Multiple Task Assignment Research Articles

Multiple Task Assignment Algorithms for Unmanned Aerial Vehicles

To fulfill the requirement of assigning task the auction algorithm is widely used. There are many classical auction algorithms those performances not up to the mark while dealing with multi-UAVs dynamic task assignment. SWARM UAVs are made up of a large number of small UAVs with limited mission resources that can operate in an autonomous, appropriate and universal manner. Based on the in-depth research of the traditional auction algorithm CAA, this paper proposes an iterative method that can improve the task allocation efficiency of multi-UAV, namely the two-stage auction algorithm. At the same time, in order to improve the daily management of airborne computing and communication resources of UAV, this paper overcomes the difficulties caused by data coupling between task allocation and path planning, and proposes a decentralized task allocation algorithm, that is, UAV re-checks the unreasonable task allocation results within the task allocation cycle. This method has the advantages of algorithm security and unpredictability, and it can control the error of task assignment evaluation within a specific range through finite complexity calculation. Simulation results show that the algorithm is effective in computing efficiency and task execution efficiency.

Multiple Task Assignment and Path Planning of a Multiple Unmanned Surface Vehicles System Based on Improved Self-Organizing Mapping and Improved Genetic Algorithm

This paper addresses multiple task assignment and path-planning problems for a multiple unmanned surface vehicle (USVs) system. Since it is difficult to solve multi-task allocation and path planning together, we divide them into two sub-problems, multiple task allocation and path planning, and study them separately. First, to resolve the multi-task assignment problem, an improved self-organizing mapping (ISOM) is proposed. The method can allocate all tasks in the mission area, and obtain the set of task nodes that each USV needs to access. Second, aiming at the path planning of the USV accessing the task nodes, an improved genetic algorithm (IGA) with the shortest path is proposed. To avoid USV collision during navigation, an artificial potential field function (APFF) is proposed. A multiple USV system with multi-task allocation and path planning is simulated. Simulation results verify the effectiveness of the proposed algorithms.

Cooperative Multiple Task Assignment Problem With Target Precedence Constraints Using a Waitable Path Coordination and Modified Genetic Algorithm

Task assignment is a critical technology for heterogeneous unmanned aerial vehicle (UAV) applications. Target precedence has typically been ignored in previous studies, such that it is possible to obtain a task assignment solution with an unreasonable target execution order. For this reason, a cooperative multiple task assignment problem with target precedence constraints (CMTAPTPC) model is proposed in this paper, which considers not only kinematic, resource, and task precedence constraints of the UAV, but also target precedence to achieve more realistic scenarios. In addition, a graph method is improved to detect and eliminate deadlocks in solutions that include target precedence constraints. We also introduced a waitable path coordination (WPC) algorithm to generate conflict-free flight paths. Unlike the traditional path elongation method, this method can reduce the number of path elongation operations and save the energy of UAVs. Based on the characteristics of the CMTAPTPC model, this study proposes a modified genetic algorithm that integrates the graph-based method and WPC algorithm to solve the task assignment problem. In the simulation, three problem-scale scenarios were designed, and the superior performance of the modified genetic algorithm was demonstrated by comparing it with a traditional genetic algorithm. Finally, a time series diagram shows the task assignment solution that meets all time constraints and illustrates the rationality of the WPC algorithm.

Cooperative Task Assignment of a Heterogeneous Multi-UAV System Using an Adaptive Genetic Algorithm

The cooperative multiple task assignment problem (CMTAP) is an NP-hard combinatorial optimization problem. In this paper, CMTAP is to allocate multiple heterogeneous fixed-wing UAVs to perform a suppression of enemy air defense (SEAD) mission on multiple stationary ground targets. To solve this problem, we study the adaptive genetic algorithm (AGA) under the assumptions of the heterogeneity of UAVs and task coupling constraints. Firstly, the multi-type gene chromosome encoding scheme is designed to generate feasible chromosomes that satisfy the heterogeneity of UAVs and task coupling constraints. Then, AGA introduces the Dubins car model to simulate the UAV path formation and derives the fitness value of each chromosome. In order to comply with the chromosome coding strategy of multi-type genes, we designed the corresponding crossover and mutation operators to generate feasible offspring populations. Especially, the proposed mutation operators with the state-transition scheme enhance the stochastic searching ability of the proposed algorithm. Last but not least, the proposed AGA dynamically adjusts the number of crossover and mutation populations to avoid the subjective selection of simulation parameters. The numerical simulations verify that the proposed AGA has a better optimization ability and convergence effect compared with the random search method, genetic algorithm, ant colony optimization method, and particle search optimization method. Therefore, the effectiveness of the proposed algorithm is proven.

