Multi-UAV Reconnaissance Task Assignment for Heterogeneous Targets Based on Modified Symbiotic Organisms Search Algorithm

Chen, Haoxiang; Nan, Ying; Yang, Yi

doi:10.3390/s19030734

Cited by 41 publications

(19 citation statements)

References 31 publications

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“…In this paper, we choose three kinds of representative tasks, which are the reconnaissance task [30], striking task [31], and damage assessment task [32], respectively, to study the damage-tolerant task assignment algorithm. And we assume that all UAVs would take off at the base, and each UAV's departure time is determined by the moment the first task of it is assigned.…”

Section: Task Model and Uav Modelmentioning

confidence: 99%

“…If the overlapping assignment attempt fails, it tries the nonoverlapping mode (see line 28). If the nonoverlapping assignment attempt succeeds, task t i P would be scheduled in the nonoverlapping mode (see lines [29][30], and the backup copy of t i P would be assigned according to whether it is needed (see lines [31][32][33]; then Algorithm 1 terminates for another task (see line 34). If the task fails to be assigned in both the overlapping mode and nonoverlapping mode, the candidate UAV set would be updated to be the next top α% nearest UAVs (see line 35), and a new round assigning attempt would start.…”

Section: Assigning Algorithm For Primary Copiesmentioning

confidence: 99%

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A Damage-Tolerant Task Assignment Algorithm for UAV Swarm in Confrontational Environments

Chen

Bao

Men

et al. 2020

International Journal of Aerospace Engineering

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As Unmanned Aerial Vehicles (UAVs) are widely used in many applications, a lot of military missions in confrontational environments are being undertaken by UAV swarm rather than human beings due to its advantages. In confrontational environments, the reliability and availability of UAV swarm would be the major concern because of UAVs’ vulnerability, so damage-tolerant task assigning algorithms are of great importance. In this paper, we come up with a novel damage-tolerant framework for assigning real-time tasks to UAVs with dynamical states in confrontational environments. Different from existing scheduling methods, we not only assign tasks but also back up copies of tasks to UAVs when needed, to promote reliability. Meanwhile, we adopt an overlapping mechanism, including Backup-Primary overlapping and Backup-Backup overlapping, in assignment to save the limited swarm resources. On the basis of the damage-tolerant and overlapping mechanism, for the first time, we propose a new damage-tolerant task assignment algorithm named DTTA, aiming at promoting the task success probability. Extensive experiments are conducted based on random synthetic workloads to compare DTTA with three baseline algorithms. The experimental results indicate that DTTA can efficiently promote the probability of tasks’ success without affecting the effectiveness of swarms in confrontational environments.

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Section: Task Model and Uav Modelmentioning

confidence: 99%

Section: Assigning Algorithm For Primary Copiesmentioning

confidence: 99%

A Damage-Tolerant Task Assignment Algorithm for UAV Swarm in Confrontational Environments

Chen

Bao

Men

et al. 2020

International Journal of Aerospace Engineering

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“…Paucar et al [9] deal with the use of UAVs for surveillance and reconnaissance operations in military areas and analyse the benefits of their model in the Ecuadorian Armed Forces. Chen et al [10] consider a reconnaissance task assignment problem for multiple UAVs with different sensor capacities; the authors propose a modified multi-objective symbiotic organisms search (MOSOS) algorithm to solve the problem. Wang et al [11] model UAV reconnaissance mission planning as an uncertain multi-objective orienteering problem; to solve this problem, the authors propose a discreet multi-objective bat algorithm with a local search strategy.…”

Section: Literature Reviewmentioning

confidence: 99%

Trajectory Optimization in a Cooperative Aerial Reconnaissance Model

Stodola

Drozd

Nohel

et al. 2019

Sensors

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In recent years, the use of modern technology in military operations has become standard practice. Unmanned systems play an important role in operations such as reconnaissance and surveillance. This article examines a model for planning aerial reconnaissance using a fleet of mutually cooperating unmanned aerial vehicles to increase the effectiveness of the task. The model deploys a number of waypoints such that, when every waypoint is visited by any vehicle in the fleet, the area of interest is fully explored. The deployment of waypoints must meet the conditions arising from the technical parameters of the sensory systems used and tactical requirements of the task at hand. This paper proposes an improvement of the model by optimizing the number and position of waypoints deployed in the area of interest, the effect of which is to improve the trajectories of individual unmanned systems, and thus increase the efficiency of the operation. To achieve this optimization, a modified simulated annealing algorithm is proposed. The improvement of the model is verified by several experiments. Two sets of benchmark problems were designed: (a) benchmark problems for verifying the proposed algorithm for optimizing waypoints, and (b) benchmark problems based on typical reconnaissance scenarios in the real environment to prove the increased effectiveness of the reconnaissance operation. Moreover, an experiment in the SteelBeast simulation system was also conducted.

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“…A new odor concentration calculation function was designed in conjunction with the constraint satisfiability problem, which accelerates the problem’s convergence rate. Chen et al [ 19 ] presented a modified multi-objective symbiotic organism search algorithm for solving the reconnaissance task assignment problem for multiple UAVs with different sensor capacities. Chen et al [ 20 ] presented a modified two-part wolf pack search algorithm to solve the multi-UAV cooperative task assignment problem.…”

Section: Introductionmentioning

confidence: 99%

Solving the Multi-Functional Heterogeneous UAV Cooperative Mission Planning Problem Using Multi-Swarm Fruit Fly Optimization Algorithm

Luo¹,

Zheng

Guo

2020

Sensors

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The complexity of unmanned aerial vehicle (UAV) missions is increasing with the rapid development of UAV technology. Multiple UAVs usually cooperate in the form of teams to improve the efficiency of mission execution. The UAVs are equipped with multiple sensors with complementary functions to adapt to the complex mission constraints. Reasonable task assignment, task scheduling, and UAV trajectory planning are the prerequisites for efficient cooperation of multi-functional heterogeneous UAVs. In this paper, a multi-swarm fruit fly optimization algorithm (MFOA) with dual strategy switching is proposed to solve the multi-functional heterogeneous UAV cooperative mission planning problem with the criterion of simultaneously minimizing the makespan and the total mission time. First, the multi-swarm mechanism is introduced to enhance the global search capability of the fruit fly optimization algorithm. Second, in the smell-based search phase, the local search strategies and large-scale search strategies are designed to drive multiple fruit fly swarms, and the dual strategy switching method is presented. Third, in the vision-based search stage, the greedy selection strategy is adopted. Finally, numerical simulation experiments are designed. The simulation results show that the MFOA algorithm is more effective and stable for solving the multi-functional heterogeneous UAV cooperative mission planning problem compared with other algorithms.

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Multi-UAV Reconnaissance Task Assignment for Heterogeneous Targets Based on Modified Symbiotic Organisms Search Algorithm

Cited by 41 publications

References 31 publications

A Damage-Tolerant Task Assignment Algorithm for UAV Swarm in Confrontational Environments

A Damage-Tolerant Task Assignment Algorithm for UAV Swarm in Confrontational Environments

Trajectory Optimization in a Cooperative Aerial Reconnaissance Model

Solving the Multi-Functional Heterogeneous UAV Cooperative Mission Planning Problem Using Multi-Swarm Fruit Fly Optimization Algorithm

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