Abstract
A solution framework for UAV motion strategies in uncertain dynamic environments is constructed in this paper. Considering that the motion states of UAV might be influenced by some dynamic uncertainties, such as control strategies, flight environments, and any other bursting-out threats, we model the uncertain factors that might cause such influences to the path planning of the UAV, unified as an unobservable part of the system and take the acceleration together with the bank angle of the UAV as a control variable. Meanwhile, the cost function is chosen based on the tracking error, then the control instructions and flight path for UAV can be achieved. Then, the cost function can be optimized through Q-learning, and the best UAV action sequence for conflict avoidance under the moving threat environment can be obtained. According to Bellman’s optimization principle, the optimal action strategies can be obtained from the current confidence level. The method in this paper is more in line with the actual UAV path planning, since the generation of the path planning strategy at each moment takes into account the influence of the UAV control strategy on its motion at the next moment. The simulation results show that all the planning paths that are created according to the solution framework proposed in this paper have a very high tracking accuracy, and this method has a much shorter processing time as well as a shorter path it can create.
Highlights
It is not easy for an unmanned aerial vehicle (UAV) to fly autonomously in uncertain, dynamic environments; this concerns the safety of the UAV itself as well as that of the air traffic controllers
A dynamic path planning solution framework for UAV constructed based on Q-learning theory is suitable, and in this solution framework, we consider the effects of the uncertainty environment along with the optimal control strategy and the dynamics of the UAV together during the path planning
Q-learning theory and a path planning model based on Q-learning strategy are described in Section 3; a path planning solving framework for UAV based on Q-learning strategy is constructed
Summary
It is not easy for an unmanned aerial vehicle (UAV) to fly autonomously in uncertain, dynamic environments; this concerns the safety of the UAV itself as well as that of the air traffic controllers. A dynamic path planning solution framework for UAV constructed based on Q-learning theory is suitable, and in this solution framework, we consider the effects of the uncertainty environment along with the optimal control strategy and the dynamics of the UAV together during the path planning. Q-learning theory and a path planning model based on Q-learning strategy are described in Section 3; a path planning solving framework for UAV based on Q-learning strategy is constructed In this framework, the uncertainty owning to the environment is considered as well as the effect of the movement changing after the UAV adopted the planned control strategies.
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