A Novel Occupancy Mapping Framework for Risk-Aware Path Planning in Unstructured Environments

In the context of autonomous robots, one of the most important tasks is to prevent potential damage to the robot during navigation. For this purpose, it is often assumed that one must deal with known probabilistic obstacles, then compute the probability of collision with each obstacle. However, in c...

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Autores principales:	Johann Laconte, Abderrahim Kasmi, François Pomerleau, Roland Chapuis, Laurent Malaterre, Christophe Debain, Romuald Aufrère
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	risk assessment path planning risk modeling occupancy grid safe navigation field robotics Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/79a66134c342462ab70ac04696c92dcb
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id	oai:doaj.org-article:79a66134c342462ab70ac04696c92dcb
record_format	dspace
spelling	oai:doaj.org-article:79a66134c342462ab70ac04696c92dcb2021-11-25T18:57:28ZA Novel Occupancy Mapping Framework for Risk-Aware Path Planning in Unstructured Environments10.3390/s212275621424-8220https://doaj.org/article/79a66134c342462ab70ac04696c92dcb2021-11-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/22/7562https://doaj.org/toc/1424-8220In the context of autonomous robots, one of the most important tasks is to prevent potential damage to the robot during navigation. For this purpose, it is often assumed that one must deal with known probabilistic obstacles, then compute the probability of collision with each obstacle. However, in complex scenarios or unstructured environments, it might be difficult to detect such obstacles. In these cases, a metric map is used, where each position stores the information of occupancy. The most common type of metric map is the Bayesian occupancy map. However, this type of map is not well suited for computing risk assessments for continuous paths due to its discrete nature. Hence, we introduce a novel type of map called the Lambda Field, which is specially designed for risk assessment. We first propose a way to compute such a map and the expectation of a generic risk over a path. Then, we demonstrate the benefits of our generic formulation with a use case defining the risk as the expected collision force over a path. Using this risk definition and the Lambda Field, we show that our framework is capable of doing classical path planning while having a physical-based metric. Furthermore, the Lambda Field gives a natural way to deal with unstructured environments, such as tall grass. Where standard environment representations would always generate trajectories going around such obstacles, our framework allows the robot to go through the grass while being aware of the risk taken.Johann LaconteAbderrahim KasmiFrançois PomerleauRoland ChapuisLaurent MalaterreChristophe DebainRomuald AufrèreMDPI AGarticlerisk assessmentpath planningrisk modelingoccupancy gridsafe navigationfield roboticsChemical technologyTP1-1185ENSensors, Vol 21, Iss 7562, p 7562 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	risk assessment path planning risk modeling occupancy grid safe navigation field robotics Chemical technology TP1-1185
spellingShingle	risk assessment path planning risk modeling occupancy grid safe navigation field robotics Chemical technology TP1-1185 Johann Laconte Abderrahim Kasmi François Pomerleau Roland Chapuis Laurent Malaterre Christophe Debain Romuald Aufrère A Novel Occupancy Mapping Framework for Risk-Aware Path Planning in Unstructured Environments
description	In the context of autonomous robots, one of the most important tasks is to prevent potential damage to the robot during navigation. For this purpose, it is often assumed that one must deal with known probabilistic obstacles, then compute the probability of collision with each obstacle. However, in complex scenarios or unstructured environments, it might be difficult to detect such obstacles. In these cases, a metric map is used, where each position stores the information of occupancy. The most common type of metric map is the Bayesian occupancy map. However, this type of map is not well suited for computing risk assessments for continuous paths due to its discrete nature. Hence, we introduce a novel type of map called the Lambda Field, which is specially designed for risk assessment. We first propose a way to compute such a map and the expectation of a generic risk over a path. Then, we demonstrate the benefits of our generic formulation with a use case defining the risk as the expected collision force over a path. Using this risk definition and the Lambda Field, we show that our framework is capable of doing classical path planning while having a physical-based metric. Furthermore, the Lambda Field gives a natural way to deal with unstructured environments, such as tall grass. Where standard environment representations would always generate trajectories going around such obstacles, our framework allows the robot to go through the grass while being aware of the risk taken.
format	article
author	Johann Laconte Abderrahim Kasmi François Pomerleau Roland Chapuis Laurent Malaterre Christophe Debain Romuald Aufrère
author_facet	Johann Laconte Abderrahim Kasmi François Pomerleau Roland Chapuis Laurent Malaterre Christophe Debain Romuald Aufrère
author_sort	Johann Laconte
title	A Novel Occupancy Mapping Framework for Risk-Aware Path Planning in Unstructured Environments
title_short	A Novel Occupancy Mapping Framework for Risk-Aware Path Planning in Unstructured Environments
title_full	A Novel Occupancy Mapping Framework for Risk-Aware Path Planning in Unstructured Environments
title_fullStr	A Novel Occupancy Mapping Framework for Risk-Aware Path Planning in Unstructured Environments
title_full_unstemmed	A Novel Occupancy Mapping Framework for Risk-Aware Path Planning in Unstructured Environments
title_sort	novel occupancy mapping framework for risk-aware path planning in unstructured environments
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/79a66134c342462ab70ac04696c92dcb
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A Novel Occupancy Mapping Framework for Risk-Aware Path Planning in Unstructured Environments

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