Multi-UAV Mission Allocation under Constraint

Abstract

Featured Application: The improved model can effectively adapt to the problem of UAV swarm reconnaissance task allocation. On the basis of ensuring the optimization of reconnaissance reward, the UAV resources should be distributed as much as possible to reasonably complete the overall combat mission.Abstract: This paper is devoted to the unmanned aerial vehicle (UAV) mission allocation problem. To solve this problem in a more realistic battlefield environment, an improved mathematical model for UAV mission allocation is proposed. Being different from previous formulations, this model not only considers the difference in the importance of the target but also the constraints of the time window. In addition, an indicator of reconnaissance reward is added to this model. Each target area has a different importance, just as the strategic value of each region is different in combat. In this paper, we randomly generate the value factor for each reconnaissance area. To solve the mathematical model with different operational intentions, a dimensionality reduction process for which the reconnaissance reward is the optimization objective is presented. Finally, based on the improved model, the simulation result with Lingo is compared with that of non-dominated sorting genetic algorithm with elite strategy (NSGA-II) and genetic algorithm (GA) to verify the reliability and the effectiveness of the improved method. Appl. Sci. 2019, 9, 2184 2 of 17 an improved genetic algorithm for the UAV mission planning problem with limited resources, and the simulation results were analyzed and verified [14]. Lanah et al. presented a maximum coverage stochastic orienteering problem with time windows (MCS-OPTW) planning approach for mission planning and showed the simulation data [15]. Zhu et al. proposed a new reconnaissance mission planning model and used a genetic algorithm with the UAVs' task sequence to solve the model [16]. The existing reconnaissance mission models used the shortest path as the objective function and only considered a single task sequence assignment without considering the reconnaissance time allocation of the UAV. In reality, the allocation of reconnaissance time is often the decisive factor in determining the success of the reconnaissance mission and the core indicator that the commander is concerned about. The new model proposed in this paper considers the reconnaissance reward maximization as the objective function. By considering the allocation of each UAV's reconnaissance time resource, the reconnaissance time allocation and task sequence allocation are considered simultaneously.The UAV mission planning problem is the extended multiple Dubins traveling salesmen problem (MDTSP), which is a typical NP-hard combinatorial optimization problem. Some studies translated related models into a mixed integer linear programming (MILP) and achieved good results [17][18][19]. The time window was first applied to the vehicle routing problem (VRP) problem model. Righini and Salani systematically introduced the VRP with time wind...

UAV Mission Planning with SAR Application

“…The basic task of UAV route planning [17] is considered as a Vehicle Route Planning (VRP) class problem, which is a popular combinatorial optimization problem widely used in the areas of distribution and transport [18]. VRP is commonly known as the first time introduced by Dantzig and Ramser (1959) [8].…”

Cooperative Task Assignment of a Heterogeneous Multi-UAV System Using an Adaptive Genetic Algorithm

“…The increasing complexity of the electromagnetic environment and the integration of weapons systems in modern battlefields have brought unprecedented challenges to the mission execution of aerial vehicles (AV) [1,2]. Due to the low cost, zero casualty, and good flexibility of unmanned aerial vehicles (UAVs), UAVs have gradually replaced the role of AV in carrying out dangerous and harsh tasks [3,4]. However, tasks in the modern battlefield become more diverse and complex.…”

Learning-Based Anomaly Detection and Monitoring for Swarm Drone Flights

“…The use of swarm of small aerial vehicles allows for extended mission area, flexible mission capability, robustness to single point failure, and cost effectiveness. Research areas related to swarm drones have been very diverse, including development of small-scale aerial vehicles [3][4][5], ad hoc communication backbone tailored to swarm operation [6][7][8], path generation to ensure collision avoidance [9][10][11], mission-level planning and scheduling to achieve high-level autonomy [12][13][14][15], and interaction/interface between the human operator and the swarm drones [16][17][18][19]. It should be noted that while earlier literature focused on technologies to enhance performance and capabilities, recent work has been looking into more safe, secure, and reliable operations of such swarm systems [20][21][22][23].…”