Výsledky vyhledávání - "Jiang Mingyang"

Akademický článek

Optimum Design and Research of the Vertical Manipulator Arm Rotation Structure

Autor: Sima Mingyang, Zhang Lin, Wang Baoping, Jiang Mingyang, Liu Hongbin

Publikováno v: Jixie chuandong, Vol 47, Pp 153-159 (2023)

Aiming at the installation of prefabricated wall panels in today's assembly building, a new type of wall panel vertical machine is designed, which realizes the adjustment of the pitch position and position of the end grabbing device through the arm t

Externí odkaz: https://doaj.org/article/aff484d31fbd4ec8bf40f283689cdd1f

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Akademický článek

Dynamic Analysis and Scale Optimization of Prefabricated Building Wall Panel Installation

Autor: Jiang Mingyang, Zhang Lin, Liu Hongbin, Sima Mingyang, Yu Ningtao

Publikováno v: Jixie chuandong, Vