Multi-UAV cooperative reconnaissance mission planning novel method under multi-radar detection

Abstract

Past swarm intelligence algorithms for solving UAV path planning problems have suffered from slow convergence, lack of complex constraints and guidance for local optimisation. It no longer meets the requirements of the Multi-UAV Cooperative Reconnaissance Mission Planning (MUCRMP) problem in the context of multi-radar detection. In this paper, a global optimisation model with the objective of a shorter distance within radar detection range of the UAV is proposed at first, including the planning of reconnaissance sequence between and within target groups, relative position to targets. More importantly, the imaging characteristics of the UAV and its minimum turning radius have been considered in depth in this study. Then an improved synthetic heuristic algorithm is proposed to solve the model, which obtains valuable reconnaissance mission plan. Finally, an example solution for a problem with 68 target point sizes is carried out, and the validity and feasibility of the model and algorithm are illustrated through the analysis given. Compared with the existing algorithms, the improved synthetic heuristic algorithm can give better anti-radar attributes to the UAV and efficiently improved the convergence speed in the specific reconnaissance mission.

UAV Formation Trajectory Planning Algorithms: A Review

“…However, there are some problems with a single drone performing its mission. For example, when a single UAV performs a reconnaissance mission, it may be limited by the observation angle and cannot observe the target area from multiple different orientations [5]; when faced with a large-scale search task, a single UAV cannot effectively cover the entire reconnaissance area [6]; during the attack, the combat range, killing radius, destruction capability and attack accuracy are limited, thus affecting the success rate of the entire combat mission [7]; if a single drone fails in the middle of a mission, it must immediately interrupt the mission and return, but in a war, it may delay the aircraft and destroy the entire operation plan. In order to improve combat effectiveness and make up for the deficiency of a single UAV, a multi-UAV cooperative formation (cluster) combat task is proposed.…”

Starling-Behavior-Inspired Flocking Control of Fixed-Wing Unmanned Aerial Vehicle Swarm in Complex Environments with Dynamic Obstacles

“…In recent years, the unmanned aerial vehicle (UAV) has become widely used in military and civilian fields such as agriculture by virtue of the development of autonomy [ 15 , 16 , 17 ]. Compared with a single UAV, multiple UAVs are able to execute missions such as surveillance and reconnaissance simultaneously, which greatly improves execution efficiency [ 18 ]. Obstacles are one of the biggest threats to flight for multi-agent systems when executing missions.…”