scholarly journals An Adaptive Prediction Target Search Algorithm for Multi-AUVs in an Unknown 3D Environment

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3853 ◽  
Author(s):  
Juan Li ◽  
Jianxin Zhang ◽  
Gengshi Zhang ◽  
Bingjian Zhang
Keyword(s):  
High Efficiency ◽  
Search Algorithm ◽  
Autonomous Underwater Vehicles ◽  
Environmental Information ◽  
Field Of View ◽  
Current Layer ◽  
Evaluation Function ◽  
Target Search ◽  
Search Tasks ◽  
Adaptive Prediction

For a target search of autonomous underwater vehicles (AUVs) in a completely unknown three-dimensional (3D) underwater environment, a multi-AUV collaborative target search algorithm based on adaptive prediction is proposed in this paper. The environmental information sensed by the forward-looking sonar is used to judge the current state of view, and the AUV system uses this environmental information to perform the target search task. If there is no target in the field of view, the AUV system will judge whether all sub-regions of the current layer have been searched or not. The next sub-region for searching is determined by the evaluation function and the task assignment strategy. If there are targets in the field of view, the evaluation function and the estimation function of the adaptive predictive optimization algorithm is used to estimate the location of the unknown target. At the same time, the algorithm also can reduce the positioning error caused by the noise of the sonar sensor. In this paper, the simulation results show that the proposed algorithm can not only deal with static targets and random dynamic interference target search tasks, but it can also perform target search tasks under some random AUV failure conditions. In this process, the underwater communication limits are also considered. Finally, simulation experiments indicate the high efficiency and great adaptability of the proposed algorithm.

Multi-AUV Cooperative Target Search Algorithm in 3-D Underwater Workspace

2017 ◽  
Vol 70 (6) ◽  
pp. 1293-1311 ◽  
Author(s):  
Xiang Cao ◽  
A-long Yu
Keyword(s):  
Neural Network ◽  
Search Task ◽  
Search Algorithm ◽  
Autonomous Underwater Vehicles ◽  
Three Dimensional ◽  
Target Search ◽  
Team Members ◽  
Neural Network Algorithm ◽  
Som Neural Network ◽  
Self Organising Map

To improve the efficiency of multiple Autonomous Underwater Vehicles (multi-AUV) cooperative target search in a Three-Dimensional (3D) underwater workspace, an integrated algorithm is proposed by combining a Self-Organising Map (SOM), neural network and Glasius Bioinspired Neural Network (GBNN). With this integrated algorithm, the 3D underwater workspace is first divided into subspaces dependent on the abilities of the AUV team members. After that, tasks are allocated to each subspace for an AUV by SOM. Finally, AUVs move to the assigned subspace in the shortest way and start their search task by GBNN. This integrated algorithm, by avoiding overlapping search paths and raising the coverage rate, can reduce energy consumption of the whole multi-AUV system. The simulation results show that the proposed algorithm is capable of guiding multi-AUV to achieve a multiple target search task with higher efficiency and adaptability compared with a more traditional bioinspired neural network algorithm.

scholarly journals Autonomous Underwater Vehicles and Field of View in Underwater Operations

2021 ◽  
Vol 9 (3) ◽  
pp. 277
Author(s):  
Isaac Segovia Ramírez ◽  
Pedro José Bernalte Sánchez ◽  
Mayorkinos Papaelias ◽  
Fausto Pedro García Márquez
Keyword(s):  
Autonomous Underwater Vehicles ◽  
Underwater Vehicles ◽  
Field Of View ◽  
Guidance System ◽  
Control Algorithms ◽  
Deep Waters ◽  
Underwater Cameras ◽  
Optimal Resolution ◽  
Acquisition Processes ◽  
And Control

Submarine inspections and surveys require underwater vehicles to operate in deep waters efficiently, safely and reliably. Autonomous Underwater Vehicles employing advanced navigation and control systems present several advantages. Robust control algorithms and novel improvements in positioning and navigation are needed to optimize underwater operations. This paper proposes a new general formulation of this problem together with a basic approach for the management of deep underwater operations. This approach considers the field of view and the operational requirements as a fundamental input in the development of the trajectory in the autonomous guidance system. The constraints and involved variables are also defined, providing more accurate modelling compared with traditional formulations of the positioning system. Different case studies are presented based on commercial underwater cameras/sonars, analysing the influence of the main variables in the measurement process to obtain optimal resolution results. The application of this approach in autonomous underwater operations ensures suitable data acquisition processes according to the payload installed onboard.

scholarly journals A Probabilistic Target Search Algorithm Based on Hierarchical Collaboration for Improving Rapidity of Drones

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2535 ◽  
Author(s):  
Il-Kyu Ha ◽  
You-Ze Cho
Keyword(s):  
High Altitude ◽  
Target Detection ◽  
Information Transfer ◽  
Search Algorithm ◽  
Search Time ◽  
Previous Method ◽  
Target Search ◽  
Low Altitude ◽  
Military Facilities ◽  
Different Altitudes

