Vision-Based Autonomous Underwater Swimming in Dense Coral for Combined Collision Avoidance and Target Selection

Manderson, Travis; Higuera, Juan Camilo Gamboa; Cheng, Ran; Dudek, Gregory

doi:10.1109/iros.2018.8594410

Cited by 34 publications

(25 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Manderson et al [63] proposed a model to control an underwater vehicle based on Convolutional Neural Network (CNN). Consisting of five layers that finally determine the yaw and pitch angles.…”

Section: Figure 4 Planning Procedures 4 Related Work For Controlling Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Vision-Based Mobile Robot Controllers: A Scientific Review

Abdulazeez¹

2021

TURCOMAT

View full text Add to dashboard Cite

Today, there are different types of self-controlled robots. Some of them had critical effects on our lives like industrial and medical robots. Others are for military usages such as drones and the pets robots just for entertainment. The crucial differences between this kind of robot and the controlled ones are their ability to move on their own and make decisions based on their observations of the world around them. Mobile robots must have a data source used as an input dataset and processed to change their behavior; for instance, moving, stopping, rotating, or doing any required action based on the information gathered from the surrounding environment. Different types of sensors were used to feed robots controllers with data. Such data source could be ultrasonic sensor, laser sensor, torque sensor, or vision sensor. Robots integrated with cameras were becoming an essential field of study. They recently attracted significant attention from researchers it has been commonly used in many sectors of healthcare, manufacturing, and many other services. The robot needs a controller with a powerful mechanism of realization to deal with such incoming data. The world of mobile robot controllers is discussed in this paper, and the latest trends were reviewed. This review aims to provide a general understanding of robot controllers and navigation methods developed over the last few years.

show abstract

Section: Figure 4 Planning Procedures 4 Related Work For Controlling Methodsmentioning

confidence: 99%

“…Shkurti et al [64] introduced a scheme near the one proposed in [63]. However, it can be deployed to serval robots to collaborate to perform the navigation task.…”

Section: Figure 4 Planning Procedures 4 Related Work For Controlling Methodsmentioning

confidence: 99%

Vision-Based Mobile Robot Controllers: A Scientific Review

Abdulazeez¹

2021

TURCOMAT

View full text Add to dashboard Cite

show abstract

“…Recent work from different groups has emphasized the potential of the Aqua2 [8] platform by providing effective real-time underwater navigation methods. Manderson et al [39] provided a deep learning-based approach for collision avoidance by training upon the decisions of a human operator, Hong et al [40] utilized deep learning for classifying obstacles to static and dynamic on top of a potential field-based planner for obstacle avoidance, while Xanthidis et al…”

Section: Related Workmentioning

confidence: 99%

“…Finally, on the front of perception-aware underwater navigation, Manderson et al [41] provided an extension to their previous work. Similarly to [39], this deep-learning technique was based on fitting on data collected by a human operator controlling the robot. The robot was taught to stay close to corals, and avoid collisions with corals and rocks.…”

Section: Related Workmentioning

confidence: 99%

AquaVis: A Perception-Aware Autonomous Navigation Framework for Underwater Vehicles

Xanthidis¹,

Kalaitzakis²,

Karapetyan³

et al. 2021

Preprint

View full text Add to dashboard Cite

Visual monitoring operations underwater require both observing the objects of interest in close-proximity, and tracking the few feature-rich areas necessary for state estimation. This paper introduces the first navigation framework, called AquaVis, that produces on-line visibility-aware motion plans that enable Autonomous Underwater Vehicles (AUVs) to track multiple visual objectives with an arbitrary camera configuration in real-time. Using the proposed pipeline, AUVs can efficiently move in 3D, reach their goals while avoiding obstacles safely, and maximizing the visibility of multiple objectives along the path within a specified proximity. The method is sufficiently fast to be executed in real-time and is suitable for single or multiple camera configurations. Experimental results show the significant improvement on tracking multiple automaticallyextracted points of interest, with low computational overhead and fast re-planning times.Accompanying short video: https://youtu.be/JKO bbrIZyU

show abstract

“…In the beginning basic patterns were used, then complex swimming gaits were developed in order to perform patterns such as swimming on the side, in a corkscrew motion, or performing a barrel roll [37]. Visual tags placed on structures were used to enable the AUV to navigate [38], while a learned reactive controller had the vehicle maintain safe distance while moving over a coral reef [39], [40].…”

Section: Related Workmentioning

confidence: 99%

Navigation in the Presence of Obstacles for an Agile Autonomous Underwater Vehicle

Xanthidis

Karapetyan

Damron

et al. 2020

2020 IEEE International Conference on Robotics and Automation (ICRA)

View full text Add to dashboard Cite

Navigation underwater traditionally is done by keeping a safe distance from obstacles, resulting in "fly-overs" of the area of interest. An Autonomous Underwater Vehicle (AUV) moving through a cluttered space, such as a shipwreck, or a decorated cave is an extremely challenging problem and has not been addressed in the past. This paper proposed a novel navigation framework utilizing an enhanced version of Trajopt for fast 3D path-optimization with near-optimal guarantees for AUVs. A sampling based correction procedure ensures that the planning is not limited by local minima, enabling navigation through narrow spaces. The method is shown, both on simulation and in-pool experiments, to be fast enough to enable real-time autonomous navigation for an Aqua2 AUV with strong safety guarantees.

show abstract

Vision-Based Autonomous Underwater Swimming in Dense Coral for Combined Collision Avoidance and Target Selection

Cited by 34 publications

References 17 publications

Vision-Based Mobile Robot Controllers: A Scientific Review

Vision-Based Mobile Robot Controllers: A Scientific Review

AquaVis: A Perception-Aware Autonomous Navigation Framework for Underwater Vehicles

Navigation in the Presence of Obstacles for an Agile Autonomous Underwater Vehicle

Contact Info

Product

Resources

About