Target Assignment Research Articles

Multi-UAV simultaneous target assignment and path planning based on deep reinforcement learning in dynamic multiple obstacles environments.

Target assignment and path planning are crucial for the cooperativity of multiple unmanned aerial vehicles (UAV) systems. However, it is a challenge considering the dynamics of environments and the partial observability of UAVs. In this article, the problem of multi-UAV target assignment and path planning is formulated as a partially observable Markov decision process (POMDP), and a novel deep reinforcement learning (DRL)-based algorithm is proposed to address it. Specifically, a target assignment network is introduced into the twin-delayed deep deterministic policy gradient (TD3) algorithm to solve the target assignment problem and path planning problem simultaneously. The target assignment network executes target assignment for each step of UAVs, while the TD3 guides UAVs to plan paths for this step based on the assignment result and provides training labels for the optimization of the target assignment network. Experimental results demonstrate that the proposed approach can ensure an optimal complete target allocation and achieve a collision-free path for each UAV in three-dimensional (3D) dynamic multiple-obstacle environments, and present a superior performance in target completion and a better adaptability to complex environments compared with existing methods.

A Potential Game Approach to Target Assignment in Heterogeneous Manned/Unmanned Aerial Team with Incomplete Information

This brief proposes a potential game method in a dis-tributed manner to address the target assignment problem with incomplete information in heterogeneous manned/unmanned aer-ial teams. Firstly, a heterogeneous system is formulated as a graph and different utility functions are designed to characterize the util-ities of the two kinds of members. Secondly, a local information matrix is developed for each member to infer the actions of others. Finally, an improved learning algorithm is proposed to achieve the optimal solution. Simulation results show the feasibility and effec-tiveness of the potential game approach with information esti-mates.

Integrated Target Assignment and Trajectory Optimization for Many-to-Many Midcourse Guidance

Integrated Target Assignment and Trajectory Optimization for Many-to-Many Midcourse Guidance

MOEA with adaptive operator based on reinforcement learning for weapon target assignment

<abstract><p>Weapon target assignment (WTA) is a typical problem in the command and control of modern warfare. Despite the significance of the problem, traditional algorithms still have shortcomings in terms of efficiency, solution quality, and generalization. This paper presents a novel multi-objective evolutionary optimization algorithm (MOEA) that integrates a deep Q-network (DQN)-based adaptive mutation operator and a greedy-based crossover operator, designed to enhance the solution quality for the multi-objective WTA (MO-WTA). Our approach (NSGA-DRL) evolves NSGA-II by embedding these operators to strike a balance between exploration and exploitation. The DQN-based adaptive mutation operator is developed for predicting high-quality solutions, thereby improving the exploration process and maintaining diversity within the population. In parallel, the greedy-based crossover operator employs domain knowledge to minimize ineffective searches, focusing on exploitation and expediting convergence. Ablation studies revealed that our proposed operators significantly boost the algorithm performance. In particular, the DQN mutation operator shows its predictive effectiveness in identifying candidate solutions. The proposed NSGA-DRL outperforms state-and-art MOEAs in solving MO-WTA problems by generating high-quality solutions.</p></abstract>

A Dynamic Air Combat Situation Assessment Model Based on Situation Knowledge Extraction and Weight Optimization

