scholarly journals Target Search Algorithm for AUV Based on Real-Time Perception Maps in Unknown Environment

Machines ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 147
Author(s):  
Juan Li ◽  
Xiaoliang Zhai ◽  
Jian Xu ◽  
Chengyue Li
Keyword(s):  
Real Time ◽  
Time Perception ◽  
Search Algorithm ◽  
Field Method ◽  
Release Mechanism ◽  
Target Search ◽  
Current Location ◽  
Revenue Function ◽  
Low Coverage ◽  
High Degree

For the problem of AUV target searches in unknown underwater environments, a target search algorithm for AUVs based on a real-time perception map is proposed. Real-time perception maps, including target existence probability maps, uncertainty maps, and pheromone maps and their updating rules, are established. Attraction source maps and search status maps based on the environmental information detected by the AUV are established. The maps are used for the AUV to search for corner areas that are unknown to a high degree and areas with low coverage around the current location. At the same time, a release mechanism for attraction and revisiting pheromones is established by combining a neural excitation network algorithm to make the gradient spread in the pheromone grid map. By setting up a search revenue function based on real-time perception maps, an AUV search decision-making method is established. When the AUV finds a suspected target, the AUV approaches the suspected target. The path planning of the AUV is carried out through an improved artificial potential field method. The short-distance confirmation of the target and obstacle avoidance in the search process are realized. The simulation results show that the algorithm has high search efficiency. Additionally, when the target exists in a corner area, the probability of the AUV to quickly search for the target is fast and feasible.

Smart Tool*/Smart Tool - Intelligent sensor-based tool management

2016 ◽  
Vol 106 (03) ◽  
pp. 106-110
Author(s):  
E. Abele ◽  
T. Grosch ◽  
E. Schaupp
Keyword(s):  
Tool Wear ◽  
Real Time ◽  
Industrie 4.0 ◽  
Tool Management ◽  
Intelligent Sensor ◽  
Individual Level ◽  
Tool Holder ◽  
Industrial Internet ◽  
Current Location

Im Kontext von Industrie 4.0 bietet eine Optimierung des Werkzeugmanagements zahlreiche Potentiale. Durch ein Track & Trace-System, welches in den gesamten Werkzeugkreislauf integriert wird, lässt sich der aktuelle Aufenthaltsort der Werkzeuge auf Individuumsebene in Echtzeit bestimmen. Eine im Werkzeughalter untergebrachte Sensorik liefert zusätzliche Informationen über den aktuellen Zustand der Werkzeuge, beispielsweise den Verschleiß.   In the context of the industrial internet (Industrie 4.0), tool management offers great potential. With a track&trace-system integrated in the whole tool cycle the current location of tools can be determined at individual level in real time. Furthermore, sensors placed in the tool holder provide information about the tool`s current condition, e.g. tool wear.

Context-aware Adaptive Surgery

2021 ◽  
Vol 5 (3) ◽  
pp. 1-22
Author(s):  
Hongli Wang ◽  
Bin Guo ◽  
Jiaqi Liu ◽  
Sicong Liu ◽  
Yungang Wu ◽  
...  
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Resource Constraints ◽  
Search Algorithm ◽  
Search Time ◽  
State Graph ◽  
Context Aware ◽  
Optimal Partition ◽  
Research Attention ◽  
Neighbor Effect

Deep Neural Networks (DNNs) have made massive progress in many fields and deploying DNNs on end devices has become an emerging trend to make intelligence closer to users. However, it is challenging to deploy large-scale and computation-intensive DNNs on resource-constrained end devices due to their small size and lightweight. To this end, model partition, which aims to partition DNNs into multiple parts to realize the collaborative computing of multiple devices, has received extensive research attention. To find the optimal partition, most existing approaches need to run from scratch under given resource constraints. However, they ignore that resources of devices (e.g., storage, battery power), and performance requirements (e.g., inference latency), are often continuously changing, making the optimal partition solution change constantly during processing. Therefore, it is very important to reduce the tuning latency of model partition to realize the real-time adaption under the changing processing context. To address these problems, we propose the Context-aware Adaptive Surgery (CAS) framework to actively perceive the changing processing context, and adaptively find the appropriate partition solution in real-time. Specifically, we construct the partition state graph to comprehensively model different partition solutions of DNNs by import context resources. Then "the neighbor effect" is proposed, which provides the heuristic rule for the search process. When the processing context changes, CAS adopts the runtime search algorithm, Graph-based Adaptive DNN Surgery (GADS), to quickly find the appropriate partition that satisfies resource constraints under the guidance of the neighbor effect. The experimental results show that CAS realizes adaptively rapid tuning of the model partition solutions in 10ms scale even for large DNNs (2.25x to 221.7x search time improvement than the state-of-the-art researches), and the total inference latency still keeps the same level with baselines.

