A Two-Layer Task Assignment Algorithm for UAV Swarm Based on Feature Weight Clustering

For the large-scale operations of unmanned aerial vehicle (UAV) swarm and the large number of UAVs, this paper proposes a two-layer task and resource assignment algorithm based on feature weight clustering. According to the numbers and types of task resources of each UAV and the distances between different UAVs, the UAV swarm is divided into multiple UAV clusters, and the large-scale allocation problem is transformed into several related small-scale problems. A two-layer task assignment algorithm based on the consensus-based bundle algorithm (CBBA) is proposed, and this algorithm uses different consensus rules between clusters and within clusters, which ensures that the UAV swarm gets a conflict-free task assignment solution in real time. The simulation results show that the algorithm can assign tasks effectively and efficiently when the number of UAVs and targets is large.

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Toward delay-tolerant multiple-unmanned aerial vehicle scheduling system using Multi-strategy Coevolution algorithm

Advances in Mechanical Engineering ◽

10.1177/1687814018815235 ◽

2018 ◽

Vol 10 (12) ◽

pp. 168781401881523 ◽

Cited By ~ 1

Author(s):

Yohanes Khosiawan ◽

Sebastian Scherer ◽

Izabela Nielsen

Keyword(s):

Particle Swarm Optimization ◽

Differential Evolution ◽

Unmanned Aerial Vehicle ◽

Task Assignment ◽

Computation Time ◽

Task Sequence ◽

Swarm Optimization ◽

Assignment Algorithm ◽

Aerial Vehicle ◽

Delay Tolerant

Autonomous bridge inspection operations using unmanned aerial vehicles take multiple task assignments and constraints into account. To efficiently execute the operations, a schedule is required. Generating a cost optimum schedule of multiple-unmanned aerial vehicle operations is known to be Non-deterministic Polynomial-time (NP)-hard. This study approaches such a problem with heuristic-based algorithms to get a high-quality feasible solution in a short computation time. A constructive heuristic called Retractable Chain Task Assignment algorithm is presented to build an evaluable schedule from a task sequence. The task sequence representation is used during the search to perform seamless operations. Retractable Chain Task Assignment algorithm calculates and incorporates slack time to the schedule according to the properties of the task. The slack time acts as a cushion which makes the schedule delay-tolerant. This algorithm is incorporated with a metaheuristic algorithm called Multi-strategy Coevolution to search the solution space. The proposed algorithm is verified through numerical simulations, which take inputs from real flight test data. The obtained solutions are evaluated based on the makespan, battery consumption, computation time, and the robustness level of the schedules. The performance of Multi-strategy Coevolution is compared to Differential Evolution, Particle Swarm Optimization, and Differential Evolution–Fused Particle Swarm Optimization. The simulation results show that Multi-strategy Coevolution gives better objective values than the other algorithms.

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Hierarchical Task Assignment Strategy for Heterogeneous Multi-UAV System in Large-Scale Search and Rescue Scenarios

International Journal of Aerospace Engineering ◽

10.1155/2021/7353697 ◽

2021 ◽

Vol 2021 ◽

pp. 1-19

Author(s):

Jie Chen ◽

Kai Xiao ◽

Kai You ◽

Xianguo Qing ◽

Fang Ye ◽

...

Keyword(s):

