Mission Allocation Research Articles

A Balanced Mission Planning for Multiple Unmanned Underwater Vehicles in Complex Marine Environments

The collaboration of a multiple unmanned underwater vehicles (multi-UUVs) system has attracted widespread attention in recent years, as it can overcome the limitations of a single UUV and enhance mission completion efficiency. Oriented towards patrol and exploration missions with multiple waypoints, this paper proposes a balanced mission planning strategy, aiming to improve mission quality while reducing mission time for multi-UUVs. Firstly, due to the uneven performance of the two optimization objectives, a quick initialization screening method is employed specifically for mission quality to reduce the mission space. Secondly, to ensure mission load distribution and collaboration among multi-UUVs, and ease the difficulty in solving the issues of mission allocation and route planning, a balanced bi-level mission planning method based on regional segmentation is proposed. Finally, applicable weight evaluation criteria are utilized to evaluate the feasible solution set and determine the optimal solution. The efficacy of the balanced mission planning strategy is substantiated through comprehensive numerical simulations in a complex 2D marine environment.

A Pesticide Spraying Mission Allocation and Path Planning With Multicopters

A Pesticide Spraying Mission Allocation and Path Planning With Multicopters

Onboard autonomous mission generation method based on user preference

This paper presents a method for autonomous onboard mission generation of Earth observation imaging satellites based on user preferences. The paper develops a planning database to store the previous user mission requirements, and proposes a database updating algorithm to track the changes in user preferences. The paper also proposes a mission generation method based on the planning database, which predicts target priorities and imaging methods. Finally, a mission allocation strategy is used to assign newly generated missions to the appropriate satellites. Simulations results show that the proposed method can effectively improve the utilization of satellite resources and thereby obtain higher mission return.

Optimization partial mission abandonment strategy for k-out-of-n multi-state system

This paper studies a system with n multi-state components, when the number of failed components reaches k, the system fails. During system operation, all non-failed components perform all missions simultaneously. A static mission allocation strategy and a partial mission abandonment strategy are explored. Among them, static mission allocation mainly refers to the allocation of system missions in a fixed proportion according to the performance of components, while partial mission abandonment represents the selection of an appropriate time to give up some missions for the mission system that can be classified according to the characteristics of importance. The study adopts the Markov process to deduce the system reliability and mission execution success probability, and explores the balance between them according to a cost-oriented partial mission abandonment strategy. A distributed computer system is taken as an example to show the feasibility of the model application, and an optimal strategy is provided for the optimization of partial mission abandonment strategy for a system characterized by missions can be graded in the analysis.

Minimization of Aerial Cost and Mission Completion Time in Multi-UAV-Enabled IoT Networks

The application of unmanned aerial vehicles (UAVs) in IoT networks, especially data collection, has received extensive attention. Due to the urgency of the mission and limitation of the network cost, the mission completion time and number of UAVs are research hotspots. Most studies mainly focus on the trajectory optimization of the UAV to shorten the mission completion time. However, under different data collection modes, flying mode (FM) and hovering mode (HM), the collection time will also greatly affect the mission completion time. This paper studies the data collection from ground IoT devices (GIDs) in Multi-UAV enabled IoT networks. The problem of data collection is formulated to minimize the aerial cost and maximum mission completion time of UAVs by optimizing mission allocation, UAV trajectory, and UAVs’ flying speeds. In view of the complexity and non-convexity of the formulated problem, we propose a heuristic-based approximation algorithm to optimize the mission allocation of UAVs. Then, we specifically optimize the trajectory of the UAV for GIDs to minimize the flight time and collection time. Since the UAV’s flying speed affects the mission completion time, the successive convex approximation (SCA) technique is adopted to optimize it. Simulation results show that our scheme achieves the performance of near-optimal solution.

Intelligent Planning Modeling and Optimization of UAV Cluster Based on Multi-Objective Optimization Algorithm

As a flight tool integrating carrier and reconnaissance, unmanned aerial vehicles (UAVs) are applied in various fields. In recent years, mission planning and path optimization have become the most important research focuses in the field of UAVs. With the continuous maturity of artificial intelligence technology, various search algorithms have been applied in the field of unmanned aerial vehicles. However, these algorithms have certain defects, which lead to problems, such as large search volume and low efficiency in task planning, and cannot meet the requirements of path planning. The objective optimization algorithm has a good performance in solving optimization problems. In this paper, the intelligent planning model of UAV cluster was established based on multi-objective optimization algorithm, and its path is optimized. In the aspect of modeling, this paper studied and analyzed online task planning, search rules and cluster formation control using an agent-based intelligent modeling method. For mission planning and optimization, it combined multi-objective optimization algorithm to build the model from three aspects of mission allocation, route planning and planning evaluation. The final simulation results showed that the UAV cluster intelligent planning modeling method and path optimization method based on multi-objective optimization algorithm met the requirements of route design and improved the path search efficiency with 2.26% task completion satisfaction.

