CNN-Based Vision Model for Obstacle Avoidance of Mobile Robot

Liu, Canglong; Zheng, Bin; Wang, Chunyang; Zhao, Yongting; Fu, Shun; Li, Haochen

doi:10.1051/matecconf/201713900007

Cited by 17 publications

(14 citation statements)

References 9 publications

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“…This study provided evidence demonstrating the superior performances of CNNs compared with traditional methods. Similarly, Liu et al [ 96 ] proposed a CNN vision-based model for obstacle avoidance, aiming to generate steering commands for a mobile robot while reducing the need for complex and time-consuming hand-engineering of features. The ability of CNN to learn feature representations directly from raw image data without the need for manual feature extraction marks a significant advancement in the field of object detection and has led to widespread adoption in various applications, including autonomous vehicles and robotics.…”

Section: Learning-based Navigation Techniques (Methods)mentioning

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 The present reliance on manual labour, static processes, and outdated technologies limits operation efficiency in the inherently dynamic and increasingly complex nature of the real-world hangar environment.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: Learning-based Navigation Techniques (Methods)mentioning

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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show abstract

“…Also, an improvement in analyzing time was noticed. Liu et al [45] used a Convolutional Neural Network (CNN) to build an end-to-end paradigm as an obstacle avoidance controller in a mobile robot. The presented model contains 5 CNN layers followed by three fully connected layers.…”

Section: Figure 4 Planning Procedures 4 Related Work For Controlling Methodsmentioning

confidence: 99%

“…Bakken et al [46] worked on almost the same idea as [45] in building a model, but they design their robot to works in the agriculture section (crop row-following). In the test results, they also referred to the accuracy of the presented model.…”

Section: Figure 4 Planning Procedures 4 Related Work For Controlling Methodsmentioning

confidence: 99%

Vision-Based Mobile Robot Controllers: A Scientific Review

Abdulazeez¹

2021

TURCOMAT

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Today, there are different types of self-controlled robots. Some of them had critical effects on our lives like industrial and medical robots. Others are for military usages such as drones and the pets robots just for entertainment. The crucial differences between this kind of robot and the controlled ones are their ability to move on their own and make decisions based on their observations of the world around them. Mobile robots must have a data source used as an input dataset and processed to change their behavior; for instance, moving, stopping, rotating, or doing any required action based on the information gathered from the surrounding environment. Different types of sensors were used to feed robots controllers with data. Such data source could be ultrasonic sensor, laser sensor, torque sensor, or vision sensor. Robots integrated with cameras were becoming an essential field of study. They recently attracted significant attention from researchers it has been commonly used in many sectors of healthcare, manufacturing, and many other services. The robot needs a controller with a powerful mechanism of realization to deal with such incoming data. The world of mobile robot controllers is discussed in this paper, and the latest trends were reviewed. This review aims to provide a general understanding of robot controllers and navigation methods developed over the last few years.

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“…2: Factory floor and cluttered room indoors [49] Factory floor 230 13 [34] Corridor indoors with few obstacles 340 7 [35] Corridor, kitchen and laboratory space 600 2 [36] Cluttered corridor indoors 7000 - [50] Cluttered maze-like indoor environment 4000 7 [51] Room with few obstacles 100 4 [52] Corridor indoors 400 55…”

Section: Approximate Sensors Cost (Gbp)mentioning

confidence: 99%

End-to-End Learning for Visual Navigation of Forest Environments

Niu

Zauner

Tarapore

2023

Forests

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Off-road navigation in forest environments is a challenging problem in field robotics. Rovers are required to infer their traversability over a priori unknown and dynamically changing forest terrain using noisy onboard navigation sensors. The problem is compounded for small-sized rovers, such as that of a swarm. Their size-proportional low-viewpoint affords them a restricted view for navigation, which may be partially occluded by forest vegetation. Hand-crafted features, typically employed for terrain traversability analysis, are often brittle and may fail to discriminate obstacles in varying lighting and weather conditions. We design a low-cost navigation system tailored for small-sized forest rovers using self-learned features. The MobileNet-V1 and MobileNet-V2 models, trained following an end-to-end learning approach, are deployed to steer a mobile platform, with a human-in-the-loop, towards traversable paths while avoiding obstacles. Receiving a 128 × 96 pixel RGB image from a monocular camera as input, the algorithm running on a Raspberry Pi 4, exhibited robustness to motion blur, low lighting, shadows and high-contrast lighting conditions. It was able to successfully navigate a total of over 3 km of real-world forest terrain comprising shrubs, dense bushes, tall grass, fallen branches, fallen tree trunks, and standing trees, in over five different weather conditions and four different times of day.

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CNN-Based Vision Model for Obstacle Avoidance of Mobile Robot

Cited by 17 publications

References 9 publications

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

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

Vision-Based Mobile Robot Controllers: A Scientific Review

End-to-End Learning for Visual Navigation of Forest Environments

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