Communication-constrained multi-UAV task allocation method for non-independent tasks

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.

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

Unmanned aerial vehicles (UAVs), commonly known as drones, offer unprecedented flexibility for complex missions such as area surveillance, search and rescue, and cooperative inspection. This paper presents a unified evaluation framework for the comparison of centralized and distributed task allocation algorithms specifically tailored to multi-UAV operations. We first contextualize the classical assignment problem (AP) under UAV mission constraints, including the flight time, propulsion energy capacity, and communication range, and evaluate optimal one-to-one solvers including the Hungarian algorithm, the Bertsekas ϵ-auction algorithm, and a minimum cost maximum flow formulation. To reflect the dynamic, uncertain environments that UAV fleets encounter, we extend our analysis to distributed multi-UAV task allocation (MUTA) methods. In particular, we examine the consensus-based bundle algorithm (CBBA) and a distributed auction 2-opt refinement strategy, both of which iteratively negotiate task bundles across UAVs to accommodate real-time task arrivals and intermittent connectivity. Finally, we outline how reinforcement learning (RL) can be incorporated to learn adaptive policies that balance energy efficiency and mission success under varying wind conditions and obstacle fields. Through simulations incorporating UAV-specific cost models and communication topologies, we assess each algorithm’s mission completion time, total energy expenditure, communication overhead, and resilience to UAV failures. Our results highlight the trade-off between strict optimality, which is suitable for small fleets in static scenarios, and scalable, robust coordination, necessary for large, dynamic multi-UAV deployments.

Dynamical mobile task allocation, by which tasks can move randomly before they are assigned robots to execute. For such a new task assignment domain, we propose a hybrid dynamic mobile task allocation and reallocation method that combines our previous proposed dynamical sequential method and global optimal method. Robots bid for tasks and transmit the costs to other robots. Then all robots select tasks from the combinatorial cost table to minimize the objective function. During the next time step, robots continue to select the assigned tasks for which costs are smaller than the set thresholds. Alternatively, robots for which costs exceed the corresponding threshold rebid unassigned tasks and transmit the calculated costs to others. The un-selected robots then re-select unassigned tasks from the combinatorial cost table according to global optimal task allocation method. In this study, the advantages of the proposed approach are demonstrated by comparison with existing task allocation methods. The simulation results demonstrate that a system implementing our method can obtain maximal accomplished efficiency of whole system and minimal executed costs for each individual robot. The negotiation time steps, communication costs and computational times are reduced using the proposed algorithm. Moreover, we believe that our method can extend the previous methods to be suitable for a large-scale distributed multi-robot coordination system.

SummaryThis paper addresses the task allocation problem in an open, dynamic grid environments and service‐oriented environments. In such environments, both grid/service providers and consumers can be modelled as intelligent agents. These agents can leave and enter the environment freely at any time. Task allocation under time constraints becomes a challenging issue in such environments because it is difficult to apply a central controller during the allocation process due to the openness and dynamism of the environments. This paper proposes a negotiation‐based method for task allocation under time constraints in an open, dynamic grid environment, where both consumer and provider agents can freely enter or leave the environment. In this method, there is no central controller available, and agents negotiate with each other for task allocation based only on local views. The experimental results show that the proposed method can outperform the current methods in terms of the success rate of task allocation and the total profit obtained from the allocated tasks by agents under different time constraints. Copyright © 2014 John Wiley & Sons, Ltd.

With the development of the technology of unmanned aerial vehicle (UAV), the multi-UAV task allocation has become a hot topic in recent years. Recently, many classical intelligent optimization algorithms have been applied to this problem, because the multi-UAV task allocation problem can be formalized as a NP-hard issue. However, most research treat this problem as a single objective optimization problem. In view of this situation, we use an improved algorithm of multi-objective particle swarm optimization (MOPSO) to solve the task allocation problem of multiple UAVs. We will take two stages of SMC resampling to improve the disadvantages in the MOPSO algorithm. In the first stage, resampling is used to improve the slow convergence of the particle swarm optimization in the middle and late stages. In the second stage, resampling is used to expand the search area of the particle swarm optimization algorithm and to prevent the algorithm from falling into the local optimal solution. The simulation results show that the improved algorithm has a good performance in solving the task allocation problem of multiple UAVs.

Task allocation in edge computing refers to the process of distributing tasks among the various nodes in an edge computing network. The main challenges in task allocation include determining the optimal location for each task based on the requirements such as processing power, storage, and network bandwidth, and adapting to the dynamic nature of the network. Different approaches for task allocation include centralized, decentralized, hybrid, and machine learning algorithms. Each approach has its strengths and weaknesses and the choice of approach will depend on the specific requirements of the application. In more detail, the selection of the most optimal task allocation methods depends on the edge computing architecture and configuration type, like mobile edge computing (MEC), cloud-edge, fog computing, peer-to-peer edge computing, etc. Thus, task allocation in edge computing is a complex, diverse, and challenging problem that requires a balance of trade-offs between multiple conflicting objectives such as energy efficiency, data privacy, security, latency, and quality of service (QoS). Recently, an increased number of research studies have emerged regarding the performance evaluation and optimization of task allocation on edge devices. While several survey articles have described the current state-of-the-art task allocation methods, this work focuses on comparing and contrasting different task allocation methods, optimization algorithms, as well as the network types that are most frequently used in edge computing systems.

