Robot path planning in uncertain environment using multi-objective particle swarm optimization

Zhang, Yong; Gong, Dunwei; Zhang, Jianhua

doi:10.1016/j.neucom.2012.09.019

Cited by 323 publications

(142 citation statements)

References 22 publications

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“…To prepare an optimal intelligent controller for an autonomous wheeled mobile robot, the Castillo et al [120] have designed the hybridization of an Ant Colony Optimization (ACO) algorithm and the Particle Swarm Optimization (PSO) algorithm to optimize the membership function of a fuzzy controller. Zhang et al [121] have proposed the Multi-Objective Particle Swarm Optimization Algorithm (MOPSO) to search a collision-free optimal path in the uncertain dynamic environment. Zhang & Li [122] have presented a new objective function for mobile robot navigation using PSO.…”

Section: Particle Swarm Optimization Algorithm For Mobile Robot Navigmentioning

confidence: 99%

Mobile Robot Navigation and Obstacle Avoidance Techniques: A Review

Pandey¹

2017

IRATJ

161

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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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Section: Particle Swarm Optimization Algorithm For Mobile Robot Navigmentioning

confidence: 99%

Mobile Robot Navigation and Obstacle Avoidance Techniques: A Review

Pandey¹

2017

IRATJ

161

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“…Typically in swarm robotics, success is achieved when some number of robots reach the goal or find a path [13,29,32,31,42,30]. However, for ACANTO, an activity is only considered completely successful if all the social group members complete the activity, and do so coherently.…”

Section: Robot Assistancementioning

confidence: 99%

Group abstraction for assisted navigation of social activities in intelligent environments

Given-Wilson

Legay

Sedwards

et al. 2018

J Reliable Intell Environ

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The ACANTO project is developing robotic assistants to aid the confidence and recovery of older adults. A key requirement of these assistants is aiding with navigation in complex and potentially chaotic environments. Prior work has addressed this for a single user, using a single robotic assistant in an intelligent environment. However, for therapeutic purposes, ACANTO supports social groups and group activities. ACANTO's robotic assistants must therefore be able to plan the motion of groups of older adults walking together. This requires an efficient navigation solution that can handle large numbers of users and that can operate rapidly on embedded computing devices. To increase user confidence, the solution must encourage group cohesion without trying to impose its own rigid structure; it must try to maintain the natural (de facto) group structure despite unpredictable behaviours and environmental conditions. Our on-the-fly group motion planner addresses these challenges by: using intelligent environment information to develop behavioural traces, clustering traces to determine groups, constructing a predictive model of the groups as a whole, and finding an optimal suggested trajectory using Statistical Model Checking. In this work we describe our proposed approach in detail and validate some of its novel aspects on the ETH Zürich pedestrian motion dataset.

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“…The velocity of the particle V i can be represented by another D-dimensional vector V i = (V i1 ,...,V iD ). The best position visited by the ith particle is denoted as pbest i = (P i1 ,...,P iD ), and gbest as the index of the particle visited the best position in the swarm; thus, gbest becomes the best solution found so far Zhang et al (2013). The working of PSO algorithm is explained in the following procedures:…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Design of Fractional-Order Controller for Trajectory Tracking Control of a Non-holonomic Autonomous Ground Vehicle

Al-Mayyahi

Wang

Birch

2015

J Control Autom Electr Syst

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(2016) Design of fractional-order controller for trajectory tracking control of a non-holonomic autonomous ground vehicle. Journal of Control, Automation and Electrical Systems, 27 (1). pp. 29-42. ISSN 2195-3880 This version is available from Sussex Research Online: http://sro.sussex.ac.uk/57057/ This document is made available in accordance with publisher policies and may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the URL above for details on accessing the published version. Copyright and reuse:Sussex Research Online is a digital repository of the research output of the University.Copyright and all moral rights to the version of the paper presented here belong to the individual author(s) and/or other copyright owners. To the extent reasonable and practicable, the material made available in SRO has been checked for eligibility before being made available.Copies of full text items generally can be reproduced, displayed or performed and given to third parties in any format or medium for personal research or study, educational, or not-for-profit purposes without prior permission or charge, provided that the authors, title and full bibliographic details are credited, a hyperlink and/or URL is given for the original metadata page and the content is not changed in any way. Abstract A robust control technique is proposed to address the problem of trajectory tracking of an autonomous ground vehicle (AGV). This technique utilizes a fractional-order proportional integral derivative (FOPID) controller to control a non-holonomic autonomous ground vehicle to track the behaviour of the predefined reference path. Two FOPID controllers are designed to control the AGV's inputs. These inputs represent the torques that are used in order to manipulate the implemented model of the vehicle to obtain the actual path. The implemented model of the non-holonomic autonomous ground vehicle takes into consideration both of the kinematic and dynamic models. In additional, a particle swarm optimization (PSO) algorithm is used to optimize the FOPID controllers' parameters. These optimal tuned parameters of FOPID controllers minimize the cost function used in the algorithm. The effectiveness and validation of the proposed method have been verified through different patterns of reference paths using MATLAB-Simulink software package. The stability of fractional-order system is analysed. Also, the robustness of the system is conducted by adding disturbances due to friction of wheels during the vehicle motion.The obtained results of FOPID controller show the advantage and the performance of the technique in terms of minimizing path tracking error and the complement of the path following.B Auday Al-Mayyahi

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Robot path planning in uncertain environment using multi-objective particle swarm optimization

Cited by 323 publications

References 22 publications

Mobile Robot Navigation and Obstacle Avoidance Techniques: A Review

Mobile Robot Navigation and Obstacle Avoidance Techniques: A Review

Group abstraction for assisted navigation of social activities in intelligent environments

Design of Fractional-Order Controller for Trajectory Tracking Control of a Non-holonomic Autonomous Ground Vehicle

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