Finding a target quickly is one of the most important tasks in drone operations. In particular, rapid target detection is a critical issue for tasks such as finding rescue victims during the golden period, environmental monitoring, locating military facilities, and monitoring natural disasters. Therefore, in this study, an improved hierarchical probabilistic target search algorithm based on the collaboration of drones at different altitudes is proposed. This is a method for reducing the search time and search distance by improving the information transfer methods between high-altitude and low-altitude drones. Specifically, to improve the speed of target detection, a high-altitude drone first performs a search of a wide area. Then, when the probability of existence of the target is higher than a certain threshold, the search information is transmitted to a low-altitude drone which then performs a more detailed search in the identified area. This method takes full advantage of fast searching capabilities at high altitudes. In other words, it reduces the total time and travel distance required for searching by quickly searching a wide search area. Several drone collaboration scenarios that can be performed by two drones at different altitudes are described and compared to the proposed algorithm. Through simulations, the performances of the proposed algorithm and the cooperation scenarios are analyzed. It is demonstrated that methods utilizing hierarchical searches with drones are comparatively excellent and that the proposed algorithm is approximately 13% more effective than a previous method and much better compared to other scenarios.

scholarly journals Directional-Sensor Network Deployment Planning for Mobile-Target Search

Robotics ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 82
Author(s):  
Shiraz Wasim ◽  
Zendai Kashino ◽  
Goldie Nejat ◽  
Beno Benhabib
Keyword(s):  
Sensor Network ◽  
Search Algorithm ◽  
Search Problem ◽  
Target Search ◽  
Physical Constraints ◽  
Sensing Model ◽  
Mobile Target ◽  
Deployment Planning ◽  
Directional Sensor Network ◽  
Directional Sensor

In this paper, a novel time-phased directional-sensor network deployment strategy is presented for the mobile-target search problem, e.g., wilderness search and rescue (WiSAR). The proposed strategy uses probabilistic target-motion models combined with a variation of a standard direct search algorithm to plan the optimal locations of directional-sensors which maximize the likelihood of target detection. A linear sensing model is employed as a simplification for directional-sensor network deployment planning, while considering physical constraints, such as on-time sensor deliverability. Extensive statistical simulations validated our method. One such illustrative experiment is included herein to demonstrate the method’s operation. A comparative study was also carried out, whose summary is included in this paper, to highlight the tangible improvement of our approach versus three traditional deployment strategies: a uniform, a random, and a ring-of-fire type deployment, respectively.

Object tracking with robotic total stations: Current technologies and improvements based on image data

2017 ◽  
Vol 11 (3) ◽  
Author(s):  
Matthias Ehrhart ◽  
Werner Lienhart
Keyword(s):  
Object Tracking ◽  
State Of The Art ◽  
Image Data ◽  
Field Of View ◽  
Experimental Measurements ◽  
Wide Angle ◽  
Target Search ◽  
Total Station ◽  
Machine Control ◽  
Wide Angle Camera

AbstractThe importance of automated prism tracking is increasingly triggered by the rising automation of total station measurements in machine control, monitoring and one-person operation. In this article we summarize and explain the different techniques that are used to coarsely search a prism, to precisely aim at a prism, and to identify whether the correct prism is tracked. Along with the state-of-the-art review, we discuss and experimentally evaluate possible improvements based on the image data of an additional wide-angle camera which is available for many total stations today. In cases in which the total station’s fine aiming module loses the prism, the tracked object may still be visible to the wide-angle camera because of its larger field of view. The theodolite angles towards the target can then be derived from its image coordinates which facilitates a fast reacquisition of the prism. In experimental measurements we demonstrate that our image-based approach for the coarse target search is 4 to 10-times faster than conventional approaches.

scholarly journals Feature Selection in Decision Systems: A Mean-Variance Approach

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Chengdong Yang ◽  
Wenyin Zhang ◽  
Jilin Zou ◽  
Shunbo Hu ◽  
Jianlong Qiu
Keyword(s):  
Feature Selection ◽  
Data Clustering ◽  
Search Algorithm ◽  
Evaluation Function ◽  
Uncertainty Measure ◽  
Greedy Search ◽  
Decision Systems ◽  
Mean Variance ◽  
Variance Approach ◽  
Experiment Analysis

Uncertainty measure is an important implement for characterizing the degree of uncertainty. It has been extensively applied in pattern recognition and data clustering. Because of instability of traditional uncertainty measures, mean-variance measure (MVM) is utilized to perform feature selection, which could depress disturbances and noises effectively. Thereby, a novel evaluation function based on MVM is designed. The forward greedy search algorithm (FGSA) with the proposed evaluation function is exploited to perform feature selection. Experiment analysis shows the validity and effectiveness of MVM.

scholarly journals Accelerated off-target search algorithm for siRNA

2004 ◽  
Vol 21 (8) ◽  
pp. 1316-1324 ◽  
Author(s):  
T. Yamada ◽  
S. Morishita
Keyword(s):  
Search Algorithm ◽  
Target Search
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