Dynamically-feasible real-time local planning for fast outdoor robots

Le, Nguyen Thanh Trung

Dynamically-feasible real-time local planning for fast outdoor robots

Le, Nguyen Thanh Trung

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Publication Type:: Thesis
Issue Date:: 2024

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Field	Value	Language
dc.contributor.author	Le, Nguyen Thanh Trung
dc.date.accessioned	2024-12-12T04:09:52Z
dc.date.available	2024-12-12T04:09:52Z
dc.date.issued	2024
dc.identifier.uri	http://hdl.handle.net/10453/182495
dc.description	University of Technology Sydney. Faculty of Engineering and Information Technology.	en_US.UTF-8
dc.description.abstract	In recent years, applications from autonomous navigation of ground mobile robots have been pivotal in exploring the advantages of deploying autonomous robots optimised for outdoor navigation. This approach holds potential across various industries, with a key strength in their ability to navigate efficiently through unstructured terrains. For robots operating in outdoor environments, autonomous navigation plays a major role in defining the effectiveness of an operation. The objective is to enable the platform to autonomously and securely navigate towards the desired destination safely and efficiently. However, this feature comes with challenges and limitations for operators. Factors including obstacle avoidance, the efficiency and reliability of the path planning solution, and the consideration of the platform’s dynamics are the major problems. This thesis introduces a novel local path-planning technique for high-speed off-road robotic vehicles called Adaptive Trajectory Library (ATL). The method integrates a dynamic trajectory library, filtered from the platform’s physical characteristics to match with operation-specified waypoints. The planner ensures the dynamic feasibility of the target platform by implementing pre-defined velocity configurations extracted from the robot’s performance data. ATL can effectively search for feasible trajectories to adapt to the robot’s current state, enhancing its motion’s smoothness. Experimental results demonstrate the controller’s response and effectiveness in maintaining dynamic feasibility at speeds up to 5m/s, showcasing its potential for improving the performance of fast off-road robotic vehicles in practical environments.	en_US.UTF-8
dc.format	Thesis (MSc (CompSc))
dc.language.iso	en_US	en_US.UTF-8
dc.rights	info:eu-repo/semantics/openAccess
dc.rights	The author owns the copyright in this thesis including all reproduction and reuse rights for the work. The work may not be altered without the permission of the copyright owner. Attribution is essential when quoting or paraphrasing from this thesis.
dc.rights	© 2024 Nguyen Thanh Trung Le
dc.rights	au.edu.uts.lib/cph
dc.title	Dynamically-feasible real-time local planning for fast outdoor robots	en_US.UTF-8
dc.type	Thesis
utslib.copyright.status	open_access	*

Abstract:

In recent years, applications from autonomous navigation of ground mobile robots have been pivotal in exploring the advantages of deploying autonomous robots optimised for outdoor navigation. This approach holds potential across various industries, with a key strength in their ability to navigate efficiently through unstructured terrains. For robots operating in outdoor environments, autonomous navigation plays a major role in defining the effectiveness of an operation. The objective is to enable the platform to autonomously and securely navigate towards the desired destination safely and efficiently. However, this feature comes with challenges and limitations for operators. Factors including obstacle avoidance, the efficiency and reliability of the path planning solution, and the consideration of the platform’s dynamics are the major problems. This thesis introduces a novel local path-planning technique for high-speed off-road robotic vehicles called Adaptive Trajectory Library (ATL). The method integrates a dynamic trajectory library, filtered from the platform’s physical characteristics to match with operation-specified waypoints. The planner ensures the dynamic feasibility of the target platform by implementing pre-defined velocity configurations extracted from the robot’s performance data. ATL can effectively search for feasible trajectories to adapt to the robot’s current state, enhancing its motion’s smoothness. Experimental results demonstrate the controller’s response and effectiveness in maintaining dynamic feasibility at speeds up to 5m/s, showcasing its potential for improving the performance of fast off-road robotic vehicles in practical environments.

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http://hdl.handle.net/10453/182495