An unscented Kalman filter based navigation algorithm for autonomous underwater vehicles

Mechatronics ◽  
2016 ◽  
Vol 39 ◽  
pp. 185-195 ◽  
Author(s):  
B. Allotta ◽  
A. Caiti ◽  
L. Chisci ◽  
R. Costanzi ◽  
F. Di Corato ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Autonomous Underwater Vehicles ◽  
Underwater Vehicles ◽  
Navigation Algorithm

An Innovative Navigation Strategy for Autonomous Underwater Vehicles: An Unscented Kalman Filter Based Approach

2015 ◽  
Author(s):  
Benedetto Allotta ◽  
Riccardo Costanzi ◽  
Enrico Meli ◽  
Alessandro Ridolfi ◽  
Luigi Chisci ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Autonomous Underwater Vehicles ◽  
Underwater Vehicles ◽  
Measurement Unit ◽  
Doppler Velocity ◽  
Underwater Robotics ◽  
Filtering Algorithm ◽  
Navigation Algorithms ◽  
Underwater Navigation

Developing reliable navigation strategies is mandatory in the field of Underwater Robotics and in particular for Autonomous Underwater Vehicles (AUVs) to ensure the correct achievement of a mission. Underwater navigation is still nowadays critical, e.g. due to lack of access to satellite navigation systems (e.g. the Global Positioning System, GPS): an AUV typically proceeds for long time intervals only relying on the measurements of its on-board sensors, without any communication with the outside environment. In this context, the filtering algorithm for the estimation of the AUV state is a key factor for the performance of the system; i.e. the filtering algorithm used to estimate the state of the AUV has to guarantee a satisfactory underwater navigation accuracy. In this paper, the authors present an underwater navigation system which exploits measurements from an Inertial Measurement Unit (IMU), Doppler Velocity Log (DVL) and a Pressure Sensor (PS) for the depth, and relies on either an Extended Kalman Filter (EKF) or an Unscented Kalman Filter (UKF) for state estimation. A comparison between the EKF approach, classically adopted in the field of underwater robotics and the UKF is given. These navigation algorithms have been experimentally validated through the data related to some sea tests with the Typhoon class AUVs, designed and assembled by the Department of Industrial Engineering of the Florence University (DIEF) for exploration and surveillance of underwater archaeological sites in the framework of the THESAURUS and European ARROWS projects. The comparison results are significant as the two filtering strategies are based on the same process and sensors models. At this initial stage of the research activity, the navigation algorithms have been tested offline. The presented results rely on the experimental navigation data acquired during two different sea missions: in the first one, Typhoon AUV #1 navigated in a Remotely Operated Vehicle (ROV) mode near Livorno, Italy, during the final demo of THESAURUS project (held in August 2013); in the latter Typhoon AUV #2 autonomously navigated near La Spezia in the framework of the NATO CommsNet13 experiment, Italy (held in September 2013). The achieved results demonstrate the effectiveness of both navigation algorithms and the superiority of the UKF without increasing the computational load. The algorithms are both affordable for online on-board AUV implementation and new tests at sea are planned for spring 2015.

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.

scholarly journals Publish and Subscribe-Based Formation and Containment Control of Heterogeneous Robotic System with Actuator Time Delay

2021 ◽  
Vol 11 (19) ◽  
pp. 9145
Author(s):  
Siddig M. Elkhider ◽  
Omar Al-Buraiki ◽  
Sami El-Ferik
Keyword(s):  
Time Delay ◽  
Obstacle Avoidance ◽  
Data Transmission ◽  
Data Exchange ◽  
Autonomous Underwater Vehicles ◽  
Underwater Vehicles ◽  
Containment Control ◽  
L1 Adaptive Control ◽  
Navigation Algorithm ◽  
Local Controller

This paper addresses the problem of controlling a heterogeneous system composed of multiple Unmanned Aerial Vehicles (UAVs) and Autonomous Underwater Vehicles (AUVs) for formation and containment maintenance. The proposed approach considers actuator time delay and, in addition to formation and containment, considers obstacle avoidance, and offers a robust navigation algorithm and uses a reliable middleware for data transmission and exchange. The methodology followed uses both flocking technique and modified L1 adaptive control to ensure the proper navigation and coordination while avoiding obstacles. The data exchange between all the agents is provided through the data distribution services (DDS) middleware, which solves the interoperability issue when dealing with heterogeneous multiagent systems. The modified L1 controller is a local controller for stabilizing the dynamic model of each UAV and AUV, and the flocking approach is used to coordinate the followers around the leader or within the space delimited by their leaders. Potential Field (PF) allows obstacle avoidance during the agents’ movement. The performance of the proposed approach under the considerations mentioned above are verified and demonstrated using simulations.

