Knowledge-inducing Global Path Planning for Robots in Environment with Hybrid Terrain

Yi-Nan Guo,Mei Yang,Jian Cheng

doi:10.5772/9705

Abstract

In complex environment with hybrid terrain, different regions may have different terrain. Path planning for robots in such environment is an open NP-complete problem, which lacks effective methods. The paper develops a novel global path planning method based on common sense and evolution knowledge by adopting dual evolution structure in culture algorithms. Common sense describes terrain information and feasibility of environment, which is used to evaluate and select the paths. Evolution knowledge describes the angle relationship between the path and the obstacles, or the common segments of paths, which is used to judge and repair infeasible individuals. Taken two types of environments with different obstacles and terrain as examples, simulation results indicate that the algorithm can effectively solve path planning problem in complex environment and decrease the computation complexity for judgment and repair of infeasible individuals. It also can improve the convergence speed and have better computation stability.

Highlights

Global path planning for a mobile robotic is defined as finding a most reasonable collision-free route from a start location to a destination in the environment with obstacles
In most of researches on global path planning, only known or unknown obstacles are considered in the environment as the terrain is simple
Common sense describes the distribution of terrain and obstacles in complex environment, and it reflects feasibility and traversability of environment

Summary

Introduction

Global path planning for a mobile robotic is defined as finding a most reasonable collision-free route from a start location to a destination in the environment with obstacles. This route is generally optimal in some aspects, such as shortest distance or motion time. Considering the important influence of terrain condition on path planning, this paper discusses modeling methods combing no-supervise learning with fuzzy logic aiming at certain environment with hybrid terrain and obstacles. A knowledge-based global path planning method for mobile robotics in rough environment with hybrid terrain is proposed in the paper. The goal of the method is to decrease computation complexity and improve the convergence speed and the precision of the solutions by adopting various knowledge

Description about Problem

Modeling of hybrid terrain

Knowledge-based Global Path Planning Method in Complex Environment

Simulation and analysis

Conclusions