Large Scale ◽

Cluster Head ◽

Task Assignment ◽

Search And Rescue ◽

Small Scale ◽

Assignment Strategy ◽

Negotiation Mechanism ◽

Decoupling Algorithm ◽

Task Assignment Problem ◽

Multi Uav

For the large-scale search and rescue (S&R) scenarios, the centralized and distributed multi-UAV multitask assignment algorithms for multi-UAV systems have the problems of heavy computational load and massive communication burden, which make it hard to guarantee the effectiveness and convergence speed of their task assignment results. To address this issue, this paper proposes a hierarchical task assignment strategy. Firstly, a model decoupling algorithm based on density clustering and negotiation mechanism is raised to decompose the large-scale task assignment problem into several nonintersection and complete small-scale task assignment problems, which effectively reduces the required computational amount and communication cost. Then, a cluster head selection method based on multiattribute decision is put forward to select the cluster head for each UAV team. These cluster heads will communicate with the central control station about the latest assignment information to guarantee the completion of S&R mission. At last, considering that a few targets cannot be effectively allocated due to UAVs’ limited and unbalanced resources, an auction-based task sharing scheme among UAV teams is presented to guarantee the mission coverage of the multi-UAV system. Simulation results and analyses comprehensively verify the feasibility and effectiveness of the proposed hierarchical task assignment strategy in large-scale S&R scenarios with dispersed clustering targets.

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

International Journal of Aerospace Engineering ◽

10.1155/2020/8878136 ◽

2020 ◽

Vol 2020 ◽

pp. 1-15

Author(s):

Chao Chen ◽

Weidong Bao ◽

Tong Men ◽

Wen Zhou ◽

Daqian Liu ◽

...

Keyword(s):

Unmanned Aerial Vehicles ◽

Success Probability ◽

Task Assignment ◽

Human Beings ◽

Assignment Algorithm ◽

Uav Swarm ◽

Task Success ◽

Damage Tolerant ◽

First Time ◽

Reliability And Availability

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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Multi-UAVs task assignment method considering expected destruction probability of target

Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University ◽

10.1051/jnwpu/20213930617 ◽

2021 ◽

Vol 39 (3) ◽

pp. 617-623

Author(s):

Qian Zhou ◽

Shesheng Gao ◽

Zhaohui Gao ◽

Juan Xia ◽

Genyuan Hong

Keyword(s):

Economic Performance ◽

Task Allocation ◽

Task Assignment ◽

Adjustment Factor ◽

Marginal Return ◽

Assignment Method ◽

Proposed Model ◽

Balanced Distribution ◽

Aerial Vehicle ◽

Simulation Results

To solve the combat task assignment of reconnaissance unmanned aerial vehicle (RUAV)/unmanned combat aerial vehicle(UCAV), this paper proposed an efficient task assignment method that takes into account the expected destruction probability of target. This method improves the utility function and constraint of the model that based on the goal of destroying the total sum of the target value. The adjustment factor is added to the model to achieve a balanced distribution of RUAVs/UCAVs resources; the expected destruction probability of target is introduced as a constraint to prevent the excessive distribution of RUAVs/UCAVs resources. Subsequently, a greedy algorithm based on maximizing marginal-return is designed to solve the proposed model. The simulation results show that the improved algorithm not only meets the combat effectiveness but also improves the economic performance on the basis of real-time task allocation.

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A Center-Rule-Based Neighborhood Search Algorithm for Roadside Units Deployment in Emergency Scenarios

Mathematics ◽

10.3390/math8101734 ◽

2020 ◽

Vol 8 (10) ◽

pp. 1734

Author(s):

Yanjun Shi ◽

Lingling Lv ◽

Hao Yu ◽

Liangjie Yu ◽

Zihui Zhang

Keyword(s):

Urban Areas ◽

Large Scale ◽

Search Algorithm ◽

Neighborhood Search ◽

Small Scale ◽

Rule Based ◽

Smart Transportation ◽

Simulation Results ◽

Emergency Scenarios ◽

Neighborhood Search Algorithm

Roadside Units Deployment (RSUD) is of great importance to smart transportation with the Internet of Things (IoT). It is believed to be not feasible for RSUD to cover and perceive the whole area due to the high installation and maintenance costs. The candidate locations set of RSUD may be huge for a future urban area with vehicle-to-everything (V2X) networks. Most of the previous studies tried to maximize the Roadside Units (RSU) coverage only and made few reports on emergency scenarios, such as accidents happening. We tried to find better candidate locations of RSUD in some grid road networks with equal length streets, and then chose some of these locations for final installation with a given budget to minimize the average reporting time of emergency messages in V2X networks. Firstly, we analyzed candidate locations of RSUD for different cases of RSUs and vehicles. Then we proposed a message dissemination model for RSUD with the V2X network, and a center-rule-based neighborhood search algorithm (CNSA for short). In this algorithm, we generated initial solutions with the center rule and then obtained better neighbor solutions. Numerical simulation results from small-scale urban streets showed that the proposed algorithm performs well on execution time. Simulation results with Veins and Simulation of Urban Mobility) (SUMO) verified the proposed model and CNSA for evaluating the RSUD scheme by distance instead of accident reporting time in urban areas with large-scale traffic flow.

