Multiple Unmanned Combat Aerial Vehicles Research Articles

Three-dimensional collaborative path planning for multiple UCAVs based on improved artificial ecosystem optimizer and reinforcement learning

This study proposes a multi-strategy evolutionary artificial ecosystem optimizer based on reinforcement learning (MEAEO-RL) to tackle the collaborative path-planning problem of multiple unmanned combat aerial vehicles (UCAVs) in complex environments with multiple constraints. The objective is to generate optimal candidate paths for each UCAV, ensuring they reach the destination simultaneously while considering time variables and obstacle avoidance. To overcome the limitations of the standard artificial ecosystem optimizer (AEO), such as local optimality and slow convergence, a learning framework inspired by brain-like perception is constructed. This framework enhances swarm agents with greater intelligence by fusing swarm intelligence and human cognitive mechanisms. Meanwhile, a multi-strategy database is implemented within the evolutionary learning framework to replace the single-update method of the AEO during the consumption phase. To reduce the computational complexity of the algorithm, agents in the consumption stage utilize experience accumulation from reinforcement learning to select an effective update strategy for obtaining the latest consumer location. Path-planning simulation experiments are conducted in a series of complex three-dimensional environments, demonstrating the algorithm’s robustness, improved convergence accuracy, and ability to plan collaborative paths for multiple UCAVs while satisfying various constraints.

A Multi-UCAV Cooperative Decision-Making Method Based on an MAPPO Algorithm for Beyond-Visual-Range Air Combat

To solve the problems of autonomous decision making and the cooperative operation of multiple unmanned combat aerial vehicles (UCAVs) in beyond-visual-range air combat, this paper proposes an air combat decision-making method that is based on a multi-agent proximal policy optimization (MAPPO) algorithm. Firstly, the model of the unmanned combat aircraft is established on the simulation platform, and the corresponding maneuver library is designed. In order to simulate the real beyond-visual-range air combat, the missile attack area model is established, and the probability of damage occurring is given according to both the enemy and us. Secondly, to overcome the sparse return problem of traditional reinforcement learning, according to the angle, speed, altitude, distance of the unmanned combat aircraft, and the damage of the missile attack area, this paper designs a comprehensive reward function. Finally, the idea of centralized training and distributed implementation is adopted to improve the decision-making ability of the unmanned combat aircraft and improve the training efficiency of the algorithm. The simulation results show that this algorithm can carry out a multi-aircraft air combat confrontation drill, form new tactical decisions in the drill process, and provide new ideas for multi-UCAV air combat.

A Multi-UCAV cooperative occupation method based on weapon engagement zones for beyond-visual-range air combat

Recent advances in on-board radar and missile capabilities, combined with individual payload limitations, have led to increased interest in the use of unmanned combat aerial vehicles (UCAVs) for cooperative occupation during beyond-visual-range (BVR) air combat. However, prior research on occupational decision-making in BVR air combat has mostly been limited to one-on-one scenarios. As such, this study presents a practical cooperative occupation decision-making methodology for use with multiple UCAVs. The weapon engagement zone (WEZ) and combat geometry were first used to develop an advantage function for situational assessment of one-on-one engagement. An encircling advantage function was then designed to represent the cooperation of UCAVs, thereby establishing a cooperative occupation model. The corresponding objective function was derived from the one-on-one engagement advantage function and the encircling advantage function. The resulting model exhibited similarities to a mixed-integer nonlinear programming (MINLP) problem. As such, an improved discrete particle swarm optimization (DPSO) algorithm was used to identify a solution. The occupation process was then converted into a formation switching task as part of the cooperative occupation model. A series of simulations were conducted to verify occupational solutions in varying situations, including two-on-two engagement. Simulated results showed these solutions varied with initial conditions and weighting coefficients. This occupation process, based on formation switching, effectively demonstrates the viability of the proposed technique. These cooperative occupation results could provide a theoretical framework for subsequent research in cooperative BVR air combat.

Combining an Extended SMAA-2 Method with Integer Linear Programming for Task Assignment of Multi-UCAV under Multiple Uncertainties

Uncertainty should be taken into account when establishing multiobjective task assignment models for multiple unmanned combat aerial vehicles (UCAVs) due to errors in the target information acquired by sensors, implicit preferences of the commander for operational objectives, and partially known weights of sensors. In this paper, we extend the stochastic multicriteria acceptability analysis-2 (SMAA-2) method and combine it with integer linear programming to achieve multiobjective task assignment for multi-UCAV under multiple uncertainties. We first represent the uncertain target information as normal distribution interval numbers so that the values of criteria (operational objectives) concerned can be computed based on the weighted arithmetic averaging operator. Thus, we obtain multiple criteria value matrices for each UCAV. Then, we propose a novel aggregation method to generate the final criteria value matrix based on which the holistic acceptability indices are computed by the extended SMAA-2 method. On this basis, we convert the task assignment model with uncertain parameters into an integer linear programming model without uncertainty so as to implement task assignment using the integer linear programming method. Finally, we conduct a case study and demonstrate the feasibility of the proposed method in solving the multiobjective task assignment problem multi-UCAV under multiple uncertainties.

