Special Issue on Mobile Robots Navigation

Reinoso, Óscar; Payá, Luis

doi:10.3390/app10041317

Cited by 11 publications

(3 citation statements)

References 14 publications

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“…In rough terrain, the environment can rarely be divided into obstacles and free space [ 21 , 22 ]. In the case of UGV, the terrain will have varying degrees of traversability depending on the terrain’s characteristics and the ability of the UGV to traverse the terrain.…”

Section: Related Workmentioning

confidence: 99%

Hyperspectral Imaging for Mobile Robot Navigation

Jakubczyk

Siemiątkowska

Więckowski

et al. 2022

Sensors

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The article presents the application of a hyperspectral camera in mobile robot navigation. Hyperspectral cameras are imaging systems that can capture a wide range of electromagnetic spectra. This feature allows them to detect a broader range of colors and features than traditional cameras and to perceive the environment more accurately. Several surface types, such as mud, can be challenging to detect using an RGB camera. In our system, the hyperspectral camera is used for ground recognition (e.g., grass, bumpy road, asphalt). Traditional global path planning methods take the shortest path length as the optimization objective. We propose an improved A* algorithm to generate the collision-free path. Semantic information makes it possible to plan a feasible and safe path in a complex off-road environment, taking traveling time as the optimization objective. We presented the results of the experiments for data collected in a natural environment. An important novelty of this paper is using a modified nearest neighbor method for hyperspectral data analysis and then using the data for path planning tasks in the same work. Using the nearest neighbor method allows us to adjust the robotic system much faster than using neural networks. As our system is continuously evolving, we intend to examine the performance of the vehicle on various road surfaces, which is why we sought to create a classification system that does not require a prolonged learning process. In our paper, we aimed to demonstrate that the incorporation of a hyperspectral camera can not only enhance route planning but also aid in the determination of parameters such as speed and acceleration.

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Section: Related Workmentioning

confidence: 99%

Hyperspectral Imaging for Mobile Robot Navigation

Jakubczyk

Siemiątkowska

Więckowski

et al. 2022

Sensors

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“…Navigation, in this context, encompasses the execution of the planned path, which necessitates continuous updates to the robot's position and precise trajectory adjustments. This section also delves into an exploration of the frameworks employed within the mobile robot navigation field [14]. Finally, the overarching element of control takes center stage.…”

Section: Introductionmentioning

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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“…In this context, map building and localization are two of the main abilities a robot must develop to be really autonomous. Finding a solution to both problems, balancing accuracy, efficiency and robustness, is very important so that a robot can navigate autonomously and safely through real working environments [ 1 ].…”

Section: Introductionmentioning

confidence: 99%

The Role of Global Appearance of Omnidirectional Images in Relative Distance and Orientation Retrieval

Román

Payá

Peidró

et al. 2021

Sensors

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Over the last few years, mobile robotics has experienced a great development thanks to the wide variety of problems that can be solved with this technology. An autonomous mobile robot must be able to operate in a priori unknown environments, planning its trajectory and navigating to the required target points. With this aim, it is crucial solving the mapping and localization problems with accuracy and acceptable computational cost. The use of omnidirectional vision systems has emerged as a robust choice thanks to the big quantity of information they can extract from the environment. The images must be processed to obtain relevant information that permits solving robustly the mapping and localization problems. The classical frameworks to address this problem are based on the extraction, description and tracking of local features or landmarks. However, more recently, a new family of methods has emerged as a robust alternative in mobile robotics. It consists of describing each image as a whole, what leads to conceptually simpler algorithms. While methods based on local features have been extensively studied and compared in the literature, those based on global appearance still merit a deep study to uncover their performance. In this work, a comparative evaluation of six global-appearance description techniques in localization tasks is carried out, both in terms of accuracy and computational cost. Some sets of images captured in a real environment are used with this aim, including some typical phenomena such as changes in lighting conditions, visual aliasing, partial occlusions and noise.

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Special Issue on Mobile Robots Navigation

Abstract: In recent years, the presence of mobile robots in diverse scenarios has considerably increased, to solve a variety of tasks [...]

Cited by 11 publications

References 14 publications

Hyperspectral Imaging for Mobile Robot Navigation

Hyperspectral Imaging for Mobile Robot Navigation

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

The Role of Global Appearance of Omnidirectional Images in Relative Distance and Orientation Retrieval

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