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A Grasshopper Optimization-Based Approach for Task Assignment in Cloud Logistics

Mathematical Problems in Engineering ◽

10.1155/2020/3298460 ◽

2020 ◽

Vol 2020 ◽

pp. 1-10 ◽

Cited By ~ 1

Author(s):

Lan Xu ◽

Yiliu Tu ◽

Yuting Zhang

Keyword(s):

Large Scale ◽

Programming Model ◽

Task Assignment ◽

Operational Mode ◽

Assignment Model ◽

Simulation Results ◽

Grasshopper Optimization ◽

Integrated Logistics ◽

And Task ◽

Biobjective Programming

A framework for the algorithm-based CL platform is established, based on which, the operational mode of it is described in detail. An integrated logistics task assignment model is built to optimally match logistics service resources and task of large scale in the algorithm-based CL. Particularly, an improved grasshopper optimization-based bitarget optimization algorithm (GROBO) is proposed to solve the biobjective programming model for service matching in CL. The case of Linyi small commodity logistics is taken as an application. Simulation results show that the proposed GROBO provides better solutions regarding to searching efficiency and stability in solving the model.

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A MODIFIED A* ALGORITHM FOR ALLOCATING TASK IN HETEROGENEOUS DISTIRBUTED COMPUTING SYSTEMS

International Journal of Computing ◽

10.47839/ijc.8.2.666 ◽

2014 ◽

pp. 50-57

Author(s):

Nirmeen A. Bahnasawy ◽

Gamal M. Attiya ◽

Mervat Mosa ◽

Magdy A. Koutb

Keyword(s):

Large Scale ◽

Search Algorithm ◽

Optimal Solution ◽

Task Assignment ◽

Turnaround Time ◽

Sequential Search ◽

A Algorithm ◽

Computing Systems ◽

Parallel Application ◽

Assignment Algorithm

Distributed computing can be used to solve large scale scientific and engineering problems. A parallel application could be divided into a number of tasks and executed concurrently on different computers in the system. This paper provides an optimal task assignment algorithm under memory constraints to minimize required time of finishing a parallel application. The proposed algorithm is based on the optimal assignment sequential search (OASS) of the A* algorithm with additional modifications. This modified algorithm yields optimal solution, lower time complexity, reduces the turnaround time of the application and considerably faster compared with the sequential search algorithm.

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Task Assignment of UAV Swarm Based on Wolf Pack Algorithm

Applied Sciences ◽

10.3390/app10238335 ◽

2020 ◽

Vol 10 (23) ◽

pp. 8335

Author(s):

Yingtong Lu ◽

Yaofei Ma ◽

Jiangyun Wang ◽

Liang Han

Keyword(s):

Genetic Algorithm ◽

Dimensional Space ◽

Task Assignment ◽

High Dimensional ◽

Swarm Optimization ◽

Uav Swarm ◽

Aerial Vehicle ◽

Key Issues ◽

Comparative Results ◽

Search Quality

To perform air missions with an unmanned aerial vehicle (UAV) swarm is a significant trend in warfare. The task assignment among the UAV swarm is one of the key issues in such missions. This paper proposes PSO-GA-DWPA (discrete wolf pack algorithm with the principles of particle swarm optimization and genetic algorithm) to solve the task assignment of a UAV swarm with fast convergence speed. The PSO-GA-DWPA is confirmed with three different ground-attack scenarios by experiments. The comparative results show that the improved algorithm not only converges faster than the original WPA and PSO, but it also exhibits excellent search quality in high-dimensional space.

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