Citation: Meysami, A.; Cuillière,
J.-C.; François, V.; Kelouwani, S.
Investigating the Impact of Triangle
and Quadrangle Mesh
Representations on AGV Path
Planning for Various Indoor
Environments: With or without
Inflation. Robotics 2022, 11, 50.
https://doi.org/10.3390/
robotics11020050
Academic Editors:
Giovanni Boschetti and João
Miguel da Costa Sousa
Received: 18 February 2022
Accepted: 10 April 2022
Published: 13 April 2022
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Article
Investigating the Impact of Triangle and Quadrangle Mesh
Representations on AGV Path Planning for Various Indoor
Environments: With or Without Inflation
Ahmadreza Meysami *
,†
, Jean-Christophe Cuillière
†
, Vincent François
†
and Sousso Kelouwani
†
Mechanical Engineering Department, Université du Québec à Trois-Rivières,
Trois-Rivieres, QC G8Z 4M3, Canada; jean-christophe.cuilliere@uqtr.ca (J.-C.C.);
vincent.francois@uqtr.ca (V.F.); sousso.kelouwani@uqtr.ca (S.K.)
* Correspondence: ahmadreza.meysami@uqtr.ca
† These authors contributed equally to this work.
Abstract:
In a factory with different kinds of spatial atmosphere (warehouses, corridors, small or large
workshops with varying sizes of obstacles and distribution patterns), the robot’s generated paths for
navigation tasks mainly depend on the representation of that environment. Hence, finding the best
representation for each particular environment is necessary to forge a compromise between length,
safety, and complexity of path planning. This paper aims to scrutinize the impact of environment model
representation on the performance of an automated guided vehicle (AGV). To do so, a multi-objective
cost function, considering the length of the path, its complexity, and minimum distance to obstacles,
is defined for a perfect circular robot. Unlike other similar studies, three types of representation,
namely quadrangle, irregular triangle, and varying-size irregular triangle, are then utilized to model
the environment while applying an inflation layer to the discretized view. Finally, a navigation scenario
is tested for different cell decomposition methods and an inflation layer size. The obtained results
indicate that a nearly constant coarse size triangular mesh is a good candidate for a fixed-size robot
in a non-changing environment. Moreover, the varying size of the triangular mesh and grid cell
representations are better choices for factories with changing plans and multi-robot sizes due to the
effect of the inflation layer. Based on the definition of a metric, which is a criterion for quantifying the
performance of path planning on a representation type, constant or variable size triangle shapes are the
only and best candidate for discretization in about 59% of industrial environments. In other cases, both
cell types, the square and the triangle, can together be the best representation.
Keywords: AGV; robotics mapping; mesh representation; path planning
1. Introduction
Automated guided vehicles (AGVs) have been used for several decades [
1
,
2
]. They
mostly rely on predefined routes to move from one point to another. However, the flexi-
bility and agility introduced by industry 4.0 [
3
] are pushing these technologies towards a
complete autonomous navigation capability [
4
]. The required autonomy forces the AGVs to
know the environment prior to moving intelligently and safely. Therefore, one of the most
important tasks for these vehicles is to model the navigation environment as accurately
as possible [
5
]. In indoor navigation on a planar floor of a factory, a 2D model of the envi-
ronment, including the specifications of the obstacle-free area, is required to perform path
planning. Abstracting, encoding, and saving this type of information is called ‘mapping’
in mobile robots [
6
]. A mobile robot uses this map repeatedly to find a path between
the current position and the goal position. The essential information for constructing
the environment model is usually obtained from raw sensor data with the help of data
structures. There are different types of data structures for saving and reading necessary
spatial information in navigation. After saving the information, path planning uses this
Robotics 2022, 11, 50. https://doi.org/10.3390/robotics11020050 https://www.mdpi.com/journal/robotics
