Inverse Reinforcement Learning-Based Fire-Control Command Calculation of an Unmanned Autonomous Helicopter Using Swarm Intelligence Demonstration

Abstract

This paper concerns the fire-control command calculation (FCCC) of an unmanned autonomous helicopter (UAH). It determines the final effect of the UAH attack. Although many different FCCC methods have been proposed for finding optimal or near-optimal fire-control execution processes, most are either slow in calculational speed or low in attack precision. This paper proposes a novel inverse reinforcement learning (IRL) FCCC method to calculate the fire-control commands in real time without losing precision by considering wind disturbance. First, the adaptive step velocity-verlet iterative algorithm-based ballistic determination method is proposed for calculation of the impact point of the unguided projectile under wind disturbance. In addition, a swarm intelligence demonstration (SID) model is proposed to demonstrate teaching; this model is based on an improved particle swarm optimization (IPSO) algorithm. Benefiting from the global optimization capability of the IPSO algorithm, the SID model often leads to an exact solution. Furthermore, a reward function neural network (RFNN) is trained according to the SID model, and a reinforcement learning (RL) model using RFNN is used to generate the fire-control commands in real time. Finally, the simulation results verify the feasibility and effectiveness of the proposed FCCC method.

Study on the Glider Soaring Strategy in Random Location Thermal Updraft via Reinforcement Learning

“…In addition, the intelligence and autonomy of UAVs is also important to consider for future development. With the development of computer algorithms, processing capabilities, and other technologies, researchers have made great progress in mission planning, strategy decisions, trajectory control, and other aspects with regards to UAVs [9,10]. The control mode of UAVs has also been improved from semi-autonomous forms, such as pre-programming and pre-planning, to fully autonomous and intelligent forms.…”

“…Equation (10) shows that the drag is the main factor of energy dissipation, and the thermal updraft can import energy for the glider. The higher the wind speed of the thermal updraft is, the greater the energy gain.…”