Task allocation and route planning of multiple UAVs in a marine environment based on an improved particle swarm optimization algorithm

Abstract

Unmanned aerial vehicles (UAVs) are considered a promising example of an automatic emergency task in a dynamic marine environment. However, the maritime communication performance between UAVs and offshore platforms has become a severe challenge. Due to the complex marine environment, the task allocation and route planning efficiency of multiple UAVs in an intelligent ocean are not satisfactory. To address these challenges, this paper proposes an intelligent marine task allocation and route planning scheme for multiple UAVs based on improved particle swarm optimization combined with a genetic algorithm (GA-PSO). Based on the simulation of an intelligent marine control system, the traditional particle swarm optimization (PSO) algorithm is improved by introducing partial matching crossover and secondary transposition mutation. The improved GA-PSO is used to solve the random task allocation problem of multiple UAVs and the two-dimensional route planning of a single UAV. The simulation results show that compared with the traditional scheme, the proposed scheme can significantly improve the task allocation efficiency, and the navigation path planned by the proposed scheme is also optimal.

An Information-Entropy-Based Hierarchical Serialization Allocation Method for UAV Tracking in 6G Networks

“…In [25], an end-to-end collaborative multiagent reinforcement learning (MARL) scheme is presented that enables UAVs to make intelligent flight decisions for collaborative target tracking based on the past and current state of the target. In [26], a multi-UAV intelligent maritime task assignment and route planning scheme is designed based on improved particle swarm optimization combined with genetic algorithm (GA-PSO). In the proposed scheme, the traditional particle swarm optimization (PSO) is improved by introducing partial matching crossover and quadratic transposition variation based on the simulation of the intelligent ship control system.…”

“…In the proposed scheme, the traditional particle swarm optimization (PSO) is improved by introducing partial matching crossover and quadratic transposition variation based on the simulation of the intelligent ship control system. Moreover, the improved GA-PSO is used in [26] to solve the stochastic task assignment problem of multiple UAVs and the two-dimensional path planning problem of a single UAV. In [27], a multitarget tracking algorithm is proposed, in which trajectories evolve over a special Euclidean group SEð2Þ.…”

A scheduling route planning algorithm based on the dynamic genetic algorithm with ant colony binary iterative optimization for unmanned aerial vehicle spraying in multiple tea fields

“…The method is able to achieve the optimal initial trajectory for multiple 10.3389/fpls.2022.998962 UAVs. Yan et al (2021) developed a multi-UAV task allocation algorithm based on an improved particle swarm optimization algorithm, this algorithm improves the traditional particle swarm algorithm by introducing partial matching crossover and secondary transposition mutation to effectively improve the efficiency of UAV task assignments in marine environment and can optimize the navigation path. Tang et al (2021) proposed a joint global and local path planning optimization for UAV task scheduling towards crowd air monitoring, which achieves the effective utilization of UAV airborne resources by improving the mutation mechanism and adaptive inertia weights of the particle swarm algorithm.…”

“…Xu et al (2015) designed a UAV planning algorithm with minimal energy consumption by dividing the plant protection area through the grid method and reasonably allocating the spraying volume and return points of each sortie, which minimizes the total energy consumption of the UAV's work, reduces the invalid consumption of energy by the UAV in non-operating situations, and improves the UAV's operating efficiency. Although all of the aforementioned algorithms can achieve the shortest flight range for their related problems (Xu et al, 2015;Li K. et al, 2020;Wang et al, 2020;Liu et al, 2021;Shafiq et al, 2021;Tang et al, 2021;Yan et al, 2021), they differ from the multi-tea field planting route scheduling planning problem in the following three aspects: (i) the types of problems solved by each study are different, such as the vehicle routing problem, the quadratic assignment problem, the traveling salesman problem, etc. ; (ii) the applied UAV sorties and models are different, in particular, this paper focuses on the scheduling route planning problem for the single sortie of plant protection UAVs; and (iii) the environment and work content of the application are different to those of the current literature.…”

An improved arithmetic optimization algorithm with hybrid elite pool strategies

“…Furthermore, the meta-heuristic algorithm does not dependent on the gradient of the objective function, which has been proved to be more assertive in solution accuracy and applicability. Thus, meta-heuristic algorithms have been widely applied in various practical problems (Yang, 2022, Ornek, 2022, such as job-shop scheduling problems (JSP) (Gao, 2020), automatic control (Tabak, 2022), image processing (Djemame, 2019), and route planning (Yan, 2021).…”