in military,civilian and other fields.Unmanned aerial vehicle path planning,as one of the important components of unmanned aerial system,especially the path planning for multi-task point scenarios,has attracted much attention.However,UAV carries limited energy.In order to save the energy consumption of UAV as much as possible,when UAV carrys out a task for a group of ground nodes,it is necessary to plan an energy-saving path for UAV and ensure the completion of the task.For improving the efficiency of UAV to complete the task,the use of multiple UAV is a suitable choice.Therefore,the energy-saving path planning of multiple UAVs is a problem worthy of study.For the scenario where multiple UAVs collect data from a group of ground sensor nodes,this thesis carrys out the multi-UAV energy-saving path planning.The goal is to minimize the total energy consumption of the UAV system.We remark a sensor as a node.When the number of nodes is small and the distribution of nodes is not extensive,one UAV can complete all the tasks of the node.At this time,the multi-UAV path planning is supposed to be not necessary to consider the energy consumption constraints of UAV.Therefore,this thesis improves the genetic algorithm(GA)and proposes a conception of "dynamic preferential retention rate" and "replication and introduction" to solve the variable UAV number energy-saving path planning without considering energy consumption constraints.When the number of nodes is large and widely distributed,one UAV cannot guarantee the completion of all node task,where the multi-UAV path planning needs to consider the energy consumption constraints of UAV.This thesis proposes a dynamic partition node allocation algorithm for node allocation and use the improved GA to solve single UAV path planning to realize the energy saving path planning with energy consumption constraints.Firstly,the thesis models the system problem with energy saving as the goal: the objective function is to minimize the total energy consumption of the UAV system.Then,the objective function is analyzed and converted,and an improved GA to solve the problem is proposed.Due to the randomness of the GA algorithm,the algorithm has the disadvantages of slow convergence and easy access to local optimality.We propose a concept of replication and introduction,which can ensure the inheritance of the excellent individual of the algorithm and the diversity of the population.We have also proposed the concept of dynamic preferential retention rate,which can reasonably retain the new individual after cross mutation according to the different of individuals.The improvement of GA in this thesis not only improves the local search ability of the algorithm,but also improves the global search ability of the algorithm.This thesis solves the optimal number of UAV that the total energy consumption of the UAV system is the smallest under this UAV number.Due to GA and related improved algorithms,when considering the problem of energy consumption constraints,the algorithm’s convergence speed and convergence accuracy cannot obtain satisfactory results.So this thesis proposes a node assignment algorithm based on dynamic partitioning to assign nodes in the scenario of this thesis.The algorithm will ensure that there is no intersection of paths between UAVs,avoiding the problem of increased energy consumption caused by the intersection of paths.Then this thesis use the distribution results to solve the path planning for a single UAV using improved GA.Finally,the algorithm is analyzed and compared with the existing algorithm.The results show that the algorithm of this thesis not only has a faster convergence speed,but also can achieve a better purpose of energy saving than existing algorithms.