The aviation industry rapidly embraces artificial intelligence (AI) to improve efficiency, safety, and passenger experience. This article deliberates on the growing importance of AI, specifically reinforcement learning (RL), in the aviation industry.
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Importance of AI in Aviation
Air travel is one of the most popular transportation modes, specifically for long distances worldwide with global air traffic rising rapidly in recent years. The number of air travelers will increase significantly in the upcoming decades. However, the aviation industry is also facing many challenges, including a rise in air pollution, system complexity, extreme competition, and stringent environmental restrictions, which are affecting their regular operations and expansion.
AI technologies can mitigate these challenges and transform the aviation sector by removing duplication, shortening design processes, and reducing costs. Leveraging AI to vastly improve flight operations and experience benefits airline corporations and customers. Predictive maintenance, pattern recognition, targeted advertising, customer feedback analysis, and automatic scheduling are the other key benefits of AI in the aviation industry.
Applications of AI
Product designing and operation: The demand for robust and economical airplane parts is increasing with the continued expansion of the aviation sector. This necessitates the development of innovative and effective approaches for designing airplane parts. AI optimization assists the design and manufacturing processes in the aviation sector.
For instance, AI-powered dynamic design and three-dimensional printing can be utilized to obtain the most suitable design for components like propellers and wings. Size is a crucial aspect that must be factored in during aircraft design. However, errors were unavoidable in conventional mathematical modeling techniques based on theoretical approaches as these approaches are not established in reality. Machine learning models can be developed to address this issue as they bring concepts much closer to reality.
Higher Fuel Efficiency: In the aviation industry, reducing fuel usage is advantageous for both the environment and airline operators, as fuel accounts for a significant share of aircraft operating costs. AI-powered devices minimize the amount of fuel consumed by airplanes.
For instance, Air Alaska saved 1,820,000 gallons of fuel within six months and prevented 4,600 tons of carbon emissions using the software Flyways for flight planning. Flyways, an AI-enabled operating system, accelerates and improves decisions for operators across different roles in the aviation sector.
The system combines unstructured data, generates a predictive operating picture by applying AI, and suggests contextually relevant actions to human operators. Similarly, a machine learning tool Safety Line has been developed that can assist pilots in optimizing their ascent trajectories before every trip, which saves a significant amount of fuel.
Air Traffic Management: Effective air traffic management is critical to prevent catastrophic mishaps like aircraft collisions. AI and machine learning models are suitable for air traffic control operations that are highly complex. They can be used for traffic detection and pilot assistance in practical scenarios. Specifically, pilots can use AI assistants to make judgments using flying data and weather data from sensors.
During extreme/severe weather, these AI assistants can provide information about alternate flight paths to the pilots. Moreover, AI can detect planes when they are on the same flight path, which allows air traffic controllers to alert pilots by telling them to change their current altitude. Air traffic controllers also oversee planes approaching or leaving the runway using smart cameras and AI.
Identification of Different Risks: AI enables aircraft real-time health monitoring systems, which are used to scan and test the entire aircraft to prevent unanticipated catastrophes. Security remains one of the major concerns in the aviation industry. AI is used at airports to ensure passenger safety using facial recognition to identify suspect individuals.
Data on individuals with criminal records are used for training the AI systems. The operational efficiency on the ground will improve as the passenger identification procedure accelerates due to the advancements in security scanning, machine learning, and biometric identification. In the United States, airports routinely utilize AI to identify security risks.
Similarly, the AI platform Syntech ONE at the Osaka Airport in Japan filters bags for many conveyor belts. Combining this AI platform with X-ray security system enhances threat detection. It will also significantly reduce the strain on airport security staff by enabling them to detect illegal items more accurately and swiftly.
Customer Experience: Client service quality and satisfaction are crucial aspects of commercial aviation. AI can be employed to deliver first-rate customer care and enhance customer engagement. Additionally, up-to-date machine learning-based apps can assist airlines and airports in predicting delays and swiftly informing passengers about such delays, which greatly enhances user experience by providing customers additional time to make alternate reservations or reschedule flights.
Role of RL in Aviation Industry
RL offers a learning-based, data-driven framework to solve and formulate sequential decision-making problems. The RL framework has gained popularity in the aviation industry due to improved computing power and data availability. Several aviation-based applications are treated or formulated as sequential decision-making problems.