Bayesian real-time perception algorithms on GPU

2010 ◽  
Vol 6 (3) ◽  
pp. 171-186 ◽  
Author(s):  
João Filipe Ferreira ◽  
Jorge Lobo ◽  
Jorge Dias
Keyword(s):  
Real Time ◽  
Time Perception

scholarly journals Motion Estimation Architecture Using Efficient Adder-Compressors for HDTV Video Coding

2010 ◽  
Vol 5 (1) ◽  
pp. 78-88 ◽  
Author(s):  
Marcelo Porto ◽  
André Silva ◽  
Sergo Almeida ◽  
Eduardo Da Costa ◽  
Sergio Bampi
Keyword(s):  
Motion Estimation ◽  
Real Time ◽  
Search Algorithm ◽  
Absolute Difference ◽  
High Definition ◽  
Case Processing ◽  
Worst Case ◽  
Average Case ◽  
High Definition Television ◽  
Internal Structures

This paper presents real time HDTV (High Definition Television) architecture for Motion Estimation (ME) using efficient adder compressors. The architecture is based on the Quarter Sub-sampled Diamond Search algorithm (QSDS) with Dynamic Iteration Control (DIC) algorithm. The main characteristic of the proposed architecture is the large amount of Processing Units (PUs) that are used to calculate the SAD (Sum of Absolute Difference) metric. The internal structures of the PUs are composed by a large number of addition operations to calculate the SADs. In this paper, efficient 4-2 and 8-2 adder compressors are used in the PUs architecture to achieve the performance to work with HDTV (High Definition Television) videos in real time at 30 frames per second. These adder compressors enable the simultaneous addition of 4 and 8 operands respectively. The PUs, using adder compressors, were applied to the ME architecture. The implemented architecture was described in VHDL and synthesized to FPGA and, with Leonardo Spectrum tool, to the TSMC 0.18μm CMOS standard cell technology. Synthesis results indicate that the new QSDS-DIC architecture reach the best performance result and enable gains of 12% in terms of processing rate. The architecture can reach real time for full HDTV (1920x1080 pixels) in the worst case processing 65 frames per second, and it can process 269 HDTV frames per second in the average case.

scholarly journals Hybrid Multilevel Thresholding and Improved Harmony Search Algorithm for Segmentation

2018 ◽  
Vol 8 (6) ◽  
pp. 4593 ◽  
Author(s):  
Erwin Erwin ◽  
Saparudin Saparudin ◽  
Wulandari Saputri
Keyword(s):  
High Performance ◽  
Search Algorithm ◽  
Signal To Noise Ratio ◽  
Harmony Search ◽  
Optimal Solution ◽  
Harmony Search Algorithm ◽  
Multilevel Thresholding ◽  
Improved Harmony Search ◽  
High Degree ◽  
Improved Harmony Search Algorithm

This paper proposes a new method for image segmentation is hybrid multilevel thresholding and improved harmony search algorithm. Improved harmony search algorithm which is a method for finding vector solutions by increasing its accuracy. The proposed method looks for a random candidate solution, then its quality is evaluated through the Otsu objective function. Furthermore, the operator continues to evolve the solution candidate circuit until the optimal solution is found. The dataset used in this study is the retina dataset, tongue, lenna, baboon, and cameraman. The experimental results show that this method produces the high performance as seen from peak signal-to-noise ratio analysis (PNSR). The PNSR result for retinal image averaged 40.342 dB while for the average tongue image 35.340 dB. For lenna, baboon and cameramen produce an average of 33.781 dB, 33.499 dB, and 34.869 dB. Furthermore, the process of object recognition and identification is expected to use this method to produce a high degree of accuracy.

Continuous dynamics in the real-time perception of race

2010 ◽  
Vol 46 (1) ◽  
pp. 179-185 ◽  
Author(s):  
Jonathan B. Freeman ◽  
Kristin Pauker ◽  
Evan P. Apfelbaum ◽  
Nalini Ambady
Keyword(s):  
Real Time ◽  
Time Perception ◽  
The Real

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.

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