Phototropism rapidly exploring random tree: An efficient rapidly exploring random tree approach based on the phototropism of plants

Chengchen Zhuge,Ying Cui,Jiayin Liu,Dongyan Guo

doi:10.1177/1729881420945213

Abstract

Inspired by the phototropism of plants, a novel variant of the rapidly exploring random tree algorithm as called phototropism rapidly exploring random tree is proposed. The phototropism rapidly exploring random tree algorithm expands less tree nodes during the exploration period and has shorter path length than the original rapidly exploring random tree algorithm. In the algorithm, a virtual light source is set up at the goal point, and a light beam propagation method is adopted on the map to generate a map of light intensity distribution. The phototropism rapidly exploring random tree expands its node toward the space where the light intensity is higher, while the original rapidly exploring random tree expands its node based on the uniform sampling strategy. The performance of the phototropism rapidly exploring random tree is tested in three scenarios which include the simulation environment and the real-world environment. The experimental results show that the proposed phototropism rapidly exploring random tree algorithm has a higher sampling efficiency than the original rapidly exploring random tree, and the path length is close to the optimal solution of the rapidly exploring random tree* algorithm without considering the non-holonomic constraint.

Highlights

Path planning is a fundamental research topic in the field of robotics
Inspired by the phototropism of plants, we propose a phototropism rapidly exploring random tree (PRRT) approach
The results show that P-rapidly exploring random tree (RRT) with the Euclidean distance has better performance than with LA* distance

Summary

Introduction

The purpose of path planning is to find a collisionfree path for the mobile robot from the current configuration to the desired configuration with the least possible cost, including shortest path length, minimum moving time, minimum energy consumption, and so on. Numerous approaches for path planning have been proposed, including graph-based methods,[1,2,3] artificial potential field (APF) method,[4] reinforcement learning (RL),[5,6] sample-based methods,[7,8,9] and so on. The common graph-based methods include A*, Dijkstra, D*, and so on. These methods can find a high-quality path depend on the resolution of the discrete map. 2. Let X obs and X goal, called obstacle region and the goal region, respectively, be open subsets of X.21. The path planning problem is to find a path s : 1⁄20; s ! X f ree such that sð0Þ 1⁄4 xinit and sðsÞ 2 X goal

Methods

Results

Conclusion