Abstract
Unmanned aerial vehicles (UAVs) are playing an increasingly important role in people’s daily lives due to their low cost of operation, low requirements for ground support, high maneuverability, high environmental adaptability, and high safety. Yet UAV path planning under various safety risks, such as crash and collision, is not an easy task, due to the complicated and dynamic nature of path environments. Therefore, developing an efficient and flexible algorithm for UAV path planning has become inevitable. Aimed at quality-oriented UAV path planning, this paper is designed to analyze UAV path planning from two aspects: global static planning and local dynamic hierarchical planning. Through a theoretical and mathematical approach, a three-dimensional UAV path planning model was established. Based on the A* algorithm, the search strategy, the step size, and the cost function were improved, and the OPEN set was simplified, thereby shortening the planning time and greatly improving the execution efficiency of the algorithm. Moreover, a dynamic exploration factor was added to the exploration mechanism of Q-learning to solve the exploration-exploitation dilemma of Q-learning to adapt to the local dynamic path adjustment for UAVs. The global-local hybrid UAV path planning algorithm was formed by combining the two. The simulation results indicate that the proposed planning model and algorithm can efficiently solve the problem of UAV path planning, improve the path quality, and can be a significant reference for solving other problems related to path planning, such as the reliability, security, and safety of UAV, when embedded into the heuristic function of the proposed algorithm.
Highlights
Unmanned aerial vehicles (UAVs) are aircraft that can be controlled by a ground station or via onboard electronic equipment and can be fully or partially autonomous
According to whether the obstacle information is known, UAV path planning can be divided into two categories: static planning, in which the locations of all obstacles and threats are known before planning and whereby a reasonable path can be planned before UAV take-off [2]; and dynamic planning, in which the UAV needs to deal with uncertain obstacles or unexpected threats by dynamically resolving the conflicts
The existing methods for path planning can be divided into numerical optimization, potential field-based method, heuristics, sampling-based method, and deep reinforcement learning [4,5,6]
Summary
Unmanned aerial vehicles (UAVs) are aircraft that can be controlled by a ground station or via onboard electronic equipment and can be fully or partially autonomous. UAV path planning mainly faces the following key technical challenges: 1) Smart algorithms commonly used for UAV path planning often take a long time due to their high complexity [7]. For this reason, these algorithms are time-consuming and difficult to implement when solving large-scale schemes of UAV path planning. Restricted by the high complexity and timeconsuming process of the model, the existing algorithms can be used only for experimental research based on simplified models [8] This approach cannot truly reflect the actual needs of UAV path planning. For UAV path planning, the scheme designed in this paper consists of two levels: the global path planning based on modified A* and the local dynamic path planning based on modified Q-learning
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