A New Double-Layer Decentralized Consistency Algorithm for the Multi-Satellite Autonomous Mission Allocation Based on a Block-Chain.

The traditional on-board centralized-distributed mission negotiation architecture has poor security and reliability. It can easily give rise to the collapse of the whole system when the master node is attacked by malicious nodes. To address this issue, the decentralized consistency algorithms commonly used in the internet world are referred to in this paper. Firstly, four typical consistency algorithms suitable for the Internet and which are named RAFT, PBFT, RIPPLE and DPOS are selected and modified for a multi-satellite autonomous mission negotiation. Additionally, based on the above modified consistency algorithms, a new double-layer decentralized consistency algorithm named DDPOS is proposed. It is well known that the above four common consistency algorithms cannot have both a low resource occupation and high security. The DDPOS algorithm can integrate the advantages of four common consistency algorithms due to its freedom of choice attribute, which can enable satellite clusters to flexibly adopt different appropriate consistency algorithms and the number of decentralized network layers. The DDPOS algorithm not only greatly improves the security and reliability of the whole satellite cluster, but also effectively reduces the computing and communication resources occupation of the satellite cluster. Without the presence of a malicious node attack, the resource occupation of the DDPOS algorithm is almost the same as that of the RAFT algorithm. However, in the case of a malicious node attack, compared with the RAFT algorithm, the total computation and total bandwidth occupation of the DDPOS algorithm have decreased by 67% and 75%, respectively. Moreover, it is surprising that although the DDPOS algorithm is more complex, its code size is only about 8% more than the RAFT algorithm. Finally, the effectiveness and feasibility of the DDPOS algorithm in the on-board practical application are analyzed and verified via simulation experiments.

Minimizing Mission Completion Time of UAVs by Jointly Optimizing the Flight and Data Collection Trajectory in UAV-Enabled WSNs

Unmanned aerial vehicles (UAVs) have been widely used to collect data from ground nodes (GNs) in various applications because of their flexibility and mobility. Some emergency situations pose strict requirements for UAV’s mission completion time. The current research focuses on the design and optimization of UAV’s flight trajectory to shorten the mission completion time. But the data collection process also greatly affects the mission completion time, especially when the uplink throughput is large. This article emphasizes the data collection process and proposes a V-shaped collection trajectory for the first time to shorten UAV’s collection time and then jointly optimizes the flight and collection trajectory to minimize the mission completion time. We formulate the mission completion time under energy constraints to an optimization problem and decompose it into three subproblems: 1) mission allocation of UAVs; 2) collection time minimization; and 3) joint optimization of collection and flight trajectory. 1) We establish the mission allocation model by setting the scaling factors for the flight and collection mission, and then solve it by utilizing the min–max multiple traveling salesman problem (MTSP) algorithm; 2) for the data collection process, we propose a V-shaped trajectory to minimize the data collection time and prove its validity; and 3) since the flight and collection trajectory of the adjacent GNs are deeply coupled, we model the joint optimization of the flight and the collection time as a mixed-integer nonlinear programming problem, and solve it by the joint optimization algorithm for flight trajectory and collection trajectory (JOFC). The simulation results show that our proposal outperforms the compared schemes.

Research on uncertain bi-objective UAV mission allocation problem

Research on uncertain bi-objective UAV mission allocation problem

Multi-UAV Optimal Mission Assignment and Path Planning for Disaster Rescue Using Adaptive Genetic Algorithm and Improved Artificial Bee Colony Method

An optimal mission assignment and path planning method of multiple unmanned aerial vehicles (UAVs) for disaster rescue is proposed. In this application, the UAVs include the drug delivery UAV, image collection UAV, and communication relay UAV. When implementing the modeling and simulation, first, three threat sources are built: the weather threat source, transmission tower threat source, and upland threat source. Second, a cost-revenue function is constructed. The flight distance, oil consumption, function descriptions of UAV, and threat source factors above are considered. The analytic hierarchy process (AHP) method is utilized to estimate the weights of cost-revenue function. Third, an adaptive genetic algorithm (AGA) is designed to solve the mission allocation task. A fitness function which considers the current and maximum iteration numbers is proposed to improve the AGA convergence performance. Finally, an optimal path plan between the neighboring mission points is computed by an improved artificial bee colony (IABC) method. A balanced searching strategy is developed to modify the IABC computational effect. Extensive simulation experiments have shown the effectiveness of our method.