Decentralized task allocation for heterogeneous multi-UAV system with task coupling constraints

Cooperative multiple task assignment problem is an essential issue in the collaboration of multiple unmanned aerial vehicles (UAVs). Consensus-based bundle algorithm (CBBA) is a decentralized task assignment method that only considers homogeneous agents and independent tasks. Thus, we develop an extended CBBA with task coupling constraints (CBBA-TCC) in this paper to solve the multi-task assignment problem with task coupling constraints in the heterogeneous multi-UAV system. CBBA is a two-stage iteration algorithm with inner and outer consensus stages. The inner consensus stage is designed as a modified version of CBBA in this paper. A Can-do list is firstly raised at the beginning of bundle construction phase on each agent to record the tasks that can be performed by this agent without violating the task precedence constraints. Hence, at the inner consensus stage, each agent will only bid on the Can-do list. Then, we adopt a task performing time list for each agent to store the performing times of its assigned tasks. With associate consensus strategy of task performing time list at the conflict resolution phase, the precedence constraint of coupled tasks can be guaranteed. After reaching inner consensus, the outer consensus stage introduces an insert-position feasibility index to determine whether the assigned tasks satisfy the coupling constraints and resolve the constraint violation conflicts. Through the iterations of inner and outer consensus stages, CBBA will reach global consensus and obtain conflict-free task assignment results. Numerical simulations demonstrate the feasibility and reliability of CBBA in various search and rescue scenarios.

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.

An Improved Lexicographical Whale Optimization Algorithm for the Type-II Assembly Line Balancing Problem Considering Preventive Maintenance Scenarios

In the traditional assembly line balancing, all the workstations are assumed available and hence the unavailability of any workstation brings about the stoppage of the whole line and the waste of the production capacity in the rest workstations. Considering the planning characteristic of preventive maintenance, this paper proposes a novel methodology of integrating the preventive maintenance scenarios into assembly line balancing problems to bypass the unavailable workstation. A lexicographic model is formulated to generate multiple task assignment plans that ensure primarily the high productivity under regular operation scenario and guarantee secondarily the production continuity under preventive maintenance scenarios. And, an improved whale optimization algorithm (IWOA) with three modifications is proposed to solve this problem. Specifically, a combined crossover operator enhances better combination in exploration; three best search agents promote the exploitation; partial regeneration avoids being trapped in local optima. More than five thousand experiments demonstrate that the joint of three modifications endows the IWOA significant superiority over six variants and other six well-known algorithms. Moreover, integrating preventive maintenance scenarios into the assembly line balancing problem increases the production efficiency by 1% at the cost of small production adjustment.

Target-bundled genetic algorithm for multi-unmanned aerial vehicle cooperative task assignment considering precedence constraints

This paper presents a modified genetic algorithm using target-bundle-based encoding and tailored genetic operators to effectively tackle cooperative multiple task assignment problems of heterogeneous unmanned aerial vehicles. In the cooperative multiple task assignment problem, multiple tasks including reconnaissance, attack, and verification have to be sequentially performed on each target (e.g. ground control stations, tanks, etc.) by one or multiple unmanned aerial vehicles. Due to the precedence constraints of different tasks, a singular task-execution order may cause deadlock situations, i.e. one or multiple unmanned aerial vehicles being trapped in infinite waiting loops. To address this problem, a target-bundled genetic algorithm is proposed. As a key element of target-bundled genetic algorithm, target-bundle-based encoding is derived to fix multiple tasks on each target as a target-bundle. And individuals are generated by fixing the task-execution order on each target-bundle subject to task precedence constraints. During the evolution process, bundle-exchange crossover and multi-type mutation operators are customized to generate deadlock-free offspring. Besides, the time coordination method is developed to ensure that task-execution time satisfies task precedence constraints. The comparison results on numerical simulations demonstrate that target-bundled genetic algorithm outperforms particle swarm optimization and random search methods in terms of optimality and efficiency.

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

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.

