Dynamic Task Allocation of Multiple UAVs Based on Improved A-QCDPSO

Abstract

With the rapid changes in the battlefield situation, the requirement of time for UAV groups to deal with complex tasks is getting higher, which puts forward higher requirements for the dynamic allocation of the UAV group. However, most of the existing methods focus on task pre-allocation, and the research on dynamic task allocation technology during task execution is not sufficient. Aiming at the high real-time requirement of the multi-UAV collaborative dynamic task allocation problem, this paper introduces the market auction mechanism to design a discrete particle swarm algorithm based on particle quality clustering by a hybrid architecture. The particle subpopulations are dynamically divided based on particle quality, which changes the topology of the algorithm. The market auction mechanism is introduced during particle initialization and task coordination to build high-quality particles. The algorithm is verified by constructing two emergencies of UAV sudden failure and a new emergency task.

A Tent-Lévy-Based Seagull Optimization Algorithm for the Multi-UAV Collaborative Task Allocation Problem

“…Six UAVs are set up in experimental scenario 2, and the total number of agricultural tasks to be performed is 50, and the rest of the conditions are consistent with experimental scenario 1. The multi-UAV cooperative task allocation algorithms based on CAM-GA [23], A-QCDPSO [24], SOA [25], PSO-AWOA [26], CESMA [27], and TLISOA are selected for comparison, and each algorithm's population size is population = 30 , and the maximum iteration is iteration Max = 500 ; in order to reduce the impact of randomness in the algorithm, each algorithm is run independently 20 times, and the simulation environment is shown in Table 1.…”

A Drone Scheduling Method for Emergency Power Material Transportation Based on Deep Reinforcement Learning Optimized PSO Algorithm

“…An upgraded cellular automaton (CA) and an optimal spanning tree technique were utilized by Li et al [24] to build the path network and find the best routes between various endpoints. Zhang et al [25] dynamically divided the particle swarm based on the particle mass and changed the topology of the algorithm. In addition, dynamic problems in tasking problems are common.…”

A Two-Stage Distributed Task Assignment Algorithm Based on Contract Net Protocol for Multi-UAV Cooperative Reconnaissance Task Reassignment in Dynamic Environments

“…So, the shorter route (green dotted line) is abandoned. The list of tasks to be allocated is TL un = [20, 21,22]. The UAVs participating in task reassignment are [1], [7,16], [], [], [8], [0, 10]], and the leftover task list of UAV formation is TL = [ [13,5,4], [6,15,12], [14], [17,18], [], [], [19,9], [11,3]].…”

“…However, the performance differences of UAVs were not considered in the model. Zhang et al [ 21 ] studied the dynamic task assignment problem in the context of multi-UAVs attacking multiple ground targets cooperatively and considered the emergence of new targets and sudden UAV failure. However, the time window constraint of targets and movement of targets were not considered in the model.…”