Multilingualism, nationality and flexibility: mobile communicators’ careers in a humanitarian agency

AbstractThis article explores the management of multilingual mobile communicators at the International Committee of the Red Cross (ICRC) according to institutional requirements and the consequences on Arabic-speaking communicators’ careers. Based on interviews complemented by institutional documents, I analyse the impact of “multiple languages”, “‘easy’ nationality” and “flexibility for non-family posts” as key requirements for mobile communicators. The institutional shortage of Arabic speakers largely limited their experience to Middle East and Northern Africa (MENA) while other communicators allegedly had a broader variety of field experience for career advancement. Linguistic repertoires and nationality shaped mission allocation since acceptance of one’s nationality in the host country largely outweighed linguistic competences in Arabic in MENA, with a preference for European or dual nationals and the Lebanese over other Arab nationals. Flexibility for non-family posts, numerous in MENA, seems to reinforce an unequal distribution of reproductive work and structural gender inequalities. Many interviewed women felt that they had to choose between the agency and family. Although desirable, family posts outside MENA were not considered as exciting communications work. Besides hiring L2 speakers with an acceptable nationality, interviewed communicators suggested a new rotation system, advising roles for Arabic-speaking professionals and special quota for women.

A Model for the Optimal Mission Allocation of Naval Warship Based on Absorbing Markov Chain Simulation

A Model for the Optimal Mission Allocation of Naval Warship Based on Absorbing Markov Chain Simulation

Mission Planning for Unmanned Aerial Vehicles Based on Voronoi Diagram‐Tabu Genetic Algorithm

Unmanned aerial vehicles (UAVs) are increasingly used in different military missions. In this paper, we focus on the autonomous mission allocation and planning abilities for the UAV systems. Such abilities enable adaptation to more complex and dynamic mission environments. We first examine the mission planning of a single unmanned aerial vehicle. Based on that, we then investigate the multi‐UAV cooperative system under the mission background of cooperative target destruction and show that it is a many‐to‐one rendezvous problem. A heterogeneous UAV cooperative mission planning model is then proposed where the mission background is generated based on the Voronoi diagram. We then adopt the tabu genetic algorithm (TGA) to obtain multi‐UAV mission planning. The simulation results show that the single‐UAV and multi‐UAV mission planning can be effectively realized by the Voronoi diagram‐TGA (V‐TGA). It is also shown that the proposed algorithm improves the performance by 3% in comparison with the Voronoi diagram‐particle swarm optimization (V‐PSO) algorithm.

A cooperative interference resource allocation method based on improved firefly algorithm

To deal with the radio frequency threat posed by modern complex radar networks to aircraft, we researched the unmanned aerial vehicle (UAV) formations radar countermeasures, aiming at the solution of radar jamming resource allocation under system countermeasures. A jamming resource allocation method based on an improved firefly algorithm (FA) is proposed. Firstly, the comprehensive factors affecting the level of threat and interference efficiency of radiation source are quantified by a fuzzy comprehensive evaluation. Besides, the interference efficiency matrix and the objective function of the allocation model are determined to establish the interference resource allocation model. Finally, A mutation operator and an adaptive heuristic are integtated into the FA algorithm, which searches an interference resource allocation scheme. The simulation results show that the improved FA algorithm can compensate for the deficiencies of the FA algorithm. The improved FA algorithm provides a more scientific and reasonable decision-making plan for aircraft mission allocation and can effectively deal with the battlefield threats of the enemy radar network. Moreover, in terms of convergence accuracy and speed as well as algorithm stability, the improved FA algorithm is superior to the simulated annealing algorithm (SA), the niche genetic algorithm (NGA), the improved discrete cuckoo algorithm (IDCS), the mutant firefly algorithm (MFA), the cuckoo search and fireflies algorithm (CSFA), and the best neighbor firefly algorithm (BNFA).

A Hierarchical Decision-Making Method with a Fuzzy Ant Colony Algorithm for Mission Planning of Multiple UAVs

Unmanned aerial vehicles (UAVs) received an unprecedented surge of people’s interest worldwide in recent years. This paper investigates the specific problem of cooperative mission planning for multiple UAVs on the battlefield from a hierarchical decision-making perspective. From the view of the actual mission planning issue, the two key problems to be solved in UAV collaborative mission planning are mission allocation and route planning. In this paper, both of these problems are taken into account via a hierarchical decision-making model. Firstly, we use a target clustering algorithm to divide the original targets into target subgroups, where each target subgroup contains multiple targets. Secondly, a fuzzy ant colony algorithm is used to calculate the global path between target subgroups for a single-target group. Thirdly, a fuzzy ant colony algorithm is also used to calculate the local path between multiple targets for a single-target subgroup. After three levels of decision-making, the complete path for multiple UAVs can be obtained. In order to improve the efficiency of a collaborative task between different types of UAVs, a cooperative communication strategy is developed, which can reduce the number of UAVs performing tasks. Finally, experimental results demonstrate the effectiveness of the proposed cooperative mission planning and cooperative communication strategy for multiple UAVs.

