Real-Time Efficient Trajectory Planning for Quadrotor Based on Hard Constraints

Trajectory planning for quadrotor has been extensively studied in terms of safety, smoothness, and dynamical feasibility. However, few methods have been proposed for the optimization of efficiency in long-distance navigation. Quadrotor often has high computational complexity when performing tasks that involve at least multiple computing modules such as location, mapping, planning, etc. Therefore, improving planning efficiency and flight efficiency can save computing resources and improve transport efficiency to complete more tasks. This paper presents a real-time trajectory planning method that can achieve long-distance navigation with less planning number and calculation time while greatly reducing flight time to reach the target point quickly. Our method is built on hard constraints such as safety distance, free-space flight corridors, and smoothness constraints that can ensure trajectory quality. For each scenario, our improved Theta* algorithm can obtain a shortest initial trajectory with several key waypoints. Low-quality segments of the initial trajectory are then screened and optimized by local replanning detection and flight corridor-based optimization, respectively. Flight efficiency, continuity, and dynamical feasibility are greatly boosted by the distributed time allocation method. Experimental results show that the flight time of our method is 22%-56% less than that of the state-of-the-art hard-constrained methods in about 50 m flight, and total calculation time is 19%-84% less, which is attributable to the reduction of planning number. The proposed trajectory planning method is also integrated into a quadrotor platform and its competence is validated by presenting autonomous flight in unknown indoor environments.