Multi-UAV Task Allocation Research Articles

Multi-UAVs task allocation method based on MPSO-SA-DQN

Multi-UAVs play an important role in the battlefield. Although many methods are proposed to solve the Multi-UAV task allocation, there still existing the problems of complex time constraints and uncertain solution space. The reason is that multi-UAVs usually face changing environmental factors. Aiming at solving such problem, this paper proposes a multi-UAV task assignment method based on Deep Q-based evolutionary reinforcement learning algorithms (MPSO-SA-DQN). Specifically, this method builds a multi-agent training framework based on the deep evolutionary reinforcement learning mechanism and SA-DQN. Its aim is to improve the global exploration and optimization capabilities of multi-agents. At the same time, the multi-dimensional particle swarm optimization algorithm is introduced to optimize the state space. Based on task priority mapping, the MPSO-SA-DQN algorithm framework is proposed. As a result, multi-agents can optimize the execution state in real time in the environment interaction. Besides, it also has the ability to reach optimal state and maximum reward. According to the characteristics of multi-UAV global task assignment, this paper designs a priority state space autoencoder strategy and global task feature. A multi-UAVs tasks allocation and iterative optimization method based on MPSO-SA-DQN algorithm is proposed, so as to continuously optimize the task allocation scheme. The simulation results show that the multi-UAV task allocation method based on MPSO-SA-DQN can effectively solve the problem of uncertainty in the optimal solution space of task allocation. At the same time, the algorithm achieves faster convergence result, and a good prospect of promotion in the field of UAV swarm cooperative task planning.

A Review of Multi-UAV Task Allocation Algorithms for a Search and Rescue Scenario

Unmanned aerial vehicles (UAVs) play a crucial role in enhancing search and rescue (SAR) operations by accessing inaccessible areas, accomplishing challenging tasks, and providing real-time monitoring and modeling in situations where human presence is unsafe. Multi-UAVs can collaborate more efficiently and cost-effectively than a single large UAV for performing SAR operations. In multi-UAV systems, task allocation (TA) is a critical and complex process involving cooperative decision making and control to minimize the time and energy consumption of UAVs for task completion. This paper offers an exhaustive review of both static and dynamic TA algorithms, confidently assessing their strengths, weaknesses, and limitations. It provides valuable insights into addressing research questions related to specific UAV operations in SAR. The paper rigorously discusses outstanding issues and challenges and confidently presents potential directions for the future development of task assignment algorithms. Finally, it confidently highlights the challenges of multi-UAV dynamic TA methods for SAR. This work is crucial for gaining a comprehensive understanding of multi-UAV dynamic TA algorithms and confidently emphasizes critical open issues and research gaps for future SAR research and development, ensuring that readers feel informed and knowledgeable.

Reinforcement-Learning-Assisted Multi-UAV Task Allocation and Path Planning for IIoT

Reinforcement-Learning-Assisted Multi-UAV Task Allocation and Path Planning for IIoT

Multi-UAV task allocation based on GCN-inspired binary stochastic L-BFGS

Task allocation has been one of the key issues for cooperative control of multiple unmanned aerial vehicles (Multi-UAVs), which has attracted a large number of researchers to conduct research in recent years. As the number of tasks and resource types increase, the solution time of most of the existing methods increases sharply, and are difficult to be deployed in other scenarios. To deal with task allocation problems with large-scale tasks and multiple types of resources, this paper proposed a multi-UAV task allocation method based on graph convolutional network (GCN)-inspired binary stochastic L-BFGS (GBSL-BFGS) with strong generalization. First, the objectives and constraints of the task allocation problem are analyzed, while a flexible and easily scalable method for describing the task allocation problem is proposed. Then, the GBSL-BFGS task allocation method is proposed for large-scale multi-UAV cluster. By introducing GCN as a graph mapper, the L-BFGS algorithm is able to optimize the binary decision matrix in the task allocation problem. Simulation experiments demonstrated that the GBSL-BFGS optimization method has a better performance and computational efficiency compared with other methods, especially for large-scale multi-UAV task allocation problems.