To realize reasonable design task modeling and high efficiency task collaboration and allocation in complicated design process, a design task model based granular space is proposed, and cross-stage collaboration mode and task allocation method are discussed. Firstly, combining study results of Collaboration design and Cloud Manufacturing (CMfg), four dimension granular space of design task is defined and a general design task model is built. Secondly, cross-stage collaboration mode based task in distributed network environment is proposed. And then, design task allocation methods on both cross-stage synergy and single stage synergy are introduced. Finally, application prototype system is illustrated. Application results show that deign task model and task allocation in the multi-task collaboration mode are feasible. Keywords-Granular Space;Design Task Modeling;Virtual Design Unit (VDU); Cross-stage Collaboration;Task Allocation

A method of task allocation and automated negotiation for multi robots was proposed. Firstly, the principles of task allocation were described based on the real capability of robot. Secondly, the model of automated negotiation was constructed, in which Least-Squares Support Vector Regression (LSSVR) was improved to estimate the opponent’s negotiation utility and the robust controller of H ∞ output feedback was employed to optimize the utility performance indicators. Thirdly, the protocol of negotiation and reallocation was proposed to improve the real-time capability and task allocation. Finally, the validity of method was proved through experiments.

Task allocation and resource scheduling capability are important indicators for evaluating cloud environment. Aiming at the problems of low resource utilization, high algorithm time complexity and low task allocation efficiency of existing task allocation strategies, a task allocation and resource scheduling method based on dynamic programming in cloud environment is proposed. Using the idea of dynamic programming, this method regards the matching of tasks and servers as a combination of multi-stage decision-making, and obtains the optimization scheme of task allocation, which reduces the completion time of tasks. The experimental results show that the proposed method can reduce the task completion time and the resource load is relatively balanced, which can effectively improve the task execution efficiency.

This paper addresses the task allocation problem in open, dynamic grid or service-oriented environments. In such environments, both grid/service providers and consumers can be modeled as intelligent agents. These agents can leave off and enter into an open environment freely at any time. Task allocation under time constraints becomes a critical issue in such environments since it is difficult to apply a central controller during the allocation process due to the openness of environments as well as decentralized natures of agents. This paper proposes a negotiation-based method for task allocation under time constraints in an open, dynamic grid environment, where both consumer and provider agents can enter into or leave off the environment freely. In this method, there is no central controller, and agents negotiate with each other for task allocation based only on local views. The experimental results show that the proposed method can outperform state-of-art methods in terms of success rate of task allocation and total profit obtained from the allocated tasks by agents under different time constraints.

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.

When multiple UAVs carry out multiple data ferry tasks with inconsistent deadlines at the same time, aiming at the problem of large volume of tasks and limited number of UAVs, the "One Wave" collaborative task allocation mode leads to low task completion rate and low utilization rate of UAVs, a "Multi-wave" task allocation method based on task urgency is proposed. In this task allocation mode, the UAV group performs specific tasks according to the task set divided by each wave, A UAV belongs to one or more task sets. Each wave adopts the scheduling strategy of FCFS (First Come First Service) to reuse UAVs. UAV groups cooperate in the same wave, while independent of each other between different waves. By determining the UAV scheduling strategy and iterating different wave intervals, in order to improve the task completion result, an improved genetic algorithm based on UAV multiplexing is used to solve the problem. The simulation results show that compared with the traditional "One-Wave" task allocation method, the proposed "Multi-Wave" task allocation method RTDA (Reused Task Distribution Algorithm) improves the completion of tasks by 112% and the average utilization of UAVs by 31.32% under the optimal wave interval.

SummaryInternet of Things (IoT) is utilized as an emerging sample for defining the future of technology in which physical items like sensors, radio‐frequency identification tags, mobile phones, actuators, and so on, can have interaction together and have cooperation with their neighbors for obtaining joint objectives. The performance of the deployed tasks and applications on the network is considered as one of the critical goals in this model, which is achieved by the task allocation mechanism. Task allocation in the IoT is so complicated due to the intricate connection among machines. The task allocation problem is considered as an NP‐hard problem, so a new task allocation algorithm in the IoT environment is proposed using the combination of Simulated Annealing (SA) and Particle Swarm Optimization (PSO) algorithm. Also, the issues of the PSO algorithm, such as getting stuck in local optimization and not achieving an optimal response, forced us to present a method based on the combination of SA and the PSO algorithms. The results of simulation in MATLAB environment illustrated that the suggested method performs better compared to the PSO and SA‐based methods.

At present, as a method of solving large-scale complex optimization problems, bionic algorithms are widely used in multi-UAV task allocation. Bat algorithm, as a kind of bionic algorithm, has problems such as long flying distance of UAV in the process of task allocation. In this paper, an improved bat algorithm for multi-UAV task allocation method is proposed. On the basis of the bat algorithm, the comparison of the number of drones and target points and the calculation of the flight distance of the drone are added. The experimental results show that the improved algorithm can reduce the calculation time and the flight time of the UAV, shorten the flight distance of the UAV, and improve the efficiency of the algorithm.KeywordsMulti-UAVsTask allocationBat algorithmFlight distanceCalculation time