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An improved sparrow search based intelligent navigational algorithm for local path planning of mobile robot

June 2022
Journal of Ambient Intelligence and Humanized Computing 14(100):1-13

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In this paper, an improved sparrow search algorithm (SSA) for local path planning problem of mobile robot in an unknown environment is presented. The problems of premature convergence and decline of population diversity of basic SSA are solved by the inspiration of fitness-distance balance (FDB) selection and Harris Hawks Algorithm. A hybrid fitness function is formulated considering both path length and path safety, which enables the mobile robot to move to the target location safely. The effectiveness and superiority of the proposed improved SSA (ISSA) is verified in CEC 2017 suite for comparison experiments with multiple intelligent optimization algorithms. Local path planning simulation experiments are implemented using the proposed algorithm in the unknown environment and compared with other algorithms, and the results show that our algorithm is effective and robust in solving local path planning problem of mobile robots.

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Levy flight

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A part of convergence curve results

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A part of box-plot results

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Vol.:(0123456789)

1 3

Journal of Ambient Intelligence and Humanized Computing (2023) 14:14111–14123

https://doi.org/10.1007/s12652-022-04115-1

ORIGINAL RESEARCH

An improved sparrow search based intelligent navigational algorithm

forlocal path planning ofmobile robot

GuangjianZhang1· EnhaoZhang1

Received: 12 November 2021 / Accepted: 6 June 2022 / Published online: 24 June 2022

Abstract

In this paper, an improved sparrow search algorithm (SSA) for local path planning problem of mobile robot in an unknown

environment is presented. The problems of premature convergence and decline of population diversity of basic SSA are

solved by the inspiration of ﬁtness-distance balance (FDB) selection and Harris Hawks Algorithm. A hybrid ﬁtness function

is formulated considering both path length and path safety, which enables the mobile robot to move to the target location

safely. The eﬀectiveness and superiority of the proposed improved SSA (ISSA) is veriﬁed in CEC 2017 suite for comparison

experiments with multiple intelligent optimization algorithms. Local path planning simulation experiments are implemented

using the proposed algorithm in the unknown environment and compared with other algorithms, and the results show that

our algorithm is eﬀective and robust in solving local path planning problem of mobile robots.

Keywords Local path planning· Sparrow search algorithm· Swarm intelligence algorithm· Mobile robot

1 Introduction

Mobile robot is an intelligent autonomous system with

wide applications in industrial, medical, military, and

aerospace ﬁelds. Path planning is one of the signiﬁcant

and challenging tasks in mobile robotics. The path plan-

ning problem can be divided into global path planning and

local path planning according to the level of knowledge

of map information. Global path planning means that the

robot has suﬃcient information about the environment

before planning the path, and when the robot is not aware

of the obstacle information in the environment is called

local path planning. Therefore, the correct path planning

method is necessary to ensure that the robot completes its

task successfully.

Path planning refers to the navigation of a mobile robot

to a target location while satisfying certain constraints (e.g.,

path length, path smoothness, path safety, etc.). There are

many related studies on path planning methods for mobile

robots (Sood and Panchal 2020; Patle etal 2019; Zhang etal

2018), however, classical methods require clear information

about the environment and are computationally overloaded.

Researchers have therefore turned to algorithms of com-

putational intelligence applied to path planning problems.

Xu etal (2020) proposed an artiﬁcial bee colony algorithm

introducing a co-evolutionary framework for path planning

of mobile robots, which improves the convergence speed

and avoids dimensionality dependence. Zhao etal (2020)

proposed a co-optimization of a multi-objective Cauchy

mutation cat colony optimization and an artiﬁcial potential

ﬁeld method to solve the path planning problem of an intel-

ligent patrol car navigation system. Song etal (2021) solved

the problem of local optimum and premature convergence

by an improved PSO algorithm that combines continuous

high-degree Bezier curves to plan the smooth path of the

robot.

However, due to the NFL principle (Wolpert and Mac-

ready 1997), each intelligent optimization algorithm has its

advantages and disadvantages, and no particular algorithm

is superior for all types of problems. Therefore, it is neces-

sary to develop new intelligent optimization algorithms to

be applied to path planning problems. Further, the study of

local path planning problems in uncertain environments is

very limited. The global path planning approach cannot be

used when the robot does not have information about the

obstacles in its area. Relying solely on sensors to provide

* Enhao Zhang

52192325122@2019.cqut.edu.cn

1 School ofArtiﬁcial Intelligence, Chongqing University

ofTechnology, Pufu Street, Chongqing401135, Chongqing,

China

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An improved sparrow search based intelligent navigational algorithm for local path planning of mobile robot

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