Rapid Penetration Path Planning Method for Stealth UAV in Complex Environment with BB Threats

Abstract

This paper presents the flight penetration path planning algorithm in a complex environment with Bogie or Bandit (BB) threats for stealth unmanned aerial vehicle (UAV). The emergence of rigorous air defense radar net necessitates efficient flight path planning and replanning for stealth UAV concerning survivability and penetration ability. We propose the improved A-Star algorithm based on the multiple step search approach to deal with this uprising problem. The objective is to achieve rapid penetration path planning for stealth UAV in a complex environment. Firstly, the combination of single-base radar, dual-base radar, and BB threats is adopted to different threat scenarios which are closer to the real combat environment. Besides, the multistep search strategy, the prediction technique, and path planning algorithm are developed for stealth UAV to deal with BB threats and achieve the penetration path replanning in complex scenarios. Moreover, the attitude angle information is integrated into the flight path which can meet real flight requirements for stealth UAV. The theoretical analysis and numerical results prove the validity of our method.

A Hierarchical Reinforcement Learning Framework for UAV Path Planning in Tactical Environments

“…TUAV path optimization under radar tracking threat has been explored by a number of researchers in non-RL settings [33,21,36,17,35,12,11]. The methods proposed by these researchers include Nonlinear Trajectory Generation (NTG) algorithm [11], Label Setting Algorithm (LSA) [33], 𝐴 * algorithm [21,35], numerical optimization procedure for a minimax optimal control, with moving average functional [12], Among the non-RL based solutions the one presented in [12] is utilized in our work since it integrates the model of the aircraft, a probabilistic model of a radar, and a behavioral approximation of missile subsystems based on the decision process for launching a SAM and the requirement to maintain tracking during missile flyout.…”

“…For an aircraft to be destroyed at time 𝑡, the radar system must have tracked it during the continuous interval [𝑡 − (𝑇 𝑟 + 𝑇 𝑓 ), 𝑡]. If Δ𝑇 = 𝑇 𝑟 + 𝑇 𝑓 is defined to be the threat window, then probability of kill is defined as: The UAV is represented using a kinematic model given in [12] and also adopted in [36], which account for the coupling between the RCS and aircraft dynamics. To be more specific, the turn rate of the aircraft is determined by its bank angle which in turn is determined by a steering input represented by 𝑢.…”

Path Planning of Unmanned Helicopter in Complex Environment Based on Heuristic Deep Q-Network

“…International Journal of Aerospace Engineering constraints [9]. Path planning algorithms are usually divided into global path planning algorithms and local path planning algorithms [10]. Among them, the global path planning algorithm requires that the environmental model is known, and the algorithm can generate the global optimal path according to the environmental constraints, and the representative one is the A * algorithm [11].…”

Optimal path planning with modified A-Star algorithm for stealth unmanned aerial vehicles in 3D network radar environment

“…[28][29][30] Therefore, a weighted factor ωðω > 1Þ was introduced into the heuristic function of the conventional A-Star algorithm to increase the algorithm search depth and ensure path optimization. 31 The heuristic function of the BML A-Star algorithm is given by f ðnÞ ¼ gðnÞ þ ωhðnÞ (13)…”