A path planning algorithm for autonomous flying vehicles in cross-country environments with a novel TF-RRT∗ method

Abstract

Autonomous flying vehicles (AFVs) are promising future vehicles, which have high obstacle avoidance ability. To plan a feasible path in a wide range of cross-country environments for the AFV, a triggered forward optimal rapidly-exploring random tree (TF-RRT∗) method is proposed. Firstly, an improved sampling and tree growth mechanism is built. Sampling and tree growth are allowed only in the forward region close to the target point, which significantly improves the planning speed; Secondly, the driving modes (ground-driving mode or air-driving mode) of the AFV are added to the sampling process as a planned state for uniform planning the driving path and driving mode; Thirdly, according to the dynamics and energy consumption models of the AFV, comprehensive indicators with energy consumption and efficiency are established for path optimal procedures, so as to select driving mode and plan driving path reasonably according to the demand. The proposed method is verified by simulations with an actual cross-country environment. Results show that the computation time is decreased by 71.08% compared with Informed-RRT∗ algorithm, and the path length of the proposed method decreased by 13.01% compared with RRT∗-Connect algorithm.