Task Assignment Of Unmanned Aerial Vehicles Research Articles

Priority-aware task assignment and path planning for efficient and load-balanced multi-UAV operation

Unmanned aerial vehicles (UAVs) have emerged as a viable option in numerous real-world applications. In large mission planning systems, demanding the effective completion of a wide variety of tasks, multi-UAV systems are significantly preferred. Collaborative task assignment and path planning have a significant impact on autonomous operation of multi-UAV systems. In this study, we propose a priority-aware task assignment and path planning (AMTP) algorithm based on actor-critic multi-agent reinforcement learning for multi-UAV operations. We utilize an actor-critic multi-agent framework based on centralized training and decentralized execution for task assignment and path planning of heterogeneous UAVs. A multi-agent deep deterministic policy gradient framework is used to train the system to deal with both the elements of the dynamic environment and the heterogeneity of UAVs and tasks. The critic network evaluates the policy by maintaining the global network state information and combines the actions of agents to simplify the training state. On the other hand, agents decide which actions to comply with their local observations to improve the policy. The performance evaluation results obtained through simulation show that the proposed AMTP outperforms existing methods in terms of task completion rate, load balancing, energy efficiency, and convergence time.

A Deadlock-Free Hybrid Estimation of Distribution Algorithm for Cooperative Multi-UAV Task Assignment With Temporally Coupled Constraints

This study addresses the cooperative multi unmanned aerial vehicles task assignment problem (CMTAP) with temporally coupled constraints and aims to find a feasible assignment to minimize the equivalent distances of all tasks. We first present a mixed-integer linear programming model of CMTAP. To solve the undesirable deadlocks of CMTAP, a Petri net amender is constructed based on a candidate solution, and a deadlock-free solution is equivalent to a feasible transition sequence that can be fired sequentially in the corresponding amender. With this amender, we present a Petri net-based deadlock amending method (PDAM) with polynomial time complexity to convert a deadlocked solution into a deadlock-free solution. Also, a deadlock-free hybrid estimation of distribution algorithm (DHEDA) is developed for CMTAP by embedding PDAM into the original EDA. To further improve the solution quality, we establish a local exploitation method, and an adaptive operational probability is used to balance the computational burden and local exploitation ability. Then a match-up based reassignment method is proposed to cope with time-sensitive targets. Finally, extensive computational experiments demonstrate that PDAM is more effective at solving deadlocks than graph-based methods, particularly for large-scale CMTAP, and DHEDA outperforms existing algorithms when solving CMTAP.

An FW–GA Hybrid Algorithm Combined with Clustering for UAV Forest Fire Reconnaissance Task Assignment

The assignment of tasks for unmanned aerial vehicles (UAVs) during forest fire reconnaissance is a highly complex and large-scale problem. Current task allocation methods struggle to strike a balance between solution speed and effectiveness. In this paper, a two-phase centralized UAV task assignment model based on expectation maximization (EM) clustering and the multidimensional knapsack model (MKP) is proposed for the forest fire reconnaissance task assignment. The fire situation information is acquired using the sensors carried by satellites at first. Then, the EM algorithm based on the Gaussian mixture model (GMM) is applied to get the initial position of every UAV. In the end, the MKP is applied for UAV task assignment based on the initial positions of the UAVs. An improved genetic algorithm (GA) based on the fireworks algorithm (FWA) is proposed for faster iteration speed. A simulation was carried out against the background of forest fires in Liangshan Prefecture, Sichuan Province, and the simulation’s results demonstrate that the task assignment model can quickly and effectively address task allocation problems on a large scale. In addition, the FW–GA hybrid algorithm has great advantages over the traditional GA, particularly in solving time, iteration convergence speed, and solution effectiveness. It can reduce up to 556% of the iteration time and increase objective function value by 1.7% compared to the standard GA. Furthermore, compared to the GA–SA algorithm, its solving time is up to 60 times lower. This paper provides a new idea for future large-scale UAV task assignment problems.

Hybrid swarm intelligent algorithm for multi-UAV formation reconfiguration

Formation flight of unmanned aerial vehicles (UAVs) utilizes reconfiguration procedures to handle a variety of emergencies, such as collision avoidance, malfunctions, fuel savings, and member replacement. As UAVs have limited computing power and energy resources, it is necessary to optimize the control inputs to reduce the distance travelled by UAVs while reducing the computing costs during formation reconfiguration. In this paper, the problem of multi-UAV reconfiguration is decoupled into two stages: task assignment and control input optimization of UAVs. For a solution to the above problem, we propose an adaptive hybrid particle swarm optimization and differential evolution algorithm (AHPSODE) to optimize minimize the distance of the total movement and reduce the computing cost of formation reconfiguration. Based on the idea of receding horizon control (RHC) and the nonlinear model of multi-UAV formation reconfiguration, an RHC controller using AHPSODE is designed to optimize the control input of the UAV group to obtain the shortest movement distance, and this method can reduce the computation time. We use the CEC 2017 test suit to test the performance of our proposed AHPSODE algorithm, and simulate the AHPSODE-based RHC controller to manage formation reconfiguration. The results show that our proposed AHPSODE performed well in convergence and accuracy and the RHC controller is effective.

