In recent years, reinforcement learning (RL) has been widely researched for fixed-wing aircraft control, offering transformative potential for more adaptive, efficient, and autonomous flight operations. This paper presents a comprehensive analysis of the current state, advantages, challenges, and future prospects of employing RL in fixed-wing aircraft control. The advantages of RL include model-free, adaptive decision-making, and effectively solving complex nonlinear constraints, leading to increased efficiency and maneuverability in high-dynamic environments. However, significant challenges, such as the guarantee of stability and optimization, interpretability, and sim-to-real transferring, highlight areas necessitating further attention and development. Overcoming these challenges requires collaborative efforts among aviation experts, control theory experts, and machine learning researchers to ensure safe, reliable integration of RL in flight systems. As research and advancements continue, the RL holds promise for providing a more efficient, maneuverable, and autonomous strategy for controlling fixed-wing aircraft in the future.

Reinforcement Learning Methods for Fixed-Wing Aircraft Control

A Technique of Distributed Missions Assignment Problem Solving in Heterogeneous Groups of Aerial Rescue Robots

Symmetry is an essential property in some dynamical systems that can be exploited for state transition prediction and control policy optimization. This paper develops two symmetry-integrated Reinforcement Learning (RL) algorithms based on standard Deep Deterministic Policy Gradient (DDPG),which leverage environment symmetry to augment explored transition samples of a Markov Decision Process(MDP). The firstly developed algorithm is named as Deep Deterministic Policy Gradient with Symmetric Data Augmentation (DDPG-SDA), which enriches dataset of standard DDPG algorithm by symmetric data augmentation method under symmetry assumption of a dynamical system. To further improve sample utilization efficiency, the second developed RL algorithm incorporates one extra critic network, which is independently trained with augmented dataset. A two-step approximate policy iteration method is proposed to integrate training for two critic networks and one actor network. The resulting RL algorithm is named as Deep Deterministic Policy Gradient with Symmetric Critic Augmentation (DDPG-SCA). Simulation results demonstrate enhanced sample efficiency and tracking performance of developed two RL algorithms in aircraft lateral tracking control task. 

pdf/deep-reinforcement-learning-with-symmetric-data-augmentation-5hylupm7uhm5.pdf

Deep reinforcement learning with symmetric data augmentation applied for
  aircraft lateral attitude tracking control

A fixed-wing aircraft can be in the final phase of a potential collision with a noncooperative dynamic obstacle (e.g., a drone) because of the limited sensing range. In the final phase of a potential collision, the performance of the existing obstacle avoidance approaches that do not take into account the bounded and nonisotropic maneuver capability and dynamic and aerodynamic characteristics of a fixed-wing aircraft is limited. To enhance the performance of fixed-wing aircraft in the final phase of a potential collision, this study develops a hierarchical reinforcement learning (RL)-based obstacle avoidance strategy. The RL-based obstacle avoidance strategy learns a high-level RL-based navigator that provides a velocity vector to avoid an obstacle and maintain the altitude, course, and airspeed of an aircraft as possible. The high-level RL-based navigator is combined with a low-level controller to guide and control the aircraft to avoid obstacles. To evaluate the RL-based obstacle avoidance strategy, the strategy is compared with a 3-D velocity obstacle (3DVO)-based obstacle avoidance strategy based on addressing dynamic obstacle avoidance problems of fixed-wing aircraft in a flight simulator. Experimental results show that for an aircraft with a sensing range of 1000 m, the RL-based obstacle avoidance strategy can achieve a success ratio of 92% in obstacle avoidance, whereas the 3DVO-based obstacle avoidance strategy can only achieve a success ratio of 60%.

Dynamic Obstacle Avoidance of Fixed-wing Aircraft in Final Phase via Reinforcement Learning

An increasing number of Autonomous Mobile Robots (AMRs) are used in warehouses and factories in recent years. The risk of some of the AMRs being out of control is surging. Although Reinforcement Learning (RL)-based approaches have achieved dramatic success in the motion planning of a large number of AMRs, the available RL-based motion planning approaches cannot provide a safety guarantee for the remaining functional AMRs if some of the AMRs are out of control. To this end, this paper develops a scalable Multi-agent RL (MARL) with Control Barrier Function (CBF)-based shields algorithm. The MARL with CBF-based shields algorithm can address complex high-level tasks by MARL and deal with the safety issue of every single functional AMR by a low-level CBF-based shield. A CBF-based shield is designed for every single functional AMR to ensure that the action of the functional AMR is safe, even if an uncontrollable AMR is pursuing the functional AMR. Experiments are conducted based on simulated warehouse environments to evaluate the effectiveness and scalability of a safe RL-based motion planning approach (The safe RL-based motion planning approach developed in this study is demonstrated in a video: https://youtu.be/I7ja5nFVpY4). developed according to the MARL with CBF-based shields algorithm.

Safe Reinforcement Learning-Based Motion Planning for Functional Mobile Robots Suffering Uncontrollable Mobile Robots

Obstacle avoidance is a crucial issue to enhance the safety of aircraft. Aircraft usually need to avoid a drop in altitude and keep a set course while avoiding an obstacle. In this paper, a hierarchical obstacle avoidance strategy is proposed to address obstacle avoidance, the drop in altitude, and course keeping simultaneously. The hierarchical obstacle avoidance strategy integrates a high-level reinforcement learning-based navigator and a low-level attitude controller. Experiments are conducted in X-Plane, a flight simulator, to evaluate the proposed hierarchical obstacle avoidance strategy.

Reinforcement Learning-based Hierarchical Obstacle Avoidance Strategy for Fixed-wing Aircraft

Onboard Operational Safety Filter for a Quadrotor in an Environment With Dynamic Obstacles

Researchers have made many attempts to apply reinforcement learning (RL) to learn to fly aircraft in recent years. However, existing RL strategies are usually not safe (e.g., can lead to crash) in the initial stage of the training of an RL-based policy. For increasingly complex piloting tasks whose representative models are hard to establish, it is not safe to apply the existing RL strategies to learn an RL-based policy by interacting with an aircraft. To enhance the safety and feasibility of applying an RL-based policy to an aircraft, this study develops an offline–online RL strategy. The offline–online RL strategy learns an effective initialization for an RL-based flight control policy from human pilots without interacting with an aircraft through offline RL. Then, the offline–online RL strategy can further improve the RL-based flight control policy safely without leading to crash by interacting with the aircraft according to regular online RL, requiring no or very little intervention performed by a human pilot. To demonstrate and evaluate the offline–online RL strategy, the strategy is utilized to address the stall recovery problem of aircraft based on a flight simulator.

Safely Learn to Fly Aircraft From Human: An Offline–Online Reinforcement Learning Strategy and Its Application to Aircraft Stall Recovery

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