Cooperative Optimization Algorithm Based on Desert Sparrow for FANETs

FANET is an emerging approach for networking these days. But it is still in its infancy. It is a form of multi-UAVs system comprising of a mesh of a number of nodes that provide a flexible on the fly communication between each other. In such networks, involving diversified nodes a system based on cooperation among the nodes for job allotment becomes an utmost important thing. Cooperative task allocation is an important aspect of networks. Cooperative task between the nodes is an important task in the field of research. Cooperation among different nodes can be achieved with the help of various framework, architecture or models. The allocation of task can be based on some mission or can be achieved through simple utilization of existing resources. Many optimization algorithms are based on cooperative behavior of nodes; the same can be understood taking examples of many living beings in the world. In our paper, the algorithm of desert sparrow optimization is taken for the cooperative mission allocation. The functions and exploration of this algorithm will be defined for the FANETs. Python and RStudio are used to plot the graphs from the dataset. The graph is drawn between time series and dataset. The algorithm will be implemented using linear regression techniques and Python. The results show, how the intelligent communication between UAVs take place and this is adopted from desert sparrow.

이산 사건 모델링 및 시뮬레이션 기반의 다수 무인기 임무 할당 및 최적화 기법

본 연구는 복잡한 다수 무인기 시스템을 효율적으로 운용하기 위해 이산 사건 모델 기반의 다수 무인기 이종 임무 할당 기법을 제안한다. 이종 임무의 생성, 임무 할당, 무인기 출동, 임무 수행, 무인기 수리 및 정비에 이르는 무인기의 임무 시작에서 종료까지 일련의 과정을 이산 사건 모델링을 통해 수학적으로 정의하고, 이를 기반으로 주어진 임무 상황에서 필요한 최적 무인기 대수를 도출한다. 도출된 최적 무인기 대수를 적용하여 이산 사건 모델 기반의 반복 시뮬레이션을 통해 그 결과를 검증한다. 제안된 이종 임무 할당 기법으로 운용자는 제한된 무인기 자원을 효율적으로 활용하여 주어진 임무 상황에서 최적의 임무 계획을 수립할 수 있다.

Multi-UAV Mission Allocation under Constraint

This paper is devoted to the unmanned aerial vehicle (UAV) mission allocation problem. To solve this problem in a more realistic battlefield environment, an improved mathematical model for UAV mission allocation is proposed. Being different from previous formulations, this model not only considers the difference in the importance of the target but also the constraints of the time window. In addition, an indicator of reconnaissance reward is added to this model. Each target area has a different importance, just as the strategic value of each region is different in combat. In this paper, we randomly generate the value factor for each reconnaissance area. To solve the mathematical model with different operational intentions, a dimensionality reduction process for which the reconnaissance reward is the optimization objective is presented. Finally, based on the improved model, the simulation result with Lingo is compared with that of non-dominated sorting genetic algorithm with elite strategy (NSGA-II) and genetic algorithm (GA) to verify the reliability and the effectiveness of the improved method.

A new multi-satellite autonomous mission allocation and planning method

To overcome the issues of inflexible interactive mode, low negotiation efficiency and poor dynamic response capability of satellite clusters, we propose a new multi-dimensional and multi-Agent clusters collaboration model (MDMA-CCM), including task pre-processing, allocation, scheduling and re-planning under failed conditions. First, the task clustering based pre-processing method is adopted to improve the observation efficiency by combining potential targets. Second, the Contract Net Protocol (CNP) based secondary allocation strategy is developed to effectively increase the observation benefit and reduce the impact of task conflicts through announcement, bidding and awarding. Third, a new Interactively Re-planning Method (IRM) is proposed to effectively reduce the loss by re-inserting tasks in case of failure. Simulation results show that the new planning model not only has obvious advantages in calculation time, but also outperforms the centralized multi-Agent system in terms of benefit value and completion rate.

Onboard mission allocation for multi-satellite system in limited communication environment

The purpose of this paper is to seek the suitable local objectives for each satellite to optimal the global mission allocation strategy with a global utility function. This paper uses a game-theoretical formulation for a multi-satellite system in which the satellites are viewed as a unique unit with their self-interests. To solve the problem, the first is to identify the utility functions for individual satellite, align them to form the global utility function which can represents the allocation requirements. The second is to design the suitable negotiation mechanisms that can be equipped on each satellite so they can pursue the optimization by their own interest. The Utility-based Regret play, the Smoke Signal play, and the Broadcast-based play are proposed as negotiation mechanisms for the team to cooperate under the distributed and decentralized system structure. The simulation results illustrate that using these mechanisms can help this multi-satellite system reaches a near-optimal allocation profile. The effectiveness of proposed mechanisms are demonstrated by comparing their simulation results with several existing mechanisms under different scale of participant number.