CNN-Based Obstacle Avoidance Using RGB-Depth Image Fusion

Mechal, Chaymae El; Idrissi, Najiba El Amrani El; Mesbah, Mostefa

doi:10.1007/978-981-33-6893-4_78

Cited by 3 publications

(3 citation statements)

References 11 publications

(13 reference statements)

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“…For instance, Qi et al [ 93 ] developed a modern CNN technique to identify and classify obstacles in complex environments; they highlighted the improvements in obstacle identification. Additionally, Mechal et al [ 94 ] presented a CNN model trained with different types of images, such as RGBD, RGB and HSD, enabling the classification of obstacle avoidance actions. The authors of [ 93 , 95 ] used CNN to estimate the depth of objects in an image to consequently steer commands for a quadrotor.…”

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 The present reliance on manual labour, static processes, and outdated technologies limits operation efficiency in the inherently dynamic and increasingly complex nature of the real-world hangar environment.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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“…One prominent application is object recognition and classification, where CNNs excel at identifying and categorizing objects within the mobile robot's environment [53]. Another crucial role that CNNs play in mobile robotics is obstacle detection and avoidance, contributing significantly to navigation [54][55][56][57][58]. By leveraging CNNs, robots can discern obstacles in their path and make informed decisions to navigate around them safely.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Elevating Mobile Robotics: Pioneering Applications of Artificial Intelligence and Machine Learning

Nasrallah,

Stepanyan,

Nassrullah

et al. 2024

RIA

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The present study delves into the utilization of subsumption architecture for the modeling of mobile robot behaviors, particularly those that respond adaptively to environmental dynamics and inaccuracies in sensor measurements. Central to this investigation is the deployment of reactive controller networks, wherein each node-representing a distinct state-is governed by sensor-triggered conditions that dictate state transitions. The methodology adopted comprises a thorough literature review, encompassing sources from IEEE Xplore, ScienceDirect, and the ACM Digital Library, which discuss the integration of subsumption architecture in the realm of mobile robot control. Through this review, the effectiveness of subsumption architecture in crafting reactive robotic behaviors is underscored. It has been established that augmented finite state machines (AFSMs), which are integral to the subsumption architecture and possess internal timing mechanisms, are pivotal in managing the temporal aspects of state transitions. Additionally, the technique of layering-merging multiple simple networks to form intricate behavior patterns-emerges as a significant finding, accentuating the architecture's capability to facilitate complex behavioral constructs. The prime contribution of this body of work lies in identifying and elucidating the strategic role of subsumption architecture in enhancing the adaptability and robustness of mobile robots. The insights gleaned from this study not only advance our understanding of robotic control systems but also hold implications for the amplification of industrial efficiency and effectiveness through the application of sophisticated AI and machine learning techniques in mobile robotics.

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“…The CNN algorithm has been used for obstacle avoidance applications in many devices. Mechal et al [27] applied CNN using RGB Depth Image Fusion for obstacle avoidance applications in robots. Duan et al [28] have applied the same algorithm to experiment with real-time computer vision for obstacle detection in automatic lawnmower applications.…”

Section: Introductionmentioning

confidence: 99%

Implementation of Convolutional Neural Network (CNN) Algorithm for Autonomous Robot

Saptaji

2024

JMechE

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In Indonesia, the development of autonomous robots has emerged intensively since the last coronavirus pandemic, especially the autonomous UV disinfection (A-UV) robot. A-UV disinfection robot has the purpose of purifying germs and pathogens in critical areas, such as the hospital. As the minuscule creature can be difficult to control, the anticipation of letting no human have contact with it is one of the other purposes of the A-UV disinfection robot. However, the systematic development of the autonomous robot is the priority, where the robot can offer a collision-free obstacle, and target-lock when arriving at the designated location. In this study, two main contributions are proposed to develop the autonomous robot: 1) Convolutional Neural Network (CNN) algorithm to learn the potential surrounding the lock area from the dataset to ensure collision-free during the operation. 2) Original design to ensure the compactness of the autonomous robot with almost omnidirectional UV light. We design the surrounding area with “BOX” as the obstacle and “SIGN STOP” as the target in our CNN dataset. The performance is validated to have 97% and 99% for training and validation performance and 0.3% for loss. The robot prototype was also developed and tested inside a workspace with a size of 2.1 x 3 m. The robot prototype successfully performed the required tasks.

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CNN-Based Obstacle Avoidance Using RGB-Depth Image Fusion

Cited by 3 publications

References 11 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

Elevating Mobile Robotics: Pioneering Applications of Artificial Intelligence and Machine Learning

Implementation of Convolutional Neural Network (CNN) Algorithm for Autonomous Robot

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