Cooperative multiple task assignment problem with stochastic velocities and time windows for heterogeneous unmanned aerial vehicles using a genetic algorithm

In this paper, a combinatorial optimization problem, formulated as a cooperative multiple task assignment problem with stochastic velocities and time windows for heterogeneous unmanned aerial vehicles, is studied in the form of a two-stage stochastic programming model. To create a more realistic mission scenario, we involve several types of constraints in this problem, such as kinematic constraints, resource constraints (both boarded weapons and fuels), and time constraints (both task sequences and time windows). Due to the prohibitive computational complexity of the problem, a novel meta-heuristic based on a modified genetic algorithm is proposed to improve the solution of this stochastic task assignment problem. After a feasible solution is obtained, a set of actual flight paths will be created by a path coordination process according to the requirements of the task precedence. In the simulation part, the effect of the proposed algorithm, both on searching capability and convergence speed, is demonstrated by comparison with the random search algorithm. Moreover, the stochastic nature of this problem caused by the stochastic flight velocities is also illustrated by comparison with a deterministic model. Additionally, actual flight trajectories meeting all time constraints are displayed for this stochastic task assignment problem.

Application of distributed auction to multi-UAV task assignment in agriculture

Abstract: This paper addresses the multiple farming task assignment for agricultural UAVs by presenting a decentralized auction algorithm, where computation and information are distributed among multiple parallel processing units (UAVs). The scheme iterates between bundle construction phase and two conflict resolution phases, and then converges to a task allocation and route plan simultaneously. In the bundle construction stage, each UAV groups the tasks with commonalities by considering its own capacities of flight endurance, weight load, battery, data storage, etc. In the following conflict resolution stages, the winning UAVs for tasks are determined by the information exchanged between UAVs. Later, the proposed algorithm is shown to have a low demand for communication and is proved to be able to achieve 50% optimality. Finally, an application of collecting the data of ground sensor nodes (including moving nodes) is used to assess the performance of the proposed scheme. Numerical experiments confirm superior convergence properties and performance of the proposed algorithm when compared with existing task-allocation algorithms. Keywords: unmanned aerial vehicle, farming task allocation, route plan, distributed, auction,agricultural aviation DOI: 10.33440/j.ijpaa.20180101.0008 Citation: Hu J, Yang J C. Application of distributed auction to multi-UAV task assignment in agriculture. Int J Precis Agric Aviat, 2018; 1(1): 44–50.

Embedding Remaining Useful Life Predictions into a Modified Receding Horizon Task Assignment Algorithm to Solve Task Allocation Problems

The Task Allocation problem is one of the fundamental combinatorial optimization problems with applications on various domains. Solving a Task Allocation problem consists in, given a set of tasks to be performed and a set of resources, defining which resource will perform each task in order to optimize an objective function. In this paper, we present a modified version of the Receding Horizon Task Assignment (RHTA) algorithm to solve multiple vehicle task assignment problems. In the proposed method, we generate a rejection list to reduce the number of candidate missions that are evaluated in each iteration of the RHTA algorithm. In addition, we incorporate in the mathematical formulation of the problem a set of constraints that limit the maximum mission duration that can be assigned to each vehicle. These constraints represent the predicted Remaining Useful Life (RUL) of each vehicle. Our model takes into account the execution time of each task and assumes that all vehicles must finish their missions at a base. The proposed model allows the vehicles to go to a base for maintenance during their missions. Numerical experiments are carried out using twenty benchmark problem instances. The results show that incorporating RUL predictions into task allocation problems increases the quality and the robustness of solutions.

The Effect of Sleep Loss on Dual Time-Based Prospective Memory Tasks

The aim of the present study was to deepen knowledge about the effect of a lowered vigilance state on time-based prospective memory (TBPM) performance. For this purpose 2 TBPM tasks (primary and interpolated), which shared a portion of the retention interval, and 3 reasoning tasks, as ongoing activities, were administered after total sleep deprivation and in a regular sleep condition. The results showed a detrimental effect of sleep deprivation on prospective memory performance and a partial dissociation between clock-checking behavior and time estimation for prospective compliance. This study clearly indicates that total sleep deprivation im- pairs the ability to complete multiple prospective task assignments in a timely fashion. Results are discussed suggesting the existence of different mechanisms involved in time monitoring and other cognitive functions underlying TBPM performance.

Genelleştirilmiş Atama Probleminin Hypergraf Çözümü ve Uzaysal Veri Kümelerine Uygulaması

In the simple task assignment problem, at most one task should be assigned to each agent; this constraint is relaxed in the multiple task assignment problems. The goal of the well-known Generalized Assignment Problem is to assign tasks to agents such that the capacity of the agent does not exceed its limits as it minimizes the total cost. In this study we present a novel approach to solve the general assignment problem by using the hypergraphs. Hypergraphs can be considered as the generalization of the graphs in such way that an edge can connect any number of vertices, and can be seen as the set systems. In the hypergraph multi-assignment problem, we are looking for a cost minimizing solution to tasks assignment to the agents which are the individual hyperedges. For this purpose, we first determine the tasks as hyperedges and then obtain the vertex cover of the simple graph representation of a hypergraph. Amongst the all possible covers, we choose the cost minimizing one as the solution.

