Monocular Based Navigation System for Autonomous Ground Robots Using Multiple Deep Learning Models

Machkour, Zakariae; Ortiz-Arroyo, Daniel; Durdevic, Petar

doi:10.1007/s44196-023-00250-5

Cited by 3 publications

(1 citation statement)

References 59 publications

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“…In area of autonomous navigation, several methods are being explored based on use of one or multiple sensors such as GPS, cameras, lidar, radar and other dead reckoning methods [1] depending on applications. One recent research has utilized deep neural networks for vison-based navigation in unknown indoor environments [2]. Another studied energy efficient motion planning of forklifts transportation utilizing Deep neural networks (DNNs) [3].…”

Section: Introductionmentioning

confidence: 99%

Outdoor Tests of Autonomous Navigation System Based on Two Different Reference Points of PurePursuit Algorithm for 10-ton Articulated Vehicle

Latif,

Lindbäck,

Lideskog

et al. 2024

IEEE Access

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This work presents outdoor test results of autonomous navigation system based on two different reference points of PurePursuit algorithm for a 10-ton articulated research platform. PurePursit is commonly used simplified tracking algorithm based on geometric calculation of desired steering angle by pursuing certain number of points/distance ahead on given path from a fixed reference point of vehicle. Choice of fixed reference point effects the tracking accuracy particularly for articulated/center-steered vehicles. In this experimental work, PurePursuit algorithm with a virtual reference point(PPV) and commonly used front-axle reference point(PPF) is evaluated for heavy duty articulated vehicles in outdoor experiments .Experimental data shows that choice of reference point in PurePursuit algorithm for articulated vehicle has impact on tracking accuracy in terms of crosstrack errors and heading errors. Navigation tests were performed on a flat asphalt surface for paths of sharp complexities i-e a path with continuous curvature (circular path) and a path with sharp turns (zigzag path) with different initial conditions i-e initial position of vehicle. In general, it can be concluded that PurePursuit algorithm with front reference point (PPF) produced fewer crosstrack errors while PurePursuit algorithm with virtual midpoint reference (PPV) produced fewer heading errors.

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Section: Introductionmentioning

confidence: 99%

Outdoor Tests of Autonomous Navigation System Based on Two Different Reference Points of PurePursuit Algorithm for 10-ton Articulated Vehicle

Latif,

Lindbäck,

Lideskog

et al. 2024

IEEE Access

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A Region-Based Approach to Monocular Mapless Navigation Using Deep Learning Techniques

Machkour,

Ortiz-Arroyo,

Durdevic

2023

2023 9th International Conference on Control, Decision and Information Technologies (CoDIT)

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In recent years, there have been significant advances in navigation methods for autonomous robotic systems, giving rise to a diverse range of navigation techniques. These techniques include GPS-based, SLAM-based, and monocular depth-based navigation. However, each of these approaches has its limitations. Typically, these techniques rely on either external sensors and positioning systems or require the creation of a local map prior to initiating navigation. This paper introduces a new approach for autonomous navigation of ground robots: mapless navigation using a pre-trained monocular depth network. This technique offers an efficient and cost-effective way of navigating without the need for a pre-existing map of the environment. To evaluate and compare the performance of our method, we conducted experiments using two different depth estimation models tested within the Gazebo simulation environment.

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Advancements in Learning-Based Navigation Systems for Robotic Applications in MRO Hangar: Review

Adiuku,

Avdelidis,

Tang

et al. 2024

Sensors

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The field of learning-based navigation for mobile robots is experiencing a surge of interest from research and industry sectors. The application of this technology for visual aircraft inspection tasks within a maintenance, repair, and overhaul (MRO) hangar necessitates efficient perception and obstacle avoidance capabilities to ensure a reliable navigation experience. The present reliance on manual labour, static processes, and outdated technologies limits operation efficiency in the inherently dynamic and increasingly complex nature of the real-world hangar environment. The challenging environment limits the practical application of conventional methods and real-time adaptability to changes. In response to these challenges, recent years research efforts have witnessed advancement with machine learning integration aimed at enhancing navigational capability in both static and dynamic scenarios. However, most of these studies have not been specific to the MRO hangar environment, but related challenges have been addressed, and applicable solutions have been developed. This paper provides a comprehensive review of learning-based strategies with an emphasis on advancements in deep learning, object detection, and the integration of multiple approaches to create hybrid systems. The review delineates the application of learning-based methodologies to real-time navigational tasks, encompassing environment perception, obstacle detection, avoidance, and path planning through the use of vision-based sensors. The concluding section addresses the prevailing challenges and prospective development directions in this domain.

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Monocular Based Navigation System for Autonomous Ground Robots Using Multiple Deep Learning Models

Cited by 3 publications

References 59 publications

Outdoor Tests of Autonomous Navigation System Based on Two Different Reference Points of PurePursuit Algorithm for 10-ton Articulated Vehicle

Outdoor Tests of Autonomous Navigation System Based on Two Different Reference Points of PurePursuit Algorithm for 10-ton Articulated Vehicle

A Region-Based Approach to Monocular Mapless Navigation Using Deep Learning Techniques

Advancements in Learning-Based Navigation Systems for Robotic Applications in MRO Hangar: Review

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