Air combat situation assessment is the basis of target assignment and maneuver decisions. The current air combat situation assessment models, whether nonparametric or parametric, ignore the continuity and timing of situation changes, making the situation assessment results lose tactical significance. Aimed at the shortcomings of current air combat situation assessment, a dynamic air combat situation assessment model based on situation knowledge extraction and weight optimization was proposed by combining a multiple regression model of hidden logic process, a weight optimization model based on grey prospect theory, a weight mapping model based on autoencoder and extreme learning machine (AE-ELM) and an air combat situation characteristic parameter prediction model based on dynamic weight online extreme learning machine (DWOSELM). Firstly, considering the timing and continuity of air combat situation change, a hidden logic process multiple regression model was introduced to realize the segmentation of air combat situation time series data and the extraction of air combat situation primitives. Secondly, the weight optimization method based on grey prospect theory was used to obtain the weight of the evaluation index under different air combat situations. On this basis, the dynamic mapping model between air combat situation characteristic parameters and the weight of index was constructed by using AE-ELM. Then, the dynamic weighted extreme learning machine was used to build the target maneuver trajectory prediction model, and the future position information of the target was predicted. On this basis, the future situation information between the enemy and us was obtained. Finally, the time weight calculation model based on normal cumulative distribution was used to determine the importance of the situation at each time. The situation information at multiple times in the air combat process was fused to obtain the comprehensive air combat situation assessment results at the current time. The simulation results show that the model can fully exploit the influence of historical information, effectively integrate the air combat situation information at multiple moments, and generate the air combat situation assessment results with practical tactical significance according to the individual differences of different pilots.

War game problem considering the mobility of weapons and targets

War-Gaming is recognized as a valuable tool for commanders, leaders, and managers. Well-executed War-Games have delivered significant competitive advantages in numerous conflicts. The war-game confirmed the commanders’ knowledge of weapon systems and performance, as well as the time and space necessary to carry out battlefield maneuvers. One of the primary missions of each army on the battlefield is weapon target assignment. The weapon target assignment (WTA) is a critical problem to command to be solved in battlefield decisions. In a WTA problem, we should assign available weapons to determined targets appropriately to optimize the performance criteria. This study discusses a problem in relation to allocating and scheduling in WTA considering the mobility weapons and mobility targets. Bi-level linear programming problem is defined so that each level independently optimizes its own objective functions but is influenced by actions taken by another unit. To solve the under studied problem, three famous meta-heuristic algorithms including simulated annealing (SA), genetic algorithm (GA) and grey wolf optimizer (GWO) methods are proposed. Since the performance of meta-heuristic algorithms depends on setting the parameters, the Taguchi method has been used statistically for set parameters of the developed Algorithms. Performance evaluation of the presented algorithms is conducted through numerical experiments involving randomly generated test problems. To compare the results of proposed meta-heuristic algorithms, ANOVA and Tukey tests were used. The Computational results have shown that proposed GWO algorithm worked better than the SA and GA algorithms.

Global Trajectory Optimization of Multispacecraft Successive Rendezvous Using Multitree Search

This paper investigates the global optimization of multispacecraft successive rendezvous trajectories, which is divided here into three subproblems: target assignment, sequence optimization, and rendezvous time optimization. A method consisting of two novel algorithms is proposed to solve these subproblems. First, a multitree search framework is developed to assign multiple targets to each spacecraft and simultaneously optimize the rendezvous sequence for every single spacecraft. Specifically, a novel algorithm of local search combined with beam search is proposed. Second, this paper converts the rendezvous time optimization problem into a multistage decision problem. Based on a critical rendezvous-epoch-dependent characteristic found in this subproblem, the number of state variables is thereby reduced. A novel dual dynamic programming algorithm is proposed and combined with dynamic programming to solve for the globally optimal rendezvous epochs efficiently. Global optimality is guaranteed by Bellman’s principle of optimality, which is the first time in such a problem to our knowledge. The proposed method achieves state-of-the-art performance in several typical fuel-optimal scenarios of active debris removal. This open-sourced method is non-database-dependent and contains only one design stage, which is expected to be adopted in other successive rendezvous missions.

Event-Triggered bundled target traversing path planning using a dynamic elliptical guidance region for unmanned surface vehicles

Multiple problems such as target assignment timeliness and path energy optimization make it challenging for USVs traversing targets. This paper proposes a novel multiple targets traversing path planning algorithm for USVs, which includes Event-Triggered bundled (ET-Bundled) genetic target assignment method and dynamic elliptical guidance region (DEGR) path planning method. In terms of solving above problems, comparative simulation studies with the state-of-the-art methods demonstrate that ET-Bundled can not only ensures that the traversing time and path length do not exceed the constraint range, but also reduces the average CPU time of membrane structure by 60%, and DEGR can optimize the path energy consumption of EGR path by 34%.

