Obstacle Avoidance Approaches for Autonomous Navigation of Unmanned Surface Vehicles

Polvara, Riccardo; Sharma, Sanjay; Wan, Jian; Manning, Andrew J.; Sutton, Robert I.

doi:10.1017/s0373463317000753

Cited by 90 publications

(51 citation statements)

References 67 publications

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“…GNC module interaction and signal flow (Fossen, 2011, p. 233) Given the interrelation, applications of semiautonomous vessels, both real (full-scale) and virtual (simulators), require a package of GNC and hydrodynamics technologies interacting together. It is widely agreed that the performance of methods is largely altered by uncertainties coming from environmental loads and ship dynamics (LaValle, 2011;Liu et al, 2016;Polvara et al, 2018).…”

Section: Resultsmentioning

confidence: 99%

“…A characteristic requirement of local path re-planning is the low computational requirements, where real-time methods such as lineof-sight (LOS) and potential fields are common. Polvara et al (2018) presented a review of global and local planning methods including a section for advanced computing-based methods. The author stated that almost all of the methods reviewed did not consider uncertainties due to environment loads and vehicle dynamics.…”

Section: Topic 2: Guidancementioning

confidence: 99%

See 1 more Smart Citation

Is Full-autonomy the Way to Go Towards Maximizing the Ocean Potentials?

Zghyer¹,

Ostnes²,

Halse³

2019

TransNav

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Growth prospects for ocean economy are promising because ocean industries are addressing challenges such as food security, energy security and climate change. However, safety and efficiency are the general challenges of ocean operations. Increased automation is believed to solve these problems. This paper discusses the impact of automation on safety and efficiency. A literature review of 'Human factors' mainly from the aviation and maritime industries is presented to untangle the human-machine relationship characteristics when increased automation is introduced to operators. A literature review of Hydrodynamics, Guidance, Navigation and Control (GNC) technologies is presented to introduce the state-of-art and associated limitations. It is concluded that, if the industry's drive is safety and efficiency, then full-autonomy is, at present, not the way to go. Remote control, instead, could facilitate a feasible future, while focused research and development are in need.

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Section: Resultsmentioning

confidence: 99%

Section: Topic 2: Guidancementioning

confidence: 99%

Is Full-autonomy the Way to Go Towards Maximizing the Ocean Potentials?

Zghyer¹,

Ostnes²,

Halse³

2019

TransNav

View full text Add to dashboard Cite

show abstract

“…Traditionally, anti-collision systems for autonomous vehicles have relied on detection by a set of laser rangefinders, sometimes mounted on a servo, for measuring distances to objects around a vehicle, as proposed in Reference [17]. This approach is satisfactory in many applications, and in general has proved to work well both separately [18][19][20] and as part of a data fusion system [21]. It does, however, have some well-known disadvantages.…”

Section: Related Workmentioning

confidence: 99%

Shore Construction Detection by Automotive Radar for the Needs of Autonomous Surface Vehicle Navigation

Stateczny

Kazimierski

Burdziakowski

et al. 2019

IJGI

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Autonomous surface vehicles (ASVs) are becoming more and more popular for performing hydrographic and navigational tasks. One of the key aspects of autonomous navigation is the need to avoid collisions with other objects, including shore structures. During a mission, an ASV should be able to automatically detect obstacles and perform suitable maneuvers. This situation also arises in near-coastal areas, where shore structures like berths or moored vessels can be encountered. On the other hand, detection of coastal structures may also be helpful for berthing operations. An ASV can be launched and moored automatically only if it can detect obstacles in its vicinity. One commonly used method for target detection by ASVs involves the use of laser rangefinders. The main disadvantage of this approach is that such systems perform poorly in conditions with bad visibility, such as in fog or heavy rain. Therefore, alternative methods need to be sought. An innovative approach to this task is presented in this paper, which describes the use of automotive three-dimensional radar on a floating platform. The goal of the study was to assess target detection possibilities based on a comparison with photogrammetric images obtained by an unmanned aerial vehicle (UAV). The scenarios considered focused on analyzing the possibility of detecting shore structures like berths, wooden jetties, and small houses, as well as natural objects like trees or other kinds of vegetation. The recording from the radar was integrated into a single complex radar image of shore targets. It was then compared with an orthophotomap prepared from AUV camera pictures, as well as with a map based on traditional land surveys. The possibility and accuracy of detection for various types of shore structure were statistically assessed. The results show good potential for the proposed approach—in general, objects can be detected using the radar—although there is a need for development of further signal processing algorithms.

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“…These methods have dramatically improved the state-of-the-art in visual object recognition [103], motion detection [104] and many other domains such as autonomous navigation [105], medical diagnosis [106], etc. The first unsupervised learning procedures used Restricted Boltzmann Machines (RBM) to restrict the connectivity of the hidden units in order to make learning easier.…”

Section: An Overview Of Deep Learningmentioning

confidence: 99%

State-of-the-Art Mobile Intelligence: Enabling Robots to Move Like Humans by Estimating Mobility with Artificial Intelligence

Kong

Bai

et al. 2018

Applied Sciences

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Mobility is a significant robotic task. It is the most important function when robotics is applied to domains such as autonomous cars, home service robots, and autonomous underwater vehicles. Despite extensive research on this topic, robots still suffer from difficulties when moving in complex environments, especially in practical applications. Therefore, the ability to have enough intelligence while moving is a key issue for the success of robots. Researchers have proposed a variety of methods and algorithms, including navigation and tracking. To help readers swiftly understand the recent advances in methodology and algorithms for robot movement, we present this survey, which provides a detailed review of the existing methods of navigation and tracking. In particular, this survey features a relation-based architecture that enables readers to easily grasp the key points of mobile intelligence. We first outline the key problems in robot systems and point out the relationship among robotics, navigation, and tracking. We then illustrate navigation using different sensors and the fusion methods and detail the state estimation and tracking models for target maneuvering. Finally, we address several issues of deep learning as well as the mobile intelligence of robots as suggested future research topics. The contributions of this survey are threefold. First, we review the literature of navigation according to the applied sensors and fusion method. Second, we detail the models for target maneuvering and the existing tracking based on estimation, such as the Kalman filter and its series developed form, according to their model-construction mechanisms: linear, nonlinear, and non-Gaussian white noise. Third, we illustrate the artificial intelligence approach-especially deep learning methods-and discuss its combination with the estimation method.

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Obstacle Avoidance Approaches for Autonomous Navigation of Unmanned Surface Vehicles

Cited by 90 publications

References 67 publications

Is Full-autonomy the Way to Go Towards Maximizing the Ocean Potentials?

Is Full-autonomy the Way to Go Towards Maximizing the Ocean Potentials?

Shore Construction Detection by Automotive Radar for the Needs of Autonomous Surface Vehicle Navigation

State-of-the-Art Mobile Intelligence: Enabling Robots to Move Like Humans by Estimating Mobility with Artificial Intelligence

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