Formation control method based on artificial potential fields for aircraft flight simulation

Abstract

As simulation becomes more present in the military context for variate purposes, the need for accurate behaviors is of paramount importance. In the air domain, a noteworthy behavior relates to how a group of aircraft moves in a coordinated way. This can be defined as formation flying, which, combined with a move-to-goal behavior, is the focus of this work. The objective of the formation control problem considered is to ensure that simulated aircraft fly autonomously, seeking a formation, while moving toward a goal waypoint. For that, we propose the use of artificial potential fields, which reduce the complexities that implementing a complete cognition model could pose. These fields define forces that control the movement of the entities into formation and to the prescribed waypoint. Our formation control approach is parameterizable, allowing modifications that translate how the aircraft prioritize its sub-behaviors. Instead of defining this prioritization on an empirical basis, we elaborate metrics to evaluate the chosen parameters. From these metrics, we use an optimization methodology to find the best parameter values for a set of scenarios. Thus, our main contribution is bringing together artificial potential fields and simulation optimization to achieve more robust results for simulated military aircraft to fly in formation. We use a large set of scenarios for the optimization process, which evaluates its objective function through the simulations. The results show that the use of the proposed approach may generate gains of up to 27% if compared to arbitrarily selected parameters, with respect to one of the metrics adopted. In addition, we were able to observe that, for the scenarios considered, the presence of a formation leader was an obstacle to achieving the best results, demonstrating that our approach may lead to conclusions with direct operational impacts.

Research on path planning of ship avoiding typhoon based on improved artificial potential field method

“…q q q q q q q q U g n ( 7 ) In the 0 ≤ ρ(q, q 0 ) ≤ ρ 0 stage, the two parts of the repulsive function are F 21 and F 22 , The expression for F 21 is:…”

“…However, there are some problems with this method, such as falling into the local minimum, resulting in oscillation and the target can not be reached, etc. In order to solve these problems, some improved methods are proposed, such as introducing the tangent vector of the obstacle as the virtual force of the obstacle avoidance process [5] , proposing four alternative formulations for the repulsive field [6] , it also combines the artificial potential field method with simulation optimization [7] , the reinforcement learning with the artificial potential field method [8] . These methods can solve the problem of their local minima or unreachable target point, but there will be the problem of misjudging the obstacle environment or the problem of non-smooth paths and long paths, which will lead to the intersection of planned paths with obstacles and so on.…”

ASA: A Simulation Environment for Evaluating Military Operational Scenarios

“…The capabilities of AsaBatch already supported several academic studies [4,2,12,5,6,3,8] that used the execution of thousands of simulations to achieve their results. It is worth noting that the high individual performance of each simulation is mainly due to the capabilities of MIXR itself.…”

“…ASA has helped develop extensive academic work in the aerospace and defense context relating to different fields of study, such as artificial intelligence, machine learning, data science, and optimization. These applications can be seen in [4,2,12,5,6,3,8,9], being detailed as follows.…”

“…In [2,3], the authors proposed the use of both artificial potential fields and simulation optimization to achieve more robust results for simulated military aircraft to fly in formation, using a large set of scenarios for the optimization process, which evaluates its objective function through the simulations.…”