A Best-Response Algorithm With Voluntary Communication and Mobility Protocols for Mobile Autonomous Teams Solving the Target Assignment Problem

A Best-Response Algorithm With Voluntary Communication and Mobility Protocols for Mobile Autonomous Teams Solving the Target Assignment Problem

Group motion control for UAV swarm confrontation using distributed dynamic target assignment

Group motion control for UAV swarm confrontation using distributed dynamic target assignment

Elite-centered artificial bee colony algorithm with extended solution boundary

To improve the performance of Artificial Bee Colony (ABC) algorithm, we propose a novel approach (named ESOABC-EC&ACS). First, the existence of solutions outside the constrained region (ESO) is designed to explore the possibility of the search helped by these solutions, which facilitates exploring the boundary of the constrained region and obtaining optimal solutions faster. Then, an elite-centered search equation is proposed to balance exploration and exploitation. Finally, adaptive bee colony scaling is constructed to help convergence. Our approach is the combination of these three techniques. The experimental results demonstrate that ESOABC-EC&ACS outperforms other ABC variants and some non-ABC meta-heuristic algorithms on most of the test functions in terms of solution quality. And the proposed heuristic factor for the dynamic weapon target assignment problem can improve the optimization performance. The ESO is the first universal strategy proposed for the survival of solutions outside the constraint region (out-of-bounds) generated when solving various problems directly, which causes extended solution boundary and affects the exploration of the constrained region boundary.

An Evolutionary Game-Theoretic Approach to Unmanned Aerial Vehicle Network Target Assignment in Three-Dimensional Scenarios

Target assignment has been a hot topic of research in the academic and industrial communities for swarms of multiple unmanned aerial vehicle (multi-UAVs). Traditional methods mainly focus on cooperative target assignment in planes, and they ignore three-dimensional scenarios for the multi-UAV network target assignment problem. This paper proposes a method for target assignment in three-dimensional scenarios based on evolutionary game theory to achieve cooperative targeting for multi-UAVs, significantly improving operational efficiency and achieving maximum utility. Firstly, we construct an evolutionary game model including game participants, a tactical strategy space, a payoff matrix, and a strategy selection probability space. Then, a multi-level information fusion algorithm is designed to evaluate the overall attack effectiveness of multi-UAVs against multiple targets. The replicator equation is leveraged to obtain the evolutionarily stable strategy (ESS) and dynamically update the optimal strategy. Finally, a typical scenario analysis and an effectiveness experiment are carried out on the RflySim platform to analyze the calculation process and verify the effectiveness of the proposed method. The results show that the proposed method can effectively provide a target assignment solution for multi-UAVs.

An integrated cooperative guidance design for target assignment and simultaneous attack on multiple targets

This paper proposes an integrated cooperative guidance strategy for multiple unmanned aerial vehicles to simultaneously attack multiple targets. To solve the challenging cooperative attack problem coupled with the target assignment problem simultaneously in a unified one-phase framework, a novel auxiliary index denoted as the integrated relative distance is designed and analysed from two opposite perspectives. Because of its special properties, the proposed integrated relative distance plays a crucial role in evaluating dynamic engagement situations between attackers and targets, and it further provides a criterion for whether the cooperative attack mission is completed. Then all integrated relative distances are minimised to directly derive a cooperative guidance law for attackers based on optimal control theory. The simultaneous minimisation of integrated relative distances from both forward and backward perspectives ensures that each target is destroyed by at least one attacker and all attackers are fully exploited to obtain optimal attack performance. By this means, the target assignment problem is automatically solved and the cooperative simultaneous attack is achieved. Several simulation cases are presented to illustrate the effectiveness of the proposed integrated cooperative guidance strategy.

A Hybrid BA - VNS Algorithm for Solving the Weapon Target Assignment Considering Mobility of Resources

A Hybrid BA - VNS Algorithm for Solving the Weapon Target Assignment Considering Mobility of Resources

Multi-UAV Collaborative Search and Attack Mission Decision-Making in Unknown Environments.

