Robust path planning for avoiding obstacles using time-environment dynamic map

Xiaolin Zhao,Boxin Zhao,Yu Zhang

doi:10.1177/0020294019847704

Abstract

Small unmanned aerial vehicles are widely used in urban space because of its flexibility and maneuverability. However, there are full of dynamic obstacles and immobile obstacles which will affect safe flying in urban space. In this paper, a novel integrated path planning approach for unmanned aerial vehicles is presented, which is consisted of three steps. First, a time-environment dynamic map is constructed to represent obstacles by introducing time axis. Second, unmanned aerial vehicles’ flyable paths are explored based on breadth-first algorithm. Third, a path planning method using A* algorithm and local trace-back model is designed in order to discover sub-optimal feasible path rapidly in unmanned aerial vehicles’ field of view. Finally, the simulation results have illustrated that the proposed method can ensure unmanned aerial vehicles’ autonomous path planning safely and effectively in urban space crowded with obstacles.

Highlights

Since unmanned aerial vehicles (UAVs) have great potential to complete missions without human intervention,[1] large number of applications of UAVs for both military and commercial purposes have been emerged
Wang et al developed a path planning method, which built a Laguerre diagram based on Delaunay graphs, to overcome the drawbacks of Voronoi diagram
The graphbased methods such as Voronoi diagrams,[2,4,5] Laguerre diagram[3,6] or visibility graphs[7] all assume that the entire environment remains unchanged when a UAV is flying, so that a safe path from start-point to end-point can be designed according to the collected environmental information

Summary

Introduction

Since unmanned aerial vehicles (UAVs) have great potential to complete missions without human intervention,[1] large number of applications of UAVs for both military and commercial purposes have been emerged. What’s more, if we regard the regions that obstacles will (probably) reach as threat regions, a path without any collision threat can be designed in 1⁄2t, t + DtTEDM only if we avoid the threat regions We adopt the breadth-first algorithm to explore the surrounding environment and find out an optimal waypoint in FOV with A* algorithm, and apply the trace-back model to plan an optimal path in each short time interval In this way, we can get a path avoiding obstacle collisions over the urban space. To further depict the safety of the UAV, some flying moments are shown in Figure 13, where the brown disk

Methods

Conclusion