Multi-criterion multi-UAV task allocation under dynamic conditions

Multi-UAV systems are essential in accomplishing specific tasks across various research applications. UAVs can be used in search and rescue (SR), which requires them to explore disaster areas and rescue survivors. A significant challenge in such a scenario is allocating tasks to UAVs to support as many survivors as possible. The allocation of tasks to a multi-UAV system is a highly intricate issue that falls under the NP-hard classification. Dealing with multiple attributes and constraints for tasks and UAVs can complicate the problem. Based on several limiting factors and distinctive characteristics, this paper proposes a compromised task allocation (TA) approach for multi-UAVs operating in SR scenarios. The proposed algorithm enhances the performance impact (PI) algorithm by incorporating compromised PI (CPI) with specific constraints. Additionally, CPI is extended to compromised dynamic PI (CDPI) to handle any new tasks that may arise during task execution to accommodate dynamic events. Simulation results demonstrate a performance improvement of 22% in task allocation and 28.57% for task prioritization as compared with the PI algorithm.

Mathematical Problems in Engineering Improved CNP-Method-Based Local Real-Time Cooperative Task Allocation of Heterogeneous Multi-UAV in Communication-Constrained Environment

UAVs are widely employed in military and civilian fields because of their inherent advantages. The cooperative task allocation of multi-UAV is more in conformity with the requirements of UAV application scenarios, which has become a hot research topic. However, resource consumption allocation and the application scenarios of communication constraints are often ignored. This paper proposes a distributed multi-UAV task allocation method based on improved CNP to solve the local cooperative task allocation problem of heterogeneous multi-UAV in the communication-constrained environment. The improved CNP-based method can be divided into four stages: task release, bid application, coalition formation, and signing contracts. In the task release stage, we proposed the adaptive maximum number setting method of information transfer times and the information consistency method to solve conflicts in the local communication network. In the process of coalition formation, the resource consumption allocation algorithm based on the Gini coefficient is proposed to keep the resource difference between UAVs in the coalition within a reasonable range. The simulation results demonstrate that improved CNP-method-based cooperative task allocation can handle the local real-time task allocation problem of heterogeneous multi-UAV under communication constraints; it obtains greater task rewards and spends less time on task completion than the resource-welfare-based method, PTCFA. Simultaneously, the resource consumption algorithm makes the UAV swarm maintain a more reasonable resource difference to maximize the number of missions completed.

Vehicle-Assisted UAV Delivery Scheme Considering Energy Consumption for Instant Delivery.

Unmanned aerial vehicles (UAVs) are increasingly used in instant delivery scenarios. The combined delivery of vehicles and UAVs has many advantages compared to their respective separate delivery, which can greatly improve delivery efficiency. Although a few studies in the literature have explored the issue of vehicle-assisted UAV delivery, we did not find any studies on the scenario of an UAV serving several customers. This study aims to design a new vehicle-assisted UAV delivery solution that allows UAVs to serve multiple customers in a single take-off and takes energy consumption into account. A multi-UAV task allocation model and a vehicle path planning model were established to determine the task allocation of the UAVs as well as the path of UAVs and the vehicle, respectively. The model also considered the impact of changing the payload of the UAV on energy consumption, bringing the results closer to reality. Finally, a hybrid heuristic algorithm based on an improved K-means algorithm and ant colony optimization (ACO) was proposed to solve the problem, and the effectiveness of the scheme was proven by multi-scale experimental instances and comparative experiments.

A multi- unmanned aerial vehicle dynamic task assignment method based on bionic algorithms

Because of the long calculation time in task allocation algorithms and the long travelled distance of the Unmanned Aerial Vehicle (UAV) during task allocation, a multi-UAV task allocation method based on three bionic algorithms, which are the ant colony algorithm, bat algorithm, and gray wolf algorithm was proposed in this paper. The Multi-UAV Dynamic Task Assignment Method Based on Bionic Algorithms (TABA) method dynamically allocates UAVs based on the number of task points and includes a comparison mechanism. The results of experiments demonstrate that, among the three algorithms, the proposed method has 30% improvement to the conventional methods, and the UAV travel time is about one-third that of the original algorithm on average, thereby revealing the effectively reduced algorithm complexity. Moreover, the algorithm can reduce the travel time of UAVs.