To address the challenge of coordinated combat involving multiple UAVs in reconnaissance and search attacks, we propose the Multi-UAV Distributed Self-Organizing Cooperative Intelligence Surveillance and Combat (CISCS) strategy. This strategy employs distributed control to overcome issues associated with centralized control and communication difficulties. Additionally, it introduces a time-constrained formation controller to address the problem of unstable multi-UAV formations and lengthy formation times. Furthermore, a multi-task allocation algorithm is designed to tackle the issue of allocating multiple tasks to individual UAVs, enabling autonomous decision-making at the local level. The distributed self-organized multi-UAV cooperative reconnaissance and combat strategy consists of three main components. Firstly, a multi-UAV finite time formation controller allows for the rapid formation of a mission-specific formation in a finite period. Secondly, a multi-task goal assignment module generates a task sequence for each UAV, utilizing an improved distributed Ant Colony Optimization (ACO) algorithm based on Q-Learning. This module also incorporates a colony disorientation strategy to expand the search range and a search transition strategy to prevent premature convergence of the algorithm. Lastly, a UAV obstacle avoidance module considers internal collisions and provides real-time obstacle avoidance paths for multiple UAVs. In the first part, we propose a formation algorithm in finite time to enable the quick formation of multiple UAVs in a three-dimensional space. In the second part, an improved distributed ACO algorithm based on Q-Learning is introduced for task allocation and generation of task sequences. This module includes a colony disorientation strategy to expand the search range and a search transition strategy to avoid premature convergence. In the third part, a multi-task target assignment module is presented to generate task sequences for each UAV, considering internal collisions. This module provides real-time obstacle avoidance paths for multiple UAVs, preventing premature convergence of the algorithm. Finally, we verify the practicality and reliability of the strategy through simulations.

Deep Reinforcement Learning‐Based Air Defense Decision‐Making Using Potential Games

This study addresses the challenge of intelligent decision‐making for command‐and‐control systems in air defense combat operations. Current autonomous decision‐making systems suffer from limited rationality and insufficient intelligence during operation processes. Recent studies have proposed methods based on deep reinforcement learning (DRL) to address these issues. However, DRL methods typically face challenges related to weak interpretability, lack of convergence guarantees, and high‐computing power requirements. To address these issues, a novel technique for large‐scale air defense decision‐making by combining a DRL technique with game theory is discussed. The proposed method transforms the target assignment problem into a potential game that provides theoretical guarantees for Nash equilibrium (NE) from a distributed perspective. The air‐defense decision problem is decomposed into separate target selection and target assignment problems. A DRL method is used to solve the target selection problem, while the target assignment problem is translated into a target assignment optimization game. This game is proven to be an exact potential game with theoretical convergence guarantees for an NE. Having simulated the proposed decision‐making method using a digital battlefield environment, the effectiveness of the proposed method is demonstrated.

Swarms of modular satellites decentralized guidance and target assignment strategy

This paper presents a decentralized guidance and target assignment (DGTA) strategy for reconfiguring swarms made up of hundreds of modules that have limited communication capabilities. This strategy provides a decentralized onboard computation method to address both near-optimal target assignment and collision-free trajectory planning for swarms of modules. To effectively estimate the transfer cost between modules and targets for target assignment, a multilayer perceptron (MLP) is developed via the backward generation method. The decentralized target assignment problem is solved by greedy algorithm according to the result of the efficient estimating solver. A decentralized model predictive control approach is proposed to guide swarm modules towards their assigned targets, while avoiding collisions with other modules. The feasibility of this guidance framework has been demonstrated through rigorous theoretical analysis. Numerical experiment confirms that the MLP-based method shows an accurate and real-time advantage for estimating transfer cost. Furthermore, the DGTA strategy has been shown to possess robustness and generalization ability, as evidenced by its successful application in a pinpoint assembly task involving 125 and 182 modules. The simulation results confirm the feasibility of this approach for in-orbit assembly application.

