Path planning based quadtree representation for mobile robot using hybrid-simulated annealing and ant colony optimization algorithm

Zhang, Qi; Ma, Jiachen; Liu, Qiang

doi:10.1109/wcica.2012.6358300

Cited by 17 publications

(8 citation statements)

References 19 publications

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“…In (Zhang, Ma, and Liu 2012), the genetic algorithm and SAA methods are applied into the study of path planning. The adaptation function of the path is evaluated to be effective with real-time performance in GPSdenied environments.…”

Section: Intelligent Algorithmsmentioning

confidence: 99%

“…Types Methods Vachtsevanos et al (1997) Global a-star search szczerba et al 2000Global sparse a-star search stentz (1994) Global Dynamic a-star search Yershova et al (2005) Global rapidly-exploring random trees andert and adolf 2009Global simulated annealing Zhang, ma, and liu (2012) Global simulate anneal arithmetic Gilmore and czuchry (1992) local Hopfield networks sugihara and suzuki (1996) local artificial potential field Bortoff (2000) local artificial potential field parunak, purcell, and o'connell 2002local ant colony algorithm sensors for dynamic, complex and large-scale environments, remains to be solved and is a prosperous area of research (Jones 2009;Márquez-Gámez 2012;Mirowski et al 2017).…”

Section: Authorsmentioning

confidence: 99%

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A survey on vision-based UAV navigation

Xue

Xia

et al. 2018

Geo-spatial Information Science

323

122

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Research on unmanned aerial vehicles (UAV) has been increasingly popular in the past decades, and UAVs have been widely used in industrial inspection, remote sensing for mapping & surveying, rescuing, and so on. Nevertheless, the limited autonomous navigation capability severely hampers the application of UAVs in complex environments, such as GPS-denied areas. Previously, researchers mainly focused on the use of laser or radar sensors for UAV navigation. With the rapid development of computer vision, vision-based methods, which utilize cheaper and more flexible visual sensors, have shown great advantages in the field of UAV navigation. The purpose of this article is to present a comprehensive literature review of the vision-based methods for UAV navigation. Specifically on visual localization and mapping, obstacle avoidance and path planning, which compose the essential parts of visual navigation. Furthermore, throughout this article, we will have an insight into the prospect of the UAV navigation and the challenges to be faced.

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Section: Intelligent Algorithmsmentioning

confidence: 99%

Section: Authorsmentioning

confidence: 99%

A survey on vision-based UAV navigation

Xue

Xia

et al. 2018

Geo-spatial Information Science

323

122

View full text Add to dashboard Cite

show abstract

“…Miao and Tian [115] have presented a simulated annealing algorithm based intelligent navigational controller, which helps the robot to search an optimal or near-optimal path in the static and dynamic environments. Zhang et al [116] have combined the simulated annealing algorithm and Ant Colony Optimization (ACO) algorithm to increase the navigation speed of the mobile robot. In [117], the authors have improved the convergence speed of the simulated annealing algorithm using the artificial neural network and applied it to mobile robot path planning.…”

Section: Simulated Annealing Algorithm For Mobile Robot Navigationmentioning

confidence: 99%

Mobile Robot Navigation and Obstacle Avoidance Techniques: A Review

Pandey¹

2017

IRATJ

184

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Mobile robot is an autonomous agent capable of navigating intelligently anywhere using sensor-actuator control techniques. The applications of the autonomous mobile robot in many fields such as industry, space, defence and transportation, and other social sectors are growing day by day. The mobile robot performs many tasks such as rescue operation, patrolling, disaster relief, planetary exploration, and material handling, etc. Therefore, an intelligent mobile robot is required that could travel autonomously in various static and dynamic environments. Several techniques have been applied by the various researchers for mobile robot navigation and obstacle avoidance. The present article focuses on the study of the intelligent navigation techniques, which are capable of navigating a mobile robot autonomously in static as well as dynamic environments. Keywords: IntroductionThis article introduces the literature survey of the various techniques used for mobile robot navigation. Navigation and obstacle avoidance are one of the fundamental problems in mobile robotics, which are being solved by the various researchers in the past two decades. The aim of navigation is to search an optimal or suboptimal path from the start point to the goal point with obstacle avoidance competence. Basically, the mobile robot navigation has been done by the Deterministic algorithm and Nondeterministic (Stochastic) algorithm. Nowadays, the hybridization of both the algorithms called as an Evolutionary algorithm is being used to solve the mobile robot navigation problem. Figure 1 shows the general classification of the Deterministic algorithm, Nondeterministic (Stochastic) algorithm, and Evolutionary algorithm, which are implemented for mobile robot navigation by various authors.

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“…However, the algorithm is not optimal in the environments having many dynamic obstacles [7]. To increase the efficiency of path planning, Zhang, Ma, and Liu proposed a path planning algorithm combining framed-quadtree representation with hybridsimulated annealing (SA) and ant colony optimization (ACO) algorithm called SAACO [8]. Andrey proposed an autonomous road extraction and navigation method by computing relative 3D position and orientation solution based on parameters of planar surfaces that are extracted from scan images [9].…”

Section: Introductionmentioning

confidence: 99%

Mobile Robot Autonomous Navigation and Dynamic Environmental Adaptation in Large-Scale Outdoor Scenes

Yang

2019

Intelligent Robotics and Applications

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In this paper, the problem of dynamic obstacle recognition and dynamic obstacle avoidance path planning for mobile robots in outdoor environment is studied. Based on the odometer data and the online matching algorithm of 3D laser scanning point clouds, the topological map and the global path planning are realized in this paper firstly. Based on the analysis of the geometric characteristics of obstacles, a novel approach of dynamic obstacle recognition method is presented. At the same time, a dynamic obstacle avoidance method based on the obstacle motion prediction is adopted to solve the reliable obstacle avoidance path planning problem of outdoor mobile robot. A series of experiments are conducted with a self-designed mobile robot platform in large-scale outdoor environments, and the experimental results show the validity and effectiveness of the proposed approach.

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Path planning based quadtree representation for mobile robot using hybrid-simulated annealing and ant colony optimization algorithm

Cited by 17 publications

References 19 publications

A survey on vision-based UAV navigation

A survey on vision-based UAV navigation

Mobile Robot Navigation and Obstacle Avoidance Techniques: A Review

Mobile Robot Autonomous Navigation and Dynamic Environmental Adaptation in Large-Scale Outdoor Scenes

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