Toward flexible and persistent UAV service: Multi-period and multi-objective system design with task assignment for disaster management

Usability of lightweight unmanned aerial vehicles (UAVs) limited in a variety of activities in disaster management due to two fundamental drawbacks: limited flight duration, and limited loading capacity. With the proper system design and task assignment methodologies, these limitations can be overcome to enable long-duration UAV missions over large areas. The present study set out to determine the optimal unmanned aerial system (UAS) system design and UAV task assignment for such applications. Based on a proposed multi-objective mathematical model, exact Pareto optimal solutions were derived using the ε–constraint method. To find approximate Pareto solutions, an approximate two-phase (ATP) approach was developed, which is computationally inexpensive compare to the optimal solution approach. Through numerical experiments based on a realistic case study, it was shown that the ATP approach results in high accuracy and a two-orders-of-magnitude reduction in computation time. Additionally, for prompt application of our study to continuously changing situations, a rolling-horizon UAS approach herein is suggested.

Group movement of UAVs in environment with dynamic obstacles: a survey

PurposeThe successful application of the group of unmanned aerial vehicles (UAVs) in the tasks of monitoring large areas is becoming a promising direction in modern robotics. This paper aims to study the tasks related to the control of the UAV group while performing a common mission.Design/methodology/approachThis paper discusses the main tasks solved in the process of developing an autonomous UAV group. During the survey, five key tasks of group robotics were investigated, namely, UAV group control, path planning, reconfiguration, task assignment and conflict resolution. Effective methods for solving each problem are presented, and an analysis and comparison of these methods are carried out. Several specifics of various types of UAVs are also described.FindingsThe analysis of a number of modern and effective methods showed that decentralized methods have clear advantages over centralized ones, since decentralized methods effectively perform the assigned mission regardless of on the amount of resources used. As for the method of planning the group movement of UAVs, it is worth choosing methods that combine the algorithms of global and local planning. This combination eliminates the possibility of collisions not only with static and dynamic obstacles, but also with other agents of the group.Originality/valueThe results of scientific research progress in the tasks of UAV group control have been summed up.

A Task Allocation Strategy of the UAV Swarm Based on Multi-Discrete Wolf Pack Algorithm

With the continuous development of artificial intelligence, swarm control and other technologies, the application of Unmanned Aerial Vehicles (UAVs) in the battlefield is more and more extensive, and the UAV swarm is increasingly playing a prominent role in the future of warfare. How tasks are assigned in the dynamic and complex battlefield environment is very important. This paper proposes a task assignment model and its objective function based on dynamic information convergence. In order to resolve this multidimensional function, the Wolf Pack Algorithm (WPA) is selected as the alternative optimization algorithm. This is because its functional optimization of high-dimensional complex problems is better than other intelligent algorithms, and the fact that it is more suitable for UAV swarm task allocation scenarios. Based on the traditional WPA algorithm, this paper proposes a Multi-discrete Wolf Pack Algorithm (MDWPA) to solve the UAV task assignment problem in a complex environment through the discretization of wandering, calling, sieging behavior, and new individual supplement. Through Orthogonal Experiment Design (OED) and analysis of variance, the results show that MDWPA performs with better accuracy, robustness, and convergence rate and can effectively solve the task assignment problem of UAVs in a complex dynamic environment.

Research on UAV Task Assignment Based on Fireworks Algorithm

From military, industry, agriculture to artificial intelligence and other fields, unmanned aerial vehicles (UAVs) have been widely used. Task allocation and path planning are the two most basic technologies in all the application research of UAV. As the core of multi-aircraft tactical deployment and cooperative control, the task allocation scheme largely determines the operational efficiency and execution effect of multi-UAVs. Due to the influence of the task environment and its own resources, the multi-machine cooperative task allocation problem is a multi-objective optimization and decision-making problem limited by many constraints. In recent years, many methods of task assignment and path planning have been put forward, and some progress has been made. According to the overall control of the application scenario, the task planning makes a reasonable task allocation scheme for the UAV to help it complete the task better. Multi-UAV task assignment is a complex multi-constraint multi-objective nonlinear optimization problem. Aiming at the respective defects of common intelligent algorithms, this paper proposes to use fireworks algorithm to deal with this problem.