A quantum algorithm for solving weapon target assignment problem

Quantum computers, known to have the potential for exponential speedup in solving some problems due to their superposition property, are expected to facilitate the solution of NP-hard optimisation problems. This study proposes a quantum algorithm to solve the weapon target assignment problem (WTAP), one of the NP-hard optimisation problems. The proposed quantum algorithm is a gate-based approach, and scenario examples are executed on Qiskit quantum computing platform and IBM Lima quantum computer with Falcon r4T processor type. The results manifest that the proposed quantum algorithm has low space and time complexity, demonstrating its memory and computational resources efficiency.

Assessing downstream aquatic habitat availability relative to headwater reservoir management in the Henrys Fork Snake River

AbstractReservoirs are sometimes managed to meet agricultural and other water demands, while also maintaining streamflow for aquatic species and ecosystems. In the Henrys Fork Snake River, Idaho (USA), irrigation‐season management of a headwater reservoir is informed by a flow target in a management reach ~95 km downstream. The target is in place to meet irrigation demand and maintain aquatic habitat within the 11.4 km management reach and has undergone four flow target assignments from 1978 to 2021. Recent changes to irrigation‐season management to maximize reservoir carryover warranted investigation into the flow target assignment. Thus, we created a streamflow‐habitat model using hydraulic measurements, habitat unit mapping, and published habitat suitability criteria for Brown Trout (Salmo trutta), Rainbow Trout (Oncorhynchus mykiss), and Mountain Whitefish (Prosopium williamsoni). We used model output to compare habitat availability across two management regimes (1978–2017 and 2018–2021). We found that efforts to minimize reservoir releases in 2018–2021 did not reduce mean irrigation‐season fish habitat relative to natural flow, but did reduce overall fish habitat variability during the irrigation season compared to streamflow management in 1978–2017. Field observations for this research led to an adjusted flow target in 2020 that moved the target location downstream of intervening irrigation diversions. Using our model output, we demonstrated that moving the location of the target to account for local irrigation diversions will contribute to more consistently suitable fish habitat in the reach. Our study demonstrates the importance of site selection for establishing environmental flow targets.

Clonal Complexes Distribution of Staphylococcus aureus Isolates from Clinical Samples from the Caribbean Islands.

The aim of this study was to comprehensively characterise S. aureus from the Caribbean Islands of Trinidad and Tobago, and Jamaica. A total of 101 S. aureus/argenteus isolates were collected in 2020, mainly from patients with skin and soft tissue infections. They were characterised by DNA microarray allowing the detection of ca. 170 target genes and assignment to clonal complexes (CC)s and strains. In addition, the in vitro production of Panton-Valentine leukocidin (PVL) was examined by an experimental lateral flow assay. Two isolates were identified as S. argenteus, CC2596. The remaining S. aureus isolates were assigned to 21 CCs. The PVL rate among methicillin-susceptible S. aureus (MSSA) isolates was high (38/101), and 37 of the 38 genotypically positive isolates also yielded positive lateral flow results. The isolate that did not produce PVL was genome-sequenced, and it was shown to have a frameshift mutation in agrC. The high rate of PVL genes can be attributed to the presence of a known local CC8-MSSA clone in Trinidad and Tobago (n = 12) and to CC152-MSSA (n = 15). In contrast to earlier surveys, the USA300 clone was not found, although one MSSA isolate carried the ACME element, probably being a mecA-deficient derivative of this strain. Ten isolates, all from Trinidad and Tobago, were identified as MRSA. The pandemic ST239-MRSA-III strain was still common (n = 7), but five isolates showed a composite SCCmec element not observed elsewhere. Three isolates were sequenced. That showed a group of genes (among others, speG, crzC, and ccrA/B-4) to be linked to its SCC element, as previously found in some CC5- and CC8-MRSA, as well as in S. epidermidis. The other three MRSA belonged to CC22, CC72, and CC88, indicating epidemiological connections to Africa and the Middle East.