The Command Decision Method of Multiple UUV Cooperative Task Assignment Based on Contract Net Protocol

Abstract With the help of multiple UCAV cooperative task control model, the mathematical model of multiple UUV cooperative task control is made. Variables related to decision are broken into goals, guidelines and programs levels by Analytical Hierarchy Process (AHP), on this basis; the command decision of multiple UUV task assignment is achieved. The correctness of task allocation algorithm is verified by case analysis. Time calculation formulas for a task assignment are given. The changes of overall effectiveness in the process of task allocation are analyzed, the time changes of each sub task allocation time in one task assignment are analyzed, the time changes of the number of tasks and platforms respectively fixed in task allocation are also discussed.

Empirical comparison of power-efficient virtual machine assignment algorithms

The advent of cloud computing has changed the way many companies do computation, allowing them to outsource it to the cloud. This has given origin to a new kind of business, the cloud providers, which run large datacenters. In order to be competitive, cloud providers must keep their operational costs low. One way to reduce these costs is reducing the energy consumed with smart task assignment algorithms, which decide where tasks are to be placed upon their arrival. Unfortunately, almost no task assignment algorithm used is power aware.In this paper we compare the performance of multiple task assignment algorithms for saving energy. We assume that tasks are in fact virtual machines that have to be assigned to physical machines, and we assume that the physical machines have a power consumption that increases superlinearly with the load. First, we propose two tunable power-aware task assignment algorithms (that subsume the algorithms studied in Arjona et al., Power-efficient assignment of virtual machines to physical machines, Future Generation Computer Systems, 2015). These algorithms are then compared with multiple state-of-the-art algorithms in different meaningful scenarios. Both algorithms prove themselves as interesting assignment algorithms since, properly configured, they outperform the other algorithms in most of the cases.

ADAPT-POLICY: Task Assignment in Server Farms when the Service Time Distributionof Tasks is Not Known A Priori

Service time distribution of certain computing workloads such as static web content is well known. However, for many other computing workloads (e.g., dynamic web content, scientific workloads) the service time distribution is not well understood and it is not correct to assume that these tasks follow a particular distribution. In this paper, we consider task assignment in server farms when both the service time distribution of tasks and (actual) sizes of tasks are not known a priori. We propose an adaptive task assignment policy, called ADAPT-POLICY, which is based on the concept of multiple static-based task assignment policies. ADAPT-POLICY defines a set of policies for a given system taking into account the specific properties of the system. These policies are selected in such a way that they have different performance characteristics under different workload conditions (i.e., service time distributions, etc.). The objective is to use the task assignment policy with the best performance (i.e., the one with the least expected waiting time) to assign tasks. Which task assignment policy performs the best depends on the traffic conditions that vary over time. ADAPT-POLICY determines the best task assignment using the service time distribution of tasks (and various other traffic properties), which is estimated on-line and then it adaptively changes the task assignment policy to suit the most recent traffic conditions. The experimental results show that ADAPT-POLICY can result in significant performance improvements over both static and dynamic task assignment policies.

Government Organization/Reorganization: Why Who Does What Matters

Antitrust law has been adopted by 120 jurisdictions worldwide. In more than half of these jurisdictions, the agency charged with enforcing antitrust law also has other responsibilities. The assignment of multiple regulatory tasks can affect the performance of a competition agency in complex and subtle ways. We present a framework for analyzing the consequences of creating public bodies with complex policy portfolios. Using examples from across the administrative state, we analyze the forces that shape the content of an agency’s policy duties, and how the portfolio of assigned duties affects the way an agency approaches its assigned tasks, and its performance of those tasks. We apply this framework to the U.S. Federal Trade Commission, whose diversified policy portfolio includes antitrust, consumer protection, and data protection/privacy.

An Efficient Coalition Formation for Multiple UAVs Task Assignment

An Efficient Coalition Formation for Multiple UAVs Task Assignment

The Effects of Multiple Task Assignment on Budget Reports with Ex Post Audits

The Effects of Multiple Task Assignment on Budget Reports with Ex Post Audits