Speed-First: An Aggressive Gradient-Based Local Planner for Quadrotor Faster Flight

Abstract

Autonomous flight for quadrotors is maturing with the development of real-time local trajectory planning. However, the current local planning method is too conservative to waste the agility of the quadrotors. So in this paper, we have focused on aggressive local trajectory planning and proposed a gradient-based planning method to rapidly plan faster executable trajectories while ensuring it is collision-free. A distance gradient information generation strategy is proposed, which finds a collision-free Hybrid-A* path to replace the control points in obstacles for safety and creates the distance gradient used in the back-end optimization. Besides, we present a novel and aggressive time span cost term to tackle unfeasibility and improve the overall trajectory speed. Extensive simulations and real-world experiments are tested to validate our method. The results show that our proposed method generates a more aggressive trajectory with a shorter planning time and a faster flight speed than the classical gradient-based method.

A Path-Planning Approach for an Unmanned Vehicle in an Off-Road Environment Based on an Improved A* Algorithm

“…Although the traditional A* algorithm can calculate the shortest path, it is limited by the fixed search direction and the fixed search-step size, and the obtained path may lack smoothness. Only the distance factor can be calculated, while other factors cannot be calculated [36][37][38]. Xie et al used flexible search angle and variable search-step size, and then represented the distribution of obstacles in the form of an artificial potential field distribution, added the distance to the obstacles to the heuristic function of the A* algorithm, which could make the ship's sea-path planning away from obstacles, and finally obtained a safe sea ship A* path-planning algorithm [39].…”