Vision and 2D LiDAR based autonomous surface vehicle docking for identify symbols and dock task in 2016 Maritime RobotX Challenge

2017 ◽  
Author(s):  
Jooho Lee ◽  
Joohyun Woo ◽  
Nakwan Kim
Keyword(s):  
Autonomous Surface Vehicle

scholarly journals A Comparison of Local Path Planning Techniques of Autonomous Surface Vehicles for Monitoring Applications: The Ypacarai Lake Case-study

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1488
Author(s):  
Federico Peralta ◽  
Mario Arzamendia ◽  
Derlis Gregor ◽  
Daniel G. Reina ◽  
Sergio Toral
Keyword(s):  
Path Planning ◽  
Random Trees ◽  
Fast Marching Method ◽  
Fast Marching ◽  
Planning Techniques ◽  
Advantages And Disadvantages ◽  
Autonomous Surface Vehicle ◽  
Local Path Planning ◽  
Custom Made ◽  
Local Path

Local path planning is important in the development of autonomous vehicles since it allows a vehicle to adapt their movements to dynamic environments, for instance, when obstacles are detected. This work presents an evaluation of the performance of different local path planning techniques for an Autonomous Surface Vehicle, using a custom-made simulator based on the open-source Robotarium framework. The conducted simulations allow to verify, compare and visualize the solutions of the different techniques. The selected techniques for evaluation include A*, Potential Fields (PF), Rapidly-Exploring Random Trees* (RRT*) and variations of the Fast Marching Method (FMM), along with a proposed new method called Updating the Fast Marching Square method (uFMS). The evaluation proposed in this work includes ways to summarize time and safety measures for local path planning techniques. The results in a Lake environment present the advantages and disadvantages of using each technique. The proposed uFMS and A* have been shown to achieve interesting performance in terms of processing time, distance travelled and security levels. Furthermore, the proposed uFMS algorithm is capable of generating smoother routes.

Design and Testing of an Universal Autonomous Surface Vehicle

2021 ◽  
Author(s):  
Indrek Roasto ◽  
Heigo Molder ◽  
Tanel Jalakas ◽  
Taavi Moller ◽  
Kristjan Tabri ◽  
...  
Keyword(s):  
Autonomous Surface Vehicle ◽  
Design And Testing

scholarly journals Autonomous Marine Robot Based on AI Recognition for Permanent Surveillance in Marine Protected Areas

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2664
Author(s):  
J. Carlos Molina-Molina ◽  
Marouane Salhaoui ◽  
Antonio Guerrero-González ◽  
Mounir Arioua
Keyword(s):  
Protected Areas ◽  
Marine Protected Areas ◽  
Recognition Accuracy ◽  
Marine Ecosystem ◽  
Marine Species ◽  
Base Station ◽  
Autonomous Surface Vehicle ◽  
Current Scenario ◽  
Illegal Activities ◽  
Marine Areas

The world’s oceans are one of the most valuable sources of biodiversity and resources on the planet, although there are areas where the marine ecosystem is threatened by human activities. Marine protected areas (MPAs) are distinctive spaces protected by law due to their unique characteristics, such as being the habitat of endangered marine species. Even with this protection, there are still illegal activities such as poaching or anchoring that threaten the survival of different marine species. In this context, we propose an autonomous surface vehicle (ASV) model system for the surveillance of marine areas by detecting and recognizing vessels through artificial intelligence (AI)-based image recognition services, in search of those carrying out illegal activities. Cloud and edge AI computing technologies were used for computer vision. These technologies have proven to be accurate and reliable in detecting shapes and objects for which they have been trained. Azure edge and cloud vision services offer the best option in terms of accuracy for this task. Due to the lack of 4G and 5G coverage in offshore marine environments, it is necessary to use radio links with a coastal base station to ensure communications, which may result in a high response time due to the high latency involved. The analysis of on-board images may not be sufficiently accurate; therefore, we proposed a smart algorithm for autonomy optimization by selecting the proper AI technology according to the current scenario (SAAO) capable of selecting the best AI source for the current scenario in real time, according to the required recognition accuracy or low latency. The SAAO optimizes the execution, efficiency, risk reduction, and results of each stage of the surveillance mission, taking appropriate decisions by selecting either cloud or edge vision models without human intervention.

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