Collision Prediction and Optimal Trajectory Generation for Collision Avoidance Systems in Trackless Mobile Machines

The continued high number of fatalities associated with Trackless Mobile Machines (TMMs) in South Africa have led to the introduction of Collision Avoidance System (CAS) regulations in the Mine Health and Safety Act in 2015. This has lead to the profusion of technologically-immature CASs from third-...

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Autores principales:	Declercq Jesse, Botha Theunis, Hamersma Herman A.
Formato:	article
Lenguaje:	EN FR
Publicado:	EDP Sciences 2021
Materias:	Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/031648dc92d247b0b4463411f9e4b3ad
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id	oai:doaj.org-article:031648dc92d247b0b4463411f9e4b3ad
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spelling	oai:doaj.org-article:031648dc92d247b0b4463411f9e4b3ad2021-12-02T17:13:35ZCollision Prediction and Optimal Trajectory Generation for Collision Avoidance Systems in Trackless Mobile Machines2261-236X10.1051/matecconf/202134700032https://doaj.org/article/031648dc92d247b0b4463411f9e4b3ad2021-01-01T00:00:00Zhttps://www.matec-conferences.org/articles/matecconf/pdf/2021/16/matecconf_sacam21_00032.pdfhttps://doaj.org/toc/2261-236XThe continued high number of fatalities associated with Trackless Mobile Machines (TMMs) in South Africa have led to the introduction of Collision Avoidance System (CAS) regulations in the Mine Health and Safety Act in 2015. This has lead to the profusion of technologically-immature CASs from third-party vendors, all of which are centered on automatic stopping and braking systems. These braking systems often result in trivial or ineffective solutions, proving costly to mining operations. The combination of braking and steering control in CASs may substantially increase the solution space and provide far safer and more efficient manoeuvres. A recursive non-linear collision prediction estimator and optimal trajectory generation model was developed to evaluate the potential contribution of the addition of steering to CASs. Three independent optimal trajectory generation models are proposed to compete against one another in an attempt to synthesize the safest, most predictable, and efficient trajectory. A deep reinforcement learning, lattice optimization and Monte Carlo hyper sampling path planning model’s trajectories are evlauated using the Earth Moving Equipment Safety Round Table (EMESRT) interaction scenarios. Initial results indicate increased CAS solution spaces in collision-avoiding scenarios, providing safer and more effective solutions in high velocity vehicle interactions.Declercq JesseBotha TheunisHamersma Herman A.EDP SciencesarticleEngineering (General). Civil engineering (General)TA1-2040ENFRMATEC Web of Conferences, Vol 347, p 00032 (2021)
institution	DOAJ
collection	DOAJ
language	EN FR
topic	Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	Engineering (General). Civil engineering (General) TA1-2040 Declercq Jesse Botha Theunis Hamersma Herman A. Collision Prediction and Optimal Trajectory Generation for Collision Avoidance Systems in Trackless Mobile Machines
description	The continued high number of fatalities associated with Trackless Mobile Machines (TMMs) in South Africa have led to the introduction of Collision Avoidance System (CAS) regulations in the Mine Health and Safety Act in 2015. This has lead to the profusion of technologically-immature CASs from third-party vendors, all of which are centered on automatic stopping and braking systems. These braking systems often result in trivial or ineffective solutions, proving costly to mining operations. The combination of braking and steering control in CASs may substantially increase the solution space and provide far safer and more efficient manoeuvres. A recursive non-linear collision prediction estimator and optimal trajectory generation model was developed to evaluate the potential contribution of the addition of steering to CASs. Three independent optimal trajectory generation models are proposed to compete against one another in an attempt to synthesize the safest, most predictable, and efficient trajectory. A deep reinforcement learning, lattice optimization and Monte Carlo hyper sampling path planning model’s trajectories are evlauated using the Earth Moving Equipment Safety Round Table (EMESRT) interaction scenarios. Initial results indicate increased CAS solution spaces in collision-avoiding scenarios, providing safer and more effective solutions in high velocity vehicle interactions.
format	article
author	Declercq Jesse Botha Theunis Hamersma Herman A.
author_facet	Declercq Jesse Botha Theunis Hamersma Herman A.
author_sort	Declercq Jesse
title	Collision Prediction and Optimal Trajectory Generation for Collision Avoidance Systems in Trackless Mobile Machines
title_short	Collision Prediction and Optimal Trajectory Generation for Collision Avoidance Systems in Trackless Mobile Machines
title_full	Collision Prediction and Optimal Trajectory Generation for Collision Avoidance Systems in Trackless Mobile Machines
title_fullStr	Collision Prediction and Optimal Trajectory Generation for Collision Avoidance Systems in Trackless Mobile Machines
title_full_unstemmed	Collision Prediction and Optimal Trajectory Generation for Collision Avoidance Systems in Trackless Mobile Machines
title_sort	collision prediction and optimal trajectory generation for collision avoidance systems in trackless mobile machines
publisher	EDP Sciences
publishDate	2021
url	https://doaj.org/article/031648dc92d247b0b4463411f9e4b3ad
work_keys_str_mv	AT declercqjesse collisionpredictionandoptimaltrajectorygenerationforcollisionavoidancesystemsintracklessmobilemachines AT bothatheunis collisionpredictionandoptimaltrajectorygenerationforcollisionavoidancesystemsintracklessmobilemachines AT hamersmahermana collisionpredictionandoptimaltrajectorygenerationforcollisionavoidancesystemsintracklessmobilemachines
_version_	1718381301085503488

Collision Prediction and Optimal Trajectory Generation for Collision Avoidance Systems in Trackless Mobile Machines

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