Hexagonal Grid-Based Framework for Mobile Robot Navigation

Duszak, Piotr; Siemiątkowska, B.; Więckowski, Rafał

doi:10.3390/rs13214216

Cited by 8 publications

(13 citation statements)

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“…The shape of the cell is not essential for dynamic approaches, but the advantages of the shape are preserved. Other publications have already shown the advantages of dividing areas using hexagons [50,51]. Fewer hexagonal sections are needed to represent the map, which reduces the computing time and storage requirements.…”

Section: Vile Operationmentioning

confidence: 99%

“…Another advantage of employing hexagonal areas is that sampling using hexagons results in minimal quantization error for a given sensor's resolution capacity [49]. First, the distance between a particular cell and its immediate neighbors is the same in all six principal directions; curved structures are represented more precisely than rectangular regions [50,51]. In that context, in the knowledge phase, the RSU collects information shared by the CAVs based on position, speed, and orientation.…”

Section: Vile Operationmentioning

confidence: 99%

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Redundancy Mitigation Mechanism for Collective Perception in Connected and Autonomous Vehicles

et al. 2023

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Due to poor local range of the perception and object recognition mechanisms used by autonomous vehicles, incorrect decisions can be made, which can jeopardize a fully autonomous operation. A connected and autonomous vehicle should be able to combine its local perception with the perceptions of other vehicles to improve its capability to detect and predict obstacles. Such a collective perception system aims to expand the field of view of autonomous vehicles, augmenting their decision-making process, and as a consequence, increasing driving safety. Regardless of the benefits of a collective perception system, autonomous vehicles must intelligently select which data should be shared with who and when in order to conserve network resources and maintain the overall perception accuracy and time usefulness. In this context, the operational impact and benefits of a redundancy reduction mechanism for collective perception among connected autonomous vehicles are analyzed in this article. Therefore, we propose a reliable redundancy mitigation mechanism for collective perception services to reduce the transmission of inefficient messages, which is called VILE. Knowledge, selection, and perception are the three phases of the cooperative perception process developed in VILE. The results have shown that VILE is able to reduce it the absolute number of redundant objects of 75% and generated packets by up to 55%. Finally, we discuss possible research challenges and trends.

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Section: Vile Operationmentioning

confidence: 99%

Section: Vile Operationmentioning

confidence: 99%

Redundancy Mitigation Mechanism for Collective Perception in Connected and Autonomous Vehicles

et al. 2023

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“…In this article, we suggest using the diffusion method for path planning [ 1 , 39 ]. The main advantage of the approach is that we can easily consider the cost of driving over different terrains.…”

Section: Application Of Surface Recognition In Collision-free Path Pl...mentioning

confidence: 99%

“…A cell is classified as occupied if the observed height exceeds a certain threshold and is free of obstacles otherwise. Figure 11 presents the map built by the ATENA navigation system [ 1 ]. Red fragments represent areas occupied by obstacles, green—free of obstacles, white—unexplored.…”

Section: Application Of Surface Recognition In Collision-free Path Pl...mentioning

confidence: 99%

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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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