An Optimized Path Tracking Approach Considering Obstacle Avoidance and Comfort

Sezer, Volkan

doi:10.1007/s10846-022-01636-x

Cited by 6 publications

(2 citation statements)

References 31 publications

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“…Machine learning (ML) is an artificial intelligence approach that is at the core of these enabling intelligent technologies with widespread adoption [ 2 ] and has become an essential component in accomplishing many intelligent tasks in robotics. The ML techniques incorporate sensor information fusion, object detection [ 48 ], collision avoidance mechanism [ 49 ], pathfinding [ 50 ], path tracking [ 51 ], and control systems [ 21 ] to solve robot autonomous navigation problems [ 52 ]. Diverse arrays of sensors, including laser scanners, cameras, LiDAR, and others, are leveraged for information gathering, mapping, obstacle detection, as well as robot positions and velocities.…”

Section: Concept and Backgroundmentioning

confidence: 99%

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 The present reliance on manual labour, static processes, and outdated technologies limits operation efficiency in the inherently dynamic and increasingly complex nature of the real-world hangar environment.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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Section: Concept and Backgroundmentioning

confidence: 99%

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 path can be defined using waypoints or through a continuous curve. Path following is often used in scenarios where the wheelchair needs to traverse a known trajectory, such as following a predetermined route in a building or following a designated pathway in an outdoor setting (Sezer 2022). Here is a stepwise explanation of how path following works:…”

Section: Path Followingmentioning

confidence: 99%

Autonomous navigation and obstacle avoidance in smart robotic wheelchairs

Sahoo,

Choudhury

2024

J. Decis. Anal. Int. Comp.

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This review research paper provides a comprehensive analysis of the advancements, challenges, and methodologies in autonomous navigation and obstacle avoidance for smart robotic wheelchairs. The integration of robotics and assistive technology has revolutionized mobility solutions for individuals with impairments, enabling them to navigate complex environments independently. The paper examines the various sensor modalities, machine learning algorithms, and computer vision techniques employed for environment perception and obstacle recognition. It discusses path planning algorithms, motion control strategies, and decision-making processes for autonomous navigation. The review also addresses limitations, such as localization accuracy and dynamic environment modelling, while highlighting recent research advancements and suggesting future directions. Overall, this paper serves as a valuable resource for researchers and practitioners in the field of smart robotic wheelchairs, aiming to enhance mobility and quality of life for individuals with mobility impairments.

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