Fuzzy Behavioral Navigation for Bottom Collision Avoidance of Autonomous Underwater Vehicles

2008 ◽  
pp. 122-130 ◽  
Author(s):  
Ningning Zhao ◽  
Demin Xu ◽  
Jian Gao ◽  
Weisheng Yan
Keyword(s):  
Collision Avoidance ◽  
Autonomous Underwater Vehicles ◽  
Underwater Vehicles

Architecture of Software for Biomimetic Autonomous Underwater Vehicle

2016 ◽  
Vol 817 ◽  
pp. 104-110
Author(s):  
Tomasz Praczyk
Keyword(s):  
Research And Development ◽  
Collision Avoidance ◽  
Autonomous Underwater Vehicle ◽  
Autonomous Underwater Vehicles ◽  
Underwater Vehicle ◽  
Underwater Vehicles ◽  
Main Task ◽  
Specialized Software ◽  
Scientific Project

Autonomous underwater vehicles are vehicles that are entirely or partly independent of human decisions. In order to obtain operational independence, the vehicles have to be equipped with a specialized software. The main task of the software is to move the vehicle along a trajectory with collision avoidance. Moreover, the software has also to manage different devices installed on the vehicle board, e.g. to start and stop cameras, sonars etc. In addition to the software embedded on the vehicle board, the software responsible for managing the vehicle on the operator level is also necessary. Its task is to define mission of the vehicle, to start, stop the mission, to send emergency commands, to monitor vehicle parameters, and to control the vehicle in remotely operated mode.The paper presents architecture of the software designed for biomimetic autonomous underwater vehicle (BAUV) that is being constructed within the framework of the scientific project financed by Polish National Center of Research and Development.

scholarly journals Deep Reinforcement Learning Controller for 3D Path Following and Collision Avoidance by Autonomous Underwater Vehicles

2021 ◽  
Vol 7 ◽  
Author(s):  
Simen Theie Havenstrøm ◽  
Adil Rasheed ◽  
Omer San
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Collision Avoidance ◽  
Autonomous Agents ◽  
Autonomous Vehicle ◽  
Autonomous Underwater Vehicles ◽  
A Priori ◽  
Path Following ◽  
Underwater Vehicles ◽  
Stability Of Dynamical Systems

Control theory provides engineers with a multitude of tools to design controllers that manipulate the closed-loop behavior and stability of dynamical systems. These methods rely heavily on insights into the mathematical model governing the physical system. However, in complex systems, such as autonomous underwater vehicles performing the dual objective of path following and collision avoidance, decision making becomes nontrivial. We propose a solution using state-of-the-art Deep Reinforcement Learning (DRL) techniques to develop autonomous agents capable of achieving this hybrid objective without having a priori knowledge about the goal or the environment. Our results demonstrate the viability of DRL in path following and avoiding collisions towards achieving human-level decision making in autonomous vehicle systems within extreme obstacle configurations.

A method for autonomous collision-free navigation of a quadrotor UAV in unknown tunnel-like environments

Robotica ◽  
2021 ◽  
pp. 1-27
Author(s):  
Taha Elmokadem ◽  
Andrey V. Savkin
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Autonomous Underwater Vehicles ◽  
Three Dimensional ◽  
Underwater Vehicles ◽  
Practical Implementation ◽  
Challenging Problem ◽  
Quadrotor Uav ◽  
Navigation Algorithm ◽  
Aerial Vehicles ◽  
Quadrotor Uavs

Abstract Unmanned aerial vehicles (UAVs) have become essential tools for exploring, mapping and inspection of unknown three-dimensional (3D) tunnel-like environments which is a very challenging problem. A computationally light navigation algorithm is developed in this paper for quadrotor UAVs to autonomously guide the vehicle through such environments. It uses sensors observations to safely guide the UAV along the tunnel axis while avoiding collisions with its walls. The approach is evaluated using several computer simulations with realistic sensing models and practical implementation with a quadrotor UAV. The proposed method is also applicable to other UAV types and autonomous underwater vehicles.

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