An Intelligent Algorithm for AGV Scheduling in Intelligent Warehouses

2021 ◽  
pp. 163-173
Author(s):  
Xue Wu ◽  
Min-Xia Zhang ◽  
Yu-Jun Zheng
Keyword(s):  
Intelligent Algorithm

Research on intelligent clustering of male upper body

2021 ◽  
pp. 004051752110001
Author(s):  
Pengpeng Cheng ◽  
Xianyi Zeng ◽  
Pascal Bruniaux ◽  
Jianping Wang ◽  
Daoling Chen
Keyword(s):  
Artificial Bee Colony ◽  
Young Men ◽  
Cuckoo Search ◽  
The Body ◽  
Upper Body ◽  
Intelligent Algorithm ◽  
Density Peak ◽  
Swarm Optimization ◽  
Bee Colony ◽  
Length Width

To study the upper body characteristics of young men, the body circumference, length, width, thickness, and angle of young men aged 18–25 and 26–35 years were collected to comprehensively characterize the concave and convex features of the front, back, and side of the human body. The Cuckoo Search-Density Peak intelligent algorithm was used to extract the feature factors of the upper body of men, and to cluster them. To verify the effectiveness of the intelligent algorithm, the clustering results of Cuckoo Search-Density Peak, Density Peak, Particle Swarm Optimization-Density Peak algorithm, Ant Colony Optimization-Density Peak algorithm, Genetic Algorithm-Density Peak algorithm, and Artificial Bee Colony-Density Peak algorithm were evaluated by Silouette and F-measures, respectively. The results show that the Cuckoo Search-Density Peak algorithm has the best clustering results and is superior to other algorithms. There are some differences in somatotype characteristics and somatotype indexes between young men aged 18–25 and 26–35 years.

Intelligent optimization of EV comfort based on a cooperative braking system

2021 ◽  
pp. 095440702110044
Author(s):  
Lingying Zhao ◽  
Min Ye ◽  
Xinxin Xu
Keyword(s):  
System Model ◽  
Regenerative Braking ◽  
Coupling Mechanism ◽  
Intelligent Algorithm ◽  
Distribution Strategy ◽  
Braking System ◽  
Logic Diagram ◽  
Braking Torque ◽  
Braking Force ◽  
The Time Domain

To address the comfort of an electric vehicle, a coupling mechanism between mechanical friction braking and electric regenerative braking was studied. A cooperative braking system model was established, and comprehensive simulations and system optimizations were carried out. The performance of the cooperative braking system was analyzed. The distribution of the braking force was optimized by an intelligent method, and the distribution of a braking force logic diagram based on comfort was proposed. Using an intelligent algorithm, the braking force was distributed between the two braking systems and between the driving and driven axles. The experiment based on comfort was carried out. The results show that comfort after optimization is improved by 76.29% compared with that before optimization by comparing RMS value in the time domain. The reason is that the braking force distribution strategy based on the optimization takes into account the driver’s braking demand, the maximum braking torque of the motor, and the requirements of vehicle comfort, and makes full use of the braking torque of the motor. The error between simulation results and experimental results is 5.13%, which indicates that the braking force’s distribution strategy is feasible.

An intelligent algorithm for autonomous scientific sampling with the VALKYRIE cryobot

2017 ◽  
Vol 17 (3) ◽  
pp. 247-257 ◽  
Author(s):  
Evan B. Clark ◽  
Nathan E. Bramall ◽  
Brent Christner ◽  
Chris Flesher ◽  
John Harman ◽  
...  
Keyword(s):  
Human Performance ◽  
Autonomous Underwater Vehicles ◽  
Field Performance ◽  
Planetary Science ◽  
Operating Environment ◽  
Intelligent Algorithm ◽  
Future State ◽  
Science Operations ◽  
Wide Range ◽  
Matanuska Glacier

AbstractThe development of algorithms for agile science and autonomous exploration has been pursued in contexts ranging from spacecraft to planetary rovers to unmanned aerial vehicles to autonomous underwater vehicles. In situations where time, mission resources and communications are limited and the future state of the operating environment is unknown, the capability of a vehicle to dynamically respond to changing circumstances without human guidance can substantially improve science return. Such capabilities are difficult to achieve in practice, however, because they require intelligent reasoning to utilize limited resources in an inherently uncertain environment. Here we discuss the development, characterization and field performance of two algorithms for autonomously collecting water samples on VALKYRIE (Very deep Autonomous Laser-powered Kilowatt-class Yo-yoing Robotic Ice Explorer), a glacier-penetrating cryobot deployed to the Matanuska Glacier, Alaska (Mission Control location: 61°42′09.3″N 147°37′23.2″W). We show performance on par with human performance across a wide range of mission morphologies using simulated mission data, and demonstrate the effectiveness of the algorithms at autonomously collecting samples with high relative cell concentration during field operation. The development of such algorithms will help enable autonomous science operations in environments where constant real-time human supervision is impractical, such as penetration of ice sheets on Earth and high-priority planetary science targets like Europa.

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