Cooperative Multi-UAV Conflict Avoidance Planning in a Complex Urban Environment

Trajectory planning is of great value and yet challenging for multirotor unmanned aerial vehicle (UAV) applications in a complex urban environment, mainly due to the complexities of handling cluttered obstacles. The problem further complicates itself in the context of autonomous multi-UAV trajectory planning considering conflict avoidance for future city applications. To tackle this problem, this paper introduces the multi-UAV cooperative trajectory planning (MCTP) problem, and proposes a bilevel model for the problem. The upper level is modeled as an extended multiple traveling salesman problem, aiming at generating trajectories based on heuristic framework for multi-UAV task allocation and scheduling and meanwhile considering UAV kinodynamic properties. The lower level is modeled as a holding time assignment problem to avoid possible spatiotemporal trajectory conflicts, where conflict time difference is analyzed based on the proposed state-time graph method. Numerical studies are conducted in both a 1 km2 virtual city and 12 km2 real city with a set of tasks and obstacles settings. The results show that the proposed model is capable of planning trajectories for multi-UAV from the system-level perspective based on the proposed method.

MULTI-UAV Task Allocation Based on Improved Genetic Algorithm

The path length of multiple unmanned aerial vehicle (multi-UAV) has a certain impact on the task allocation of multi-UAV. In order to improve the efficiency of multi-UAV and reduce the loss of multi-UAV during the process of performing tasks, this paper takes the path length as one of the influencing factors of the evaluation function. The UAV path length, UAV performance, and task characteristics are taken as the influencing factors of multi-UAV task allocation evaluation function. In addition, in order to improve the efficiency of genetic algorithm (GA) in solving multi-UAV task allocation problem, this paper proposes a fusion genetic algorithm based on improved simulated annealing (ISAFGA). In order to improve the population diversity of GA, the second selection operation of GA is carried out and the improved simulated annealing algorithm (SA) is used in the second selection operation. The threshold is set to improve the acceptance criteria of new solutions in SA, and then the promotion of secondary selection operation on population diversity is improved. The simulation results showed that the improved algorithm could improve the diversity of the population and improve the global search ability, and verified the effectiveness of the improved algorithm.

UAV Task Allocation Based on Clone Selection Algorithm

With the continuous development of computer and network technology, the large‐scale and clustered operations of drones have gradually become a reality. How to realize the reasonable allocation of UAV cluster combat tasks and realize the intelligent optimization control of UAV cluster is one of the most challenging difficulties in UAV cluster combat. Solving the task allocation problem and finding the optimal solution have been proven to be an NP‐hard problem. This paper proposes a CSA‐based approach to simultaneously optimize four objectives in multi‐UAV task allocation, i.e., maximizing the number of successfully allocated tasks, maximizing the benefits of executing tasks, minimizing resource costs, and minimizing time costs. Experimental results show that, compared with the genetic algorithm, the proposed method has better performance on solving the UAV task allocation problem with multiple objectives.

Fish-Inspired Task Allocation Algorithm for Multiple Unmanned Aerial Vehicles in Search and Rescue Missions

The challenge concerning the optimal allocation of tasks across multiple unmanned aerial vehicles (multi-UAVs) has significantly spurred research interest due to its contribution to the success of various fleet missions. This challenge becomes more complex in time-constrained missions, particularly if they are conducted in hostile environments, such as search and rescue (SAR) missions. In this study, a novel fish-inspired algorithm for multi-UAV missions (FIAM) for task allocation is proposed, which was inspired by the adaptive schooling and foraging behaviors of fish. FIAM shows that UAVs in an SAR mission can be similarly programmed to aggregate in groups to swiftly survey disaster areas and rescue-discovered survivors. FIAM’s performance was compared with three long-standing multi-UAV task allocation (MUTA) paradigms, namely, opportunistic task allocation scheme (OTA), auction-based scheme, and ant-colony optimization (ACO). Furthermore, the proposed algorithm was also compared with the recently proposed locust-inspired algorithm for MUTA problem (LIAM). The experimental results demonstrated FIAM’s abilities to maintain a steady running time and a decreasing mean rescue time with a substantially increasing percentage of rescued survivors. For instance, FIAM successfully rescued 100% of the survivors with merely 16 UAVs, for scenarios of no more than eight survivors, whereas LIAM, Auction, ACO and OTA rescued a maximum of 75%, 50%, 35% and 35%, respectively, for the same scenarios. This superiority of FIAM performance was maintained under a different fleet size and number of survivors, demonstrating the approach’s flexibility and scalability.