Safe Navigation Algorithm for Autonomous Underwater Vehicles

2021 ◽  
Vol 29 (1) ◽  
pp. 97-110
Author(s):  
V.S. Bykova ◽  
◽  
A.I. Mashoshin ◽  
I.V. Pashkevich ◽  
◽  
...  
Keyword(s):  
Control System ◽  
Autonomous Underwater Vehicle ◽  
Autonomous Underwater Vehicles ◽  
Underwater Vehicle ◽  
Underwater Vehicles ◽  
Limited Size ◽  
Safe Navigation ◽  
Navigation Algorithm ◽  
Navigation Algorithms ◽  
Ice Edge

Two safe navigation algorithms for autonomous underwater vehicles are described: algorithm for avoidance of point obstacles including all the moving underwater and surface objects, and limited size bottom objects, and algorithm for bypassing extended obstacles such as bottom elevations, rough lower ice edge, garbage patches. These algorithms are developed for a control system of a heavyweight autonomous underwater vehicle.

Application of Improved Unscented Kalman Filter to AUV Based on Terrain-Aided Strapdown Inertial Navigation System

2013 ◽  
Vol 389 ◽  
pp. 758-764 ◽  
Author(s):  
Qi Wang ◽  
Dong Li ◽  
Zi Jia Zhang ◽  
Chang Song Yang
Keyword(s):  
Kalman Filter ◽  
Information Fusion ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Unscented Kalman Filter ◽  
Autonomous Underwater Vehicles ◽  
Inertial Navigation ◽  
Strapdown Inertial Navigation System ◽  
Integrated Navigation ◽  
Strapdown Inertial Navigation

To improve the navigation precision of autonomous underwater vehicles, a terrain-aided strapdown inertial navigation based on Improved Unscented Kalman Filter (IUKF) is proposed in this paper. The characteristics of strapdown inertial navigation system and terrain-aided navigation system are described in this paper, and improved UKF method is applied to the information fusion. Simulation experiments of novel integrated navigation system proposed in the paper were carried out comparing to the traditional Kalman filtering methods. The experiment results suggest that the IUKF method is able to greatly improve the long-time navigation precision, relative to the traditional information fusion method.

scholarly journals Using augmented state Kalman filter to localize multi autonomous underwater vehicles

2007 ◽  
Vol 13 (2) ◽  
pp. 61-70
Author(s):  
Silvia Botelho ◽  
Renato Neves ◽  
Lorenzo Taddei ◽  
Vinícius Oliveira
Keyword(s):  
Kalman Filter ◽  
Autonomous Underwater Vehicles ◽  
Underwater Vehicles ◽  
Augmented State

scholarly journals Tracking Control of Autonomous Underwater Vehicles with Acoustic Localization and Extended Kalman Filter

2021 ◽  
Vol 11 (17) ◽  
pp. 8038
Author(s):  
Dongzhou Zhan ◽  
Huarong Zheng ◽  
Wen Xu
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Tracking Control ◽  
Autonomous Underwater Vehicles ◽  
Acoustic Signals ◽  
Underwater Vehicles ◽  
Ocean Currents ◽  
Acoustic Localization ◽  
And Control ◽  
Constraints Model

The absence of global positioning system (GPS) signals and the influence of ocean currents are two of the main challenges facing the autonomy of autonomous underwater vehicles (AUVs). This paper proposes an acoustic localization-based tracking control method for AUVs. Particularly, three buoys that emit acoustic signals periodically are deployed over the surface. Times of arrivals of these acoustic signals at the AUV are then obtained and used to calculate an estimated position of the AUV. Moreover, the uncertainties involved in the localization and ocean currents are handled together in the framework of the extended Kalman filter. To deal with system physical constraints, model predictive control relying on online repetitive optimizations is applied in the tracking controller design. Furthermore, due to the different sampling times between localization and control, the dead-reckoning technique is utilized considering detailed AUV dynamics. To avoid using the highly nonlinear and complicated AUV dynamics in the online optimizations, successive linearizations are employed to achieve a trade-off between computational complexity and control performance. Simulation results show that the proposed algorithms are effective and can achieve the AUV tracking control goals.

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