Cooperative Multiple Task Assignment of Heterogeneous UAVs Using a Modified Genetic Algorithm with Multi-type-gene Chromosome Encoding Strategy

The cooperative multiple task assignment problem (CMTAP) of heterogeneous fixed-wing unmanned aerial vehicles (UAVs) performing the Suppression of Enemy Air Defense (SEAD) mission against multiple ground stationary targets is studied in this paper. The CMTAP is a NP-hard combinatorial optimization problem, which faces many challenges like problem scale, heterogeneity of UAVs (different capability and maneuverability), task coupling and task precedence constraints. To address this issue, we proposed a modified genetic algorithm (GA) with multi-type-gene chromosome encoding strategy. Firstly, the multi-type-gene encoding scheme is raised to generate feasible chromosomes that satisfy the UAV capability, task coupling and task precedence constraints. Then, Dubins car model is adopted to calculate the mission execution time (objective function of CMTAP model) of each chromosome, and make each chromosome conform to the UAV maneuverability constraint. To balance the searching ability of algorithm and the diversity of population, we raise the modified crossover operator and multiple mutation operators according to the multi-type-gene chromosome encoding. The simulation results demonstrate that the modified GA has better optimization performance compared with random search method, ant colony optimization method and particle search optimization method.

Boolean Networks-Based Auction Algorithm for Task Assignment of Multiple UAVs

This paper presents an application of Boolean networks-based auction algorithm (BNAA) for task assignment in unmanned aerial vehicles (UAVs) systems. Under reasonable assumptions, the assignment framework consists of mission control system, communication network, and ground control station. As the improved algorithm of consensus-based bundle algorithm (CBBA), the BNAA utilizes a cluster-based combinatorial auction policy to handle multiple tasks. Instead of empirical method based on look-up table about conditional variables, Boolean network is introduced into consensus routine of BNAA for solving the conflict of assignment across the fleet of UAVs. As a new mathematic theory, semitensor product provides the implementation and theoretical proof of Boolean networks. Numerical results demonstrate the effectiveness and efficiency of proposed BNAA method.

Globally solving a nonlinear UAV task assignment problem by stochastic and deterministic optimization approaches

In this paper, we consider a task allocation model that consists of assigning a set of m unmanned aerial vehicles (UAVs) to a set of n tasks in an optimal way. The optimality is quantified by target scores. The mission is to maximize the target score while satisfying capacity constraints of both the UAVs and the tasks. This problem is known to be NP-hard. Existing algorithms are not suitable for the large scale setting. Scalability and robustness are recognized as two main issues. We deal with these issues by two optimization approaches. The first approach is the Cross-Entropy (CE) method, a generic and practical tool of stochastic optimization for solving NP-hard problem. The second one is Branch and Bound algorithm, an efficient classical tool of global deterministic optimization. The numerical results show the efficiency of our approaches, in particular the CE method for very large scale setting.

UAV Task Assignment

Unmanned aerial vehicles (UAVs) are becoming vital warfare and homeland security platforms because they have the potential to significantly reduce cost and risk to human life while amplifying warfighter and first-responder capabilities. This article builds on the very active area of planning and control for autonomous multiagent systems. This work represents a step toward enabling robust decision making for distributed autonomous UAVs by improving the team's operational reliability and capabilities through better system self-awareness and adaptive mission planning. The health-aware task assignment algorithm developed in this article was demonstrated to be effective both in simulation and flight experiments.

A robust approach to the UAV task assignment problem

AbstractThis paper presents a new robust approach to the task assignment of unmanned aerial vehicles (UAVs) operating in uncertain dynamic environments for which the optimization data, such as target cost and target–UAV distances, are time varying and uncertain. The impact of this uncertainty in the data is mitigated by tightly integrating two approaches for improving the robustness of the assignment algorithm. One approach is to design task assignment plans that are robust to the uncertainty in the data, which reduces the sensitivity to errors in the situational awareness (SA), but can be overly conservative for long duration plans. A second approach is to replan as the SA is updated, which results in the best plan given the current information, but can lead to a churning type of instability if the updates are performed too rapidly. The strategy proposed in this paper combines robust planning with the techniques developed to eliminate churning. This combination results in the robust filter‐embedded task assignment algorithm that uses both proactive techniques that hedge against the uncertainty, and reactive approaches that limit churning behavior by the vehicles. Numerous simulations are shown to demonstrate the performance benefits of this new algorithm. Copyright © 2007 John Wiley & Sons, Ltd.