Journal ArticleDOI
Radar-based collision avoidance for unmanned surface vehicles
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TLDR
This study developed an embedded collision avoidance system based on the marine radar, investigated a highly real-time target detection method which contains adaptive smoothing algorithm and robust segmentation algorithm, developed a stable and reliable dynamic local environment model to ensure the safety of USV navigation, and constructed a collision avoidance algorithm based on velocity obstacle (V-obstacle) which adjusts the USV’s heading and speed in real- time.Abstract:
Unmanned surface vehicles (USVs) have become a focus of research because of their extensive applications. To ensure safety and reliability and to perform complex tasks autonomously, USVs are required to possess accurate perception of the environment and effective collision avoidance capabilities. To achieve these, investigation into realtime marine radar target detection and autonomous collision avoidance technologies is required, aiming at solving the problems of noise jamming, uneven brightness, target loss, and blind areas in marine radar images. These technologies should also satisfy the requirements of real-time and reliability related to high navigation speeds of USVs. Therefore, this study developed an embedded collision avoidance system based on the marine radar, investigated a highly real-time target detection method which contains adaptive smoothing algorithm and robust segmentation algorithm, developed a stable and reliable dynamic local environment model to ensure the safety of USV navigation, and constructed a collision avoidance algorithm based on velocity obstacle (V-obstacle) which adjusts the USV’s heading and speed in real-time. Sea trials results in multi-obstacle avoidance firstly demonstrate the effectiveness and efficiency of the proposed avoidance system, and then verify its great adaptability and relative stability when a USV sailing in a real and complex marine environment. The obtained results will improve the intelligent level of USV and guarantee the safety of USV independent sailing.read more
Citations
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Journal ArticleDOI
Ship collision avoidance methods : State-of-the-art
TL;DR: This paper offers a comprehensive overview of collision prevention techniques based on the three basic processes of determining evasive solutions, namely, motion prediction, conflict detection, and conflict resolution.
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Visual Recognition Based on Deep Learning for Navigation Mark Classification
TL;DR: A fine-grained classification model named RMA (ResNet-Multiscale-Attention) based on deep learning is proposed to analyse the subtle and local differences among navigation mark types for the recognition of navigation marks.
Journal ArticleDOI
A ship collision avoidance system for human-machine cooperation during collision avoidance
TL;DR: A framework of HMI oriented Collision Avoidance System (HMI-CAS) whose decision-making process is interpretable and interactive for human operators and enables the human operators to take over the control of the MASS safely and acknowledges the under-actuated feature of ships.
Journal ArticleDOI
The Empirical Application of Automotive 3D Radar Sensor for Target Detection for an Autonomous Surface Vehicle’s Navigation
TL;DR: An empirical analysis of the surface target detection possibilities in a water environment, which can be used for the future development of tracking and anti-collision systems for autonomous surface vehicles (ASV).
Journal ArticleDOI
Collision Avoidance of Podded Propulsion Unmanned Surface Vehicle With COLREGs Compliance and Its Modeling and Identification
TL;DR: A collision avoidance system (CAS) with COLREGs compliance to improve autonomous navigational ability of USV and the results of collision avoidance under four encounter situations are described in detail.
References
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Motion Planning in Dynamic Environments Using Velocity Obstacles
Paolo Fiorini,Zvi Shiller +1 more
TL;DR: This paper presents a method for robot motion planning in dynamic environments that consists of selecting avoidance maneuvers to avoid static and moving obstacles in the velocity space, based on the rental positions and velocities of the robot and obstacles.
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P. Fiorini,Z. Shiller +1 more
TL;DR: In this paper, the authors present heuristic methods for motion planning in dynamic environments, based on the concept of Velocity Obstacle (VO), which is a heuristic method for motion prediction in a dynamic environment.
Journal IssueDOI
Autonomous driving in urban environments: Boss and the Urban Challenge
Chris Urmson,Joshua Anhalt,Drew Bagnell,Christopher R. Baker,Robert Bittner,Michael Clark,John M. Dolan,D Duggins,Tugrul Galatali,Christopher Geyer,Michele Gittleman,Sam Harbaugh,Martial Hebert,Thomas M. Howard,Sascha Kolski,Alonzo Kelly,Maxim Likhachev,Matthew McNaughton,Nick Miller,Kevin Peterson,Brian Pilnick,Ragunathan Rajkumar,Paul E. Rybski,Bryan Salesky,Young-Woo Seo,Sanjiv Singh,Jarrod M. Snider,Anthony Stentz,William Whittaker,Ziv Wolkowicki,Jason Ziglar,Hong Bae,Thomas G. Brown,Daniel Demitrish,Bakhtiar Brian Litkouhi,Jim Nickolaou,Varsha Sadekar,Wende Zhang,Joshua Struble,Michael Taylor,Michael Darms,Dave Ferguson +41 more
TL;DR: Boss is an autonomous vehicle that uses on-board sensors to track other vehicles, detect static obstacles, and localize itself relative to a road model using a spiral system development process with a heavy emphasis on regular, regressive system testing.
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Linear-time connected-component labeling based on sequential local operations
TL;DR: By comparative evaluations, it has been shown that the efficiency of the proposed algorithm is superior to those of the conventional algorithms.
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TL;DR: This paper develops a reliable algorithm which takes into account the stability of local bandwidth estimates across scales, and demonstrates that, within the large sample approximation, the local covariance is estimated by the matrix that maximizes the magnitude of the normalized mean shift vector.