Separation Assurance and Collision Avoidance
The next-generation airborne collision avoidance system (ACAS-X) has been built upon the traffic alert and collision avoidance system (TCAS) by introducing a partially observable Markov decision process (MDP) for the problem formulation. ACAS-X provides visual and audible warnings to pilots by assessing the time to closest approach, which determines the possibility of a collision.
MDP-based collision avoidance in free-flight airspace can avoid collisions between aircraft in a three-dimensional environment with both non-cooperative and cooperative aircraft. Similarly, a deep reinforcement learning (DRL) method has been implemented to optimize a collision avoidance problem.
Reward decomposition and deep Q-Learning from demonstrations (DQfD) were developed to provide interpretable solutions for aircraft collision avoidance. A framework using GPS waypoints and RL has been introduced in a study to avoid collisions. A double deep Q-network (DDQN) has been leveraged to guide an aircraft through terminal sectors without colliding with other aircraft.
This approach is specifically suitable for tackling cases where conventional collision avoidance methods are ineffective, like in dense airspace, as it provides reasonable corrections to ensure adequate safety among aircraft. Proximal policy optimization (PPO) methods are used extensively in aircraft collision avoidance. The collision avoidance problem in structured airspace using PPO networks has been resolved using attention networks and a long short-term memory (LSTM) network for handling a variable number of aircraft.
Although these PPO models demonstrate good performance within the training environment, a slight alteration in the evaluation environment can reduce the performance of these algorithms. An execution-time data augmentation and Monte Carlo Dropout-based safety module has been proposed to address the collision avoidance problem in environments different from training environments.
Moreover, a message-passing network has been developed to support collision avoidance, while a physics-informed DRL algorithm was built for aircraft collision avoidance using prior physical information of airplanes. A study proposed a reward engineering approach to assist the PPO network in addressing aircraft collision avoidance problems in two-dimensional airspace. Many studies have utilized deep deterministic policy gradient (DDPG) for aircraft collision avoidance issues. For instance, a DRL method has been applied for two-aircraft conflict resolution in the presence of uncertainty based on DDPG.
Aircraft Flight and Attitude Control
Attitude control of an aircraft is challenging owing to the system's uncertainties, nonlinearities, and noises acting upon the system. Recently, advanced RL-based controllers have been developed for flat spin recovery, visual servoing, stabilization, vision-based landing, single/multi-agent obstacle avoidance, and target tracking. Studies have demonstrated the feasibility of directly training a controller using RL based on an unknown or nonlinear model. For instance, a DQN method has been utilized to design attitude control systems for aircraft.
A comparative performance analysis of controllers based on various RL algorithms showed that a DQN is more effective for discrete tasks compared to a DDPG or policy gradient, while a DDPG is more suitable for tasks with high complexity. Similarly, an improved DDPG method was integrated with transfer learning, and a control system was built to track autonomous maneuvering targets.
Overall, AI is transforming the aviation industry by improving efficiency and safety while enhancing customer experience. However, challenges like safety and reliability must be mitigated, and the privacy of passengers must be ensured for the widespread AI implementation in the aviation industry.
References and Further Reading
Razzaghi, P., Tabrizian, A., Guo, W., Chen, S., Taye, A., Thompson, E., Bregeon, A., Baheri, A., Wei, P. (2022). A Survey on Reinforcement Learning in Aviation Applications. ArXiv. https://doi.org/10.48550/arXiv.2211.02147
Abubakar, M., EriOluwa, O., Teyei, M., Al-Turjman, F. (2022). AI Application in the Aviation Sector. 2022 International Conference on Artificial Intelligence of Things and Crowdsensing (AIoTCs), 52-55. https://doi.org/10.1109/AIoTCs58181.2022.00015
Kumar, M. (2022). Optimized application of artificial intelligence (AI) in aviation market. International Journal of Recent Research Aspects, 9(4). https://www.academia.edu/98890537/Optimized_application_of_artificial_intelligence_AI_in_aviation_market#:~:text.
FLYWAYS [Online] Available at https://www.airspace-intelligence.com/flyways-platform (Accessed on 18 March 2024)