A predator-prey particle swarm optimization approach to multiple UCAV air combat modeled by dynamic game theory

Dynamic game theory has received considerable attention as a promising technique for formulating control actions for agents in an extended complex enterprise that involves an adversary. At each decision making step, each side seeks the best scheme with the purpose of maximizing its own objective function. In this paper, a game theoretic approach based on predatorprey particle swarm optimization (PP-PSO) is presented, and the dynamic task assignment problem for multiple unmanned combat aerial vehicles (UCAVs) in military operation is decomposed and modeled as a two-player game at each decision stage. The optimal assignment scheme of each stage is regarded as a mixed Nash equilibrium, which can be solved by using the PP-PSO. The effectiveness of our proposed methodology is verified by a typical example of an air military operation that involves two opposing forces: the attacking force Red and the defense force Blue.

Real-time decentralized cooperative robust trajectory planning for multiple UCAVs air-to-ground target attack

Planning cooperative trajectories in real time is of great importance for multiple unmanned combat aerial vehicles (multi-UCAV) in performing autonomous time-critical cooperative air-to-ground target attack missions. The models of the vehicle, constraints, and a multi-criteria objective function are set up, and then the problem is formulated as a decentralized cooperative receding horizon optimal control problem. An elaborate framework for provable effective decentralized cooperative trajectory planning in two-degree-of-freedom decentralized receding horizon control is presented to provide the robustness and adaptivity for trajectory planning in dynamic and uncertain environments. Furthermore, a two-stage iterative optimization algorithm is developed to solve the problem. The novel algorithm integrates the differential flatness theory to generate trajectories, and the coordination variable-based strategy to coordinate the arrival time of multi-UCAV online, which can improve the computational efficiency and achieve the cooperative objective. The results of numerical simulations show that the proposed approach is feasible and well suited for the real-time implementation in dynamic and uncertain environment.

Collision-free Multiple Unmanned Combat Aerial Vehicles Cooperative Trajectory Planning for Time-critical Missions using Differential Flatness Approach

This paper investigates the cooperative trajectory planning for multiple unmanned combat aerial vehicles in performing autonomous cooperative air-to-ground target attack missions. Firstly, the collision-free cooperative trajectory planning problem for time-critical missions is formulated as a cooperative trajectory optimal control problem (CTP-OCP), which is based on an approximate allowable attack region model, several constraints model, and a multi-criteria objective function. Next, a planning algorithm based on the differential flatness, B-spline curves and nonlinear programming is designed to solve the CTP-OCP. In particular, the notion of the virtual time is introduced to deal with the temporal constraints. Finally, the proposed approach is validated by two typical scenarios and the simulation results show the feasibility and effectiveness of the proposed planning approach. Defence Science Journal, Vol. 64, No. 1, January 2014, DOI:10.14429/dsj.64.2999

A Virtual Motion Camouflage Approach for Cooperative Trajectory Planning of Multiple UCAVs

This paper investigates cooperative trajectory planning of multiple unmanned combat aerial vehicles (multi-UCAV) in performing autonomous cooperative air-to-ground target attack missions. By integrating an approximate allowable attack region model, several constraint models, and a multicriterion objective function, the problem is formulated as a cooperative trajectory optimal control problem (CTOCP). Then, a virtual motion camouflage (VMC) for cooperative trajectory planning of multi-UCAV, combining with the differential flatness theory, Gauss pseudospectral method (GPM), and nonlinear programming, is designed to solve the CTOCP. In particular, the notion of the virtual time is introduced to the VMC problem formulation to handle the temporal cooperative constraints. The simulation experiments validate that the CTOCP can be effectively solved by the cooperative trajectory planning algorithm based on VMC which integrates the spatial and temporal constraints on the trajectory level, and the comparative experiments illustrate that VMC based algorithm is more efficient than GPM based direct collocation method in tackling the CTOCP.

Path planning for multiple unmanned combat aerial vehicles based on improved artificial bee colony algorithm

Path planning for multiple unmanned combat aerial vehicles based on improved artificial bee colony algorithm

Cooperative Trajectory Planning for Multi-UCAV Performing Air-to-Ground Target Attack Missions

The problem of planning flight trajectories is studied for multiple unmanned combat aerial vehicles (UCAVs) performing a cooperated air-to-ground target attack (CA/GTA) mission. Several constraints including individual and cooperative constraints are modeled, and an objective function is constructed. Then, the cooperative trajectory planning problem is formulated as a cooperative trajectory optimal control problem (CTP-OCP). Moreover, in order to handle the temporal constraints, a notion of the virtual time based strategy is introduced. Afterwards, a planning algorithm based on the differential flatness theory and B-spline curves is developed to solve the CTP-OCP. Finally, the proposed approach is demonstrated using a typical CA/GTA mission scenario, and the simulation results show that the proposed approach is feasible and effective.