Abstract
A deep reinforcement learning-based computational guidance method is presented, which is used to identify and resolve the problem of collision avoidance for a variable number of fixed-wing UAVs in limited airspace. The cooperative guidance process is first analyzed for multiple aircraft by formulating flight scenarios using multiagent Markov game theory and solving it by machine learning algorithm. Furthermore, a self-learning framework is established by using the actor-critic model, which is proposed to train collision avoidance decision-making neural networks. To achieve higher scalability, the neural network is customized to incorporate long short-term memory networks, and a coordination strategy is given. Additionally, a simulator suitable for multiagent high-density route scene is designed for validation, in which all UAVs run the proposed algorithm onboard. Simulated experiment results from several case studies show that the real-time guidance algorithm can reduce the collision probability of multiple UAVs in flight effectively even with a large number of aircraft.
Highlights
With the rapid development of unmanned aerial vehicle (UAV) technology, fixed-wing UAVs have been playing an increasingly important role in both modern life and military affairs [1, 2]
Due to the technical characteristics of fixedwing UAVs such as unable to hover and limited speed control range, it is easy to cause safety accidents which can lead to property losses and even personal injuries in environments with high route density [3]. erefore, a large number of fixed-wing UAV flight conflicts in the given airspace have become a prominent problem that needs to be solved in related fields [4]. e pilot of a conventional aircraft can judge the distance to other aircraft by visual inspection and make collision avoidance operations timely [5]
In order to simplify the complexity of the guidance problem, this paper only considers horizontal actions in the process of controlling the UAVs. is assumption makes it possible to deal with the high-density air traffic of UAVs by distinguishing the multiple flight altitudes. is assumption can effectively limit the range of state space, which is beneficial to reinforcement learning system training
Summary
With the rapid development of unmanned aerial vehicle (UAV) technology, fixed-wing UAVs have been playing an increasingly important role in both modern life and military affairs [1, 2]. Many recent research studies for limited airspace operations, such as the unmanned aircraft system traffic management (UTM) [7] and air traffic management (ATM) [8], require an autonomous collision avoidance guidance system to maintain safety and efficiency. One of the most well-known works in air traffic control is the Autoresolver program developed by NASA scientists [10]. Kuchar and his colleagues presented a comprehensive summary of more than 30 different methods for flight conflict resolution, in which the key methods such as artificial potential field approach, biological evolution method, and optimization algorithm are given in detail [11]. Yang and Wei presented a message-based decentralized computational guidance algorithm which is implemented by the Monte Carlo tree search technique [13]. eir work
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