Route planning method design for UAV under radar ECM scenario

Duan, Yuanwang; Ji, Xiaoyu; Li, Manling; Li, Yunjie

doi:10.1109/icosp.2014.7014979

Cited by 8 publications

(5 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With the rapid development of artificial intelligence technology, unmanned aerial vehicle (UAV) is more and more widely used, whether in aerial photography, plant protection, remote sensing surveying and mapping in the civil field, or in reconnaissance, electronic jamming and strike in the military field [1][2][3][4][5][6][7][8][9]. However, the primary link to complete the task is to plan the flight route of UAV.…”

Section: Introductionmentioning

confidence: 99%

Chaotic Random Opposition-Based Learning and Cauchy Mutation Improved Moth-Flame Optimization Algorithm for Intelligent Route Planning of Multiple UAVs

Shi

et al. 2022

IEEE Access

View full text Add to dashboard Cite

UAV route planning is the key issue for application of UAV in real-world scenarios. Compared with the traditional route planning methods, although the intelligent optimization algorithm has stronger applicability and optimization performance, it also has the problem of poor convergence accuracy and easy to fall into local optimization. Therefore, an intelligent route planning method for UAV based on chaotic random opposition-based learning and cauchy mutation improved Moth-flame optimization algorithm (OLTC-MFO) is proposed. First, the terrain environment is constructed by digital elevation map, and the threat model is established to realize the equivalent three-dimensional (3D) environment. Then, the opposite population is introduced to increase the diversity of solutions and improve the search speed of the algorithm. Then, the Logistic-Tent chaos map is introduced to realize random perturbation of flame position, which improves the global search capability of the algorithm. Finally, the probability operator and Cauchy mutation operator are introduced, which makes the algorithm not only accept the current solution with a certain probability, but also jump out of the current sub-optimal solution, thus enhancing the global search capability of the algorithm. The simulation results show that when the number of iterations is 1000, the length of route planning based on OLTC-MFO algorithm is 45.3716km shorter than MFO algorithm, and the convergence result of this method is stable and more accurate, which achieves the purpose of assisting UAV combat decision-making.

show abstract

Section: Introductionmentioning

confidence: 99%

Chaotic Random Opposition-Based Learning and Cauchy Mutation Improved Moth-Flame Optimization Algorithm for Intelligent Route Planning of Multiple UAVs

Shi

et al. 2022

IEEE Access

View full text Add to dashboard Cite

show abstract

“…W ITH the rapid development of unmanned aerial vehicle (UAV) technology, UAVs have been widely used in military wars in recent years. The characteristics of UAVs, such as low cost, strong survivability and high operational efficiency, make them perform well in executing tasks such as intelligence, surveillance, and reconnaissance [1], [2], electronic countermeasures [3], [4], and ground attacks [5], [6]. To accomplish these tasks successfully, UAVs usually need to achieve autonomous motion control (AMC) in complex and changeable unknown environments.…”

Section: Introductionmentioning

confidence: 99%

Asynchronous Curriculum Experience Replay: A Deep Reinforcement Learning Approach for UAV Autonomous Motion Control in Unknown Dynamic Environments

Hu¹,

Gao²,

Wan³

et al. 2022

Preprint

View full text Add to dashboard Cite

Unmanned aerial vehicles (UAVs) have been widely used in military warfare. In this paper, we formulate the autonomous motion control (AMC) problem as a Markov decision process (MDP) and propose an advanced deep reinforcement learning (DRL) method that allows UAVs to execute complex tasks in large-scale dynamic three-dimensional (3D) environments. To overcome the limitations of the prioritized experience replay (PER) algorithm and improve performance, the proposed asynchronous curriculum experience replay (ACER) uses multithreads to asynchronously update the priorities, assigns the true priorities and applies a temporary experience pool to make available experiences of higher quality for learning. A firstin-useless-out (FIUO) experience pool is also introduced to ensure the higher use value of the stored experiences. In addition, combined with curriculum learning (CL), a more reasonable training paradigm of sampling experiences from simple to difficult is designed for training UAVs. By training in a complex unknown environment constructed based on the parameters of a real UAV, the proposed ACER improves the convergence speed by 24.66% and the convergence result by 5.59% compared to the state-ofthe-art twin delayed deep deterministic policy gradient (TD3) algorithm. The testing experiments carried out in environments with different complexities demonstrate the strong robustness and generalization ability of the ACER agent.

show abstract

“…Although these algorithms promise to find the most optimal solution, they are impossible to be implemented in many real world applications due to the amount of computer memory and computational time required [5]. To address these issues, many heuristic methods for trajectory planning have been proposed such as artificial potential field (APF), Genetic Algorithm (GA) [6], fuzzy logic [7,8], Ant Colony Algorithm, Particle Swarm Optimization [9], and Gray Wolf Optimization [10].…”

Section: Introductionmentioning

confidence: 99%

“…The fundamental issue with these methods is that they cannot guarantee the optimal solution. These methods are advantageous because they require less time and computational effort to solve trajectory planning problems [5]. Real-world applications require very fast algorithms that can handle the complexity of the large-scale problem [11].…”

Section: Introductionmentioning

confidence: 99%

Partial Differential Equation-Based Trajectory Planning for Multiple Unmanned Air Vehicles in Dynamic and Uncertain Environments

Radmanesh

Kumar

French

2020

Journal of Dynamic Systems, Measurement, and Control

View full text Add to dashboard Cite

This paper proposes a physics-inspired method for unmanned aerial vehicle (UAV) trajectory planning in three dimensions using partial differential equations (PDEs) for application in dynamic hostile environments. The proposed method exploits the dynamical property of fluid flowing through a porous medium. This method evaluates risk to generate porosity values throughout the computational domain. The trajectory that encounters the highest porosity values determines the trajectory from the point of origin to the goal position. The best trajectory is found using the reaction of the fluid in porous media by the way of streamlines obtained by numerically solving the PDEs representing the fluid flow. Constraints due to UAV dynamics, obstacles, and predefined way points are applied to the problem after solving for the best trajectory to find the optimal and feasible trajectory. This method shows near-optimality and much reduced computational effort when compared to the other typical numerical optimization methods.

show abstract

Route planning method design for UAV under radar ECM scenario

Cited by 8 publications

References 4 publications

Chaotic Random Opposition-Based Learning and Cauchy Mutation Improved Moth-Flame Optimization Algorithm for Intelligent Route Planning of Multiple UAVs

Chaotic Random Opposition-Based Learning and Cauchy Mutation Improved Moth-Flame Optimization Algorithm for Intelligent Route Planning of Multiple UAVs

Asynchronous Curriculum Experience Replay: A Deep Reinforcement Learning Approach for UAV Autonomous Motion Control in Unknown Dynamic Environments

Partial Differential Equation-Based Trajectory Planning for Multiple Unmanned Air Vehicles in Dynamic and Uncertain Environments

Contact Info

Product

Resources

About