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Path Planning and scheduling for a fleet of autonomous vehicles

Published online by Cambridge University Press:  29 April 2015

Elias Xidias ,
Paraskevi Zacharia  and
Andreas Nearchou
Elias Xidias
Affiliation:
Department of Product and Systems Design Engineering 84100, University of the Aegean, Hermoupolis, Syros, Greece
Paraskevi Zacharia*
Affiliation:
Department of Business Administration, University of Patras, 26 500, Rio, Patras, Greece
Andreas Nearchou
Affiliation:
Department of Business Administration, University of Patras, 26 500, Rio, Patras, Greece
*
*Corresponding author. E-mail: zacharia@mech.upatras.gr
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Summary

This paper presents a new solution approach for managing the motion of a fleet of autonomous vehicles (AVs) in indoor factory environments. AVs are requested to serve a number of workstations (WS) (following a specified desired production plan for materials requirements) while taking into account the safe movement (collisions avoidance) in the shop floor as well as time duration and energy resources. The proposed approach is based on the Bump-Surface concept to represent the 2D environment through a single mathematical entity. The solution of the combined problem of path planning and task scheduling is searched on a higher-dimension B-surface (in our case 3D) in such a way that its inverse image into the robot environment satisfies the given objectives and constraints. Then, a modified Genetic Algorithm (GA) is used to search for a near-optimum solution. The objective of the fleet coordination consists of determining the best feasible paths for the AVs so that all the WS are served at the lowest possible cost. The efficiency of the developed method is investigated and discussed through characteristic simulated experiments concerning a variety of operating environments.

Keywords

Conflicts-freePath planningMotion planningLogisticsService robots

Information

Type
Articles
Information
Robotica , Volume 34 , Issue 10 , October 2016 , pp. 2257 - 2273
Copyright
Copyright © Cambridge University Press 2015 

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