Intelligent Beetle Antennae Search for UAV Sensing and Avoidance of Obstacles.

Qing Wu,Dechao Chen,Ameer Hamza Khan,Yuanzhe Jin,Shuai Li,Xudong Shen,Zeyu Chen

doi:10.3390/s19081758

Abstract

Based on a bio-heuristic algorithm, this paper proposes a novel path planner called obstacle avoidance beetle antennae search (OABAS) algorithm, which is applied to the global path planning of unmanned aerial vehicles (UAVs). Compared with the previous bio-heuristic algorithms, the algorithm proposed in this paper has advantages of a wide search range and breakneck search speed, which resolves the contradictory requirements of the high computational complexity of the bio-heuristic algorithm and real-time path planning of UAVs. Besides, the constraints used by the proposed algorithm satisfy various characteristics of the path, such as shorter path length, maximum allowed turning angle, and obstacle avoidance. Ignoring the z-axis optimization by combining with the minimum threat surface (MTS), the resultant path meets the requirements of efficiency and safety. The effectiveness of the algorithm is substantiated by applying the proposed path planning algorithm on the UAVs. Moreover, comparisons with other existing algorithms further demonstrate the superiority of the proposed OABAS algorithm.

Highlights

Unmanned aerial vehicles (UAVs) are increasingly being used in military and civilian environments to perform critical tasks [1,2] because of their ability to complete missions under dangerous conditions or extreme weather [3]
We read the map information and generated a random initial path, used this path as input to the obstacle avoidance beetle antennae search (OABAS) algorithm which adjusted the waypoints until the path met the convergence condition
We find that the OABAS algorithm only takes about 1000 iterations to plan a better path

Summary

Introduction

Unmanned aerial vehicles (UAVs) are increasingly being used in military and civilian environments to perform critical tasks [1,2] because of their ability to complete missions under dangerous conditions or extreme weather [3]. Sampling based methods include probabilistic roadmaps (PRM) [8], Voronoi maps [9], corridor map [10] and artificial potential field (APF) [11]. These types of algorithms usually need to pre-process the map, grid or sample the map, and randomly search for paths. These types of path planning algorithms have a time complexity of O(n log n) to O(n2 ), which is reasonably fast and can be applied to real-time path planning

Methods

Results

Conclusion