FC-RRT*: An Improved Path Planning Algorithm for UAV in 3D Complex Environment

Yicong Guo,Xiaoxiong Liu,Xuhang Liu,Yue Yang,Weiguo Zhang

doi:10.3390/ijgi11020112

Abstract

In complex environments, path planning is the key for unmanned aerial vehicles (UAVs) to perform military missions autonomously. This paper proposes a novel algorithm called flight cost-based Rapidly-exploring Random Tree star (FC-RRT*) extending the standard Rapidly-exploring Random Tree star (RRT*) to deal with the safety requirements and flight constraints of UAVs in a complex 3D environment. First, a flight cost function that includes threat strength and path length was designed to comprehensively evaluate the connection between two path nodes. Second, in order to solve the UAV path planning problem from the front-end, the flight cost function and flight constraints were used to inspire the expansion of new nodes. Third, the designed cost function was used to guide the update of the parent node to allow the algorithm to consider both the threat and the length of the path when generating the path. The simulation and comparison results show that FC-RRT* effectively overcomes the shortcomings of standard RRT*. FC-RRT* is able to plan an optimal path that significantly improves path safety as well as maintains has the shortest distance while satisfying flight constraints in the complex environment. This paper has application value in UAV 3D global path planning.

Highlights

In the last decade, unmanned aerial vehicles (UAVs) have gradually come to play an important role in various military operations [1]
Scholars have conducted a lot of research on the UAV path planning problem and proposed a series of algorithms, such as graph-based optimization methods, including the visibility graph (VG) algorithm [7] and Voronoi diagrams [8]; the searching-based methods, including the Dijkstra [9] algorithm, A* algorithm [10] and D* algorithm [11]; the sampling-based methods, such as PRM algorithm [12] and RRT algorithm [13]; the nature-inspired methods, such as genetic algorithm (GA) [14], ant colony optimization (ACO) [15], artificial potential field algorithm [16], particle swarm optimization (PSO) [17] and fluid-based algorithm [18]; and other methods, such as control theory-based methods [19]
Since this paper focuses on the UAV path planning problem rather than constructing new flight constraints, we directly select and derive a set of flight constraints suitable for this paper based on the existing representative constraints in [37,39] to accommodate the key performance constraints and complex environmental requirements in UAV path planning

Summary

Introduction

In the last decade, unmanned aerial vehicles (UAVs) have gradually come to play an important role in various military operations [1]. As a sampling-based path planning algorithm, RRT* does not explicitly construct the entire planning space and its boundaries as a searching-based method, but instead directly obtains samples through sampling to form a search tree. This approach avoids the problem where search time grows exponentially with the number of spatial dimensions, significantly reducing the search time available for path planning in high-dimensional spaces [20,21]. Compared to intelligent methods, such as GA and ACO, RRT* has a lower algorithmic complexity in 3D space [22,23] As such, it is more suitable for solving path planning problem in 3D space. RRT* is more suitable for UAV path planning than other algorithms in complex environments

Results

Discussion

Conclusion