A Bid-Based Grouping Method for Communication-Efficient Decentralized Multi-UAV Task Allocation

This paper presents an extension to a decentralized multi-UAV task allocation algorithm, consensus-based bundle algorithm (CBBA), to improve the communication efficiency in the plan consensus process. The presented algorithm termed grouped consensus-based bundle algorithm (G-CBBA) provides a systematic way of grouping the UAVs based on their task preference represented by the initial guess created by the UAVs. G-CBBA features a nested iteration between two layers of plan consensus: local and global. The local stage aims at plan consensus over the list of tasks shared within a group, while the global stage ensures agreement across the entire network over the whole task set. The proposed two-layer scheme can improve the communication efficiency by reducing the need for propagating irrelevant bids while preserving robust convergence property of CBBA. Numerical experiments demonstrate reduction in the number of messages to be communicated without incurring substantial performance degradation compared to the baseline CBBA.

Adaptive task allocation for multi-UAV systems based on bacteria foraging behaviour

The foraging behaviour of bacteria in colonies exhibits motility patterns that are simple and reasoned by stimuli. Notwithstanding its simplicity, bacteria behaviour demonstrates a level of intelligence that can feasibly inspire the creation of solutions to address numerous optimisation problems. One such challenge is the optimal allocation of tasks across multiple unmanned aerial vehicles (multi-UAVs) to perform cooperative tasks for future autonomous systems. In light of this, this paper proposes a bacteria-inspired heuristic for the efficient distribution of tasks amongst deployed UAVs. The usage of multi-UAVs is a promising concept to combat the spread of the red palm weevil (RPW) in palm plantations. For that purpose, the proposed bacteria-inspired heuristic was utilised to resolve the multi-UAV task allocation problem when combating RPW infestation. The performance of the proposed algorithm was benchmarked in simulated detect-and-treat missions against three long-standing multi-UAV task allocation strategies, namely opportunistic task allocation, auction-based scheme, and the max-sum algorithm, and a recently introduced locust-inspired algorithm for the allocation of multi-UAVs. The experimental results demonstrated the superior performance of the proposed algorithm, as it substantially improved the net throughput and maintained a steady runtime performance under different scales of fleet sizes and number of infestations, thereby expressing the high flexibility, scalability, and sustainability of the proposed bacteria-inspired approach.

Secure Multi-UAV Collaborative Task Allocation

Unmanned aerial vehicle technology has made great progress in the past and is widely used in many fields. However, they are unable to meet large-scale and complex missions with a limited energy reserve. Only multiple unmanned aerial vehicles (multi-UAV) work together to better cope with this problem and have been extensively studied. In this paper, a new systematic framework is proposed to solve the problem of multi-UAV collaborative task allocation. It is formulated as a combinatorial optimization problem and solved by the improved clustering algorithm. The purpose is to enable multi-UAV to complete tasks with lower energy consumption. As the number of UAVs rises, it also appears the flight safety issues such as collisions among the UAVs, an improved multi-UAV collision-resistant method based on the improved artificial potential field is proposed. Besides, the UAVs connected with the internet are vulnerable to the various type of network attacks, a method based on the intrusion detection system is proposed to resist the network attack during multi-UAV mission execution. We have also proposed an improved method to improve the accuracy of task allocation further. In addition, an online real-time path planning is proposed to enhance the robustness of multi-UAV to cope with sudden problems. Finally, the numerical simulations and real physical flying experiments showed that the proposed method could provide a viable solution for multi-UAV task allocation; moreover, compared with other task allocation methods, our method has great performance.

A Multi-UAV Task Allocation Algorithm Combatting Red Palm Weevil Infestation

The spread of red palm weevil (RPW) infestation of palm trees is prevalent in many countries, causing tremendous economic losses estimated at multi-millions of dollars annually. The utilization of a swarm of multiple unmanned aerial vehicles (UAVs) with suitable equipment has the potential to support RPW detect and treat (DAT) missions. Nevertheless, this approach raises a challenge regarding the efficient distribution of UAVs during search and detect tasks within the constraints of a mission. This paper proposes a new autonomous bio-inspired approach for efficiently allocating tasks among multiple UAVs during DAT missions. The new approach is inspired by the autonomous behaviour of bacteria as they forage for food. The performance of the proposed algorithm was benchmarked against two long-standing multi-UAV task allocation paradigms: opportunistic task allocation and auction-based heuristics, which were thoroughly tested in simulated DAT mission scenarios to comparatively assess their performance. The experimental results demonstrated the superior performance of the proposed algorithm, as it detected more infestations at shorter runtimes. These results validate the high flexibility, scalability, and sustainability of the proposed approach.