Autonomous Underwater Vehicle for Vision Based Tracking

Kumar, Gourav; Painumgal, Unnikrishnan V.; Kumar, Mohinder; RAJESH, K.

doi:10.1016/j.procs.2018.07.021

Cited by 21 publications

(13 citation statements)

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“…It has been also exploited by other techniques and applications such as mapping and geographic information systems to extract certain regions [31]. In robotics, DIP has been very widely exploited [32] to implement very crucial images-based tasks [31,[33][34][35][36][37] e.g., to do a 3D-based motion detection and multi-frame images recognition [38], to implement a vision-based tracking process for underwater vehicles [39], to enable an image sketching procedure from which a selected robot can benefit [32], or to enable the robot move to autonomously detect shadows of a region-of-interest (ROI) objects [40]. It has also been used by machine translation to measure the distance between an object and machine for a better focus calculation [41] or to propose a spectral-spatial classification method [42].…”

Section: Literature Reviewmentioning

confidence: 99%

“…The PICA computation time in a relation to total computation times for the three methods is calculated using (39):…”

Section: ) Computation Time-based Enhancement Percentage Compared Tomentioning

confidence: 99%

“…where | is rate of computation time of PICA. By using (39), | is  7.9, 11.2, and 11.4% for images' sizes 352×288, 640×480, and 726×544, respectively. From this, it is clear that rate(s) of computation time for both ref.…”

Section: ) Computation Time-based Enhancement Percentage Compared Tomentioning

confidence: 99%

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Pixel Intensity-Based Contrast Algorithm (PICA) for Image Edges Extraction (IEE)

et al. 2020

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In this paper, images' pixels are exploited to extract objects' edges. This paper has proposed a Pixel Intensity based Contrast Algorithm (PICA) for Image Edges Extraction (IEE). This paper highlights three contributions. Firstly, IEE process is fast and PICA has less computation time when processing different images' sizes. Secondly, IEE is simple and uses a 2×4 mask which is different from other masks where it doesn't require while-loop(s) during processing images. Instead, it has adopted an if-conditional procedure to reduce the code complexity and enhance computation time. That is, the reason why this design is faster than other designs and how it contributes to IEE will be explained. Thirdly, design and codes of IEE and its mask are available, made an open source, and in-detail presented and supported by an interactive file; it is simulated in a video motion design. One of the PICA's features and contributions is that PICA has adopted to use less while-loop(s) than traditional methods and that has contributed to the computation time and code complexity. Experiments have tested 526 samples with different images' conditions e.g., inclined, blurry, and complexbackground images to evaluate PICA's performance in terms of computation time, enhancement rate for processing a single image, accuracy, and code complexity.By comparing PICA to other research works, PICA consumes 5.7 mS to process a single image which is faster and has less code complexity by u×u. Results have shown that PICA can accurately detect edges under different images' conditions. Results have shown that PICA has enhanced computation time rate for processing a single image by 92.1% compared to other works. PICA has confirmed it is accurate and robust under different images' conditions. PICA can be used with several types of images e.g., medical images and useful for real-time applications.

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Section: Literature Reviewmentioning

confidence: 99%

“…The PICA computation time in a relation to total computation times for the three methods is calculated using (39):…”

Section: ) Computation Time-based Enhancement Percentage Compared Tomentioning

confidence: 99%

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Pixel Intensity-Based Contrast Algorithm (PICA) for Image Edges Extraction (IEE)

et al. 2020

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“…Taking the vacuum cleaner robot as a path planning example, which sweeps each accessible area within the indoor environment, this mechanism is referred as a coverage region path planning [10]. For more path planning examples, there are increasing trends towards developing Autonomous Underwater Vehicles to reduce the costs of ocean survey and also minimize the risks of sending humans to the hostile environment in the deep seas with unpredictable weather state and the higher pressure at greater depth [11]. In addition, path planning is used also for Unmanned Aerial Vehicles (UAVs), which indicates that according to the UAV maneuver performance, hit probability, penetration probability, flight timing within bounds, it can help to search for a feasible or optimal path from any initial/starting point to the final/target point flight [12].…”

Section: Introductionmentioning

confidence: 99%

A Novel Path Planning System for Guidance of Blind People using Integration of GA and A* Techniques

Mohamed¹,

E.²,

F.³

2019

IJCA

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It is a challenging task for the blind people to perceive indoor environment information and walk independently. Path planning is a crucial requirement to provide constant assistance for blind people to navigate from one location to another. This paper presents a novel system for path planning to assist blind people for walking through the indoor environment and avoiding the obstacles. The algorithm is based on genetic algorithm and A* algorithm to select the shortest path that achieves the lowest computational time. A comparison a mong the GA, A* and the new algorithm shows that is faster and shorter than the previous algorithms.

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“…Unlike other floating vehicles, due to the principle of hovercraft, the friction between the vehicle and water surface is avoided and there by a considerable amount of energy can be saved. (Kumar et al, 2018) The vertical take-off and landing ability of vehicle can position the vehicle in precise water locations across rivers, ponds and other water bodies to collect water samples. Design of amphibious vehicle with sufficient strength characteristics (Gunwant, 2019) to withstand lift conditions and carrying necessary payload are considered to be challenging (Solly et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Mechanical Strength Characteristics of Double Ducted Unmanned Amphibious Aerial Vehicle using Finite Element Analysis

Raj

Esakki

Ganesan

2019

Int J Math, Eng, Manag Sci

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Unmanned Aerial Vehicles are extensively exploited for diverse applications importantly surveillance, defence and military, photography. Development of unmanned amphibious vehicle with integrating features of hovercraft principles and multirotor to navigate along and above the water surface, land surface and flying in the air is challenging demand. This article presents conceptual design of amphibious vehicle for the payload capacity of 7 kg with an endurance of 20 minutes and provision for mounting water sampler to collect water samples in remote water bodies. Structural strength characteristics of each part of the amphibious vehicle and integrity of same are analysed by Finite Element Analysis. FEA results indicated that the designed amphibious vehicle structure is well within the stress limit and minimal displacement is obtained. Based on structural analysis materials for various parts of the amphibious vehicle are determined and integrated structure is analysed.

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Autonomous Underwater Vehicle for Vision Based Tracking

Cited by 21 publications

References 1 publication

Pixel Intensity-Based Contrast Algorithm (PICA) for Image Edges Extraction (IEE)

Pixel Intensity-Based Contrast Algorithm (PICA) for Image Edges Extraction (IEE)

A Novel Path Planning System for Guidance of Blind People using Integration of GA and A* Techniques

Evaluation of Mechanical Strength Characteristics of Double Ducted Unmanned Amphibious Aerial Vehicle using Finite Element Analysis

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