Abstract
In this article, a tree search algorithm is proposed to find the near optimal conflict avoidance solutions for unmanned aerial vehicles. In the dynamic environment, the unmodeled elements, such as wind, would make UAVs deviate from nominal traces. It brings about difficulties for conflict detection and resolution. The back propagation neural networks are utilized to approximate the unmodeled dynamics of the environment. To satisfy the online planning requirement, the search length of the tree search algorithm would be limited. Therefore, the algorithm may not be able to reach the goal states in search process. The midterm reward function for assessing each node is devised, with consideration given to two factors, namely, the safe separation requirement and the mission of each unmanned aerial vehicle. The simulation examples and the comparisons with previous approaches are provided to illustrate the smooth and convincing behaviours of the proposed algorithm.
Highlights
The applications of unmanned aerial vehicles (UAVs) in military and civilian fields have achieved great success in recent years
In section ‘‘Conflict detection and resolution analysis’’, we study on the conflict resolution problem and devise the midterm reward function
As the ultimate objective of conflict resolution is to return to the nominal paths and to minimize the influence to the air traffic, we propose to find a balance between safety and efficiency
Summary
The applications of unmanned aerial vehicles (UAVs) in military and civilian fields have achieved great success in recent years. To meet with the online planning requirement and adapt to the change of the environment, we propose to search conflict-free maneuvers in a limited time interval (lookahead interval) PN. Such a method requires that the optimization task is solved periodically to ensure that the moving look-ahead interval is always placed in the future. The multi-UAV conflict resolution problem is studied and the tree search midterm reward function is proposed in section ‘‘MultiUAV conflict resolution’’. The performance of our tree search algorithm is demonstrated by comparing it with existing algorithms in section ‘‘Simulation experiments.’’ Conclusions on our works are presented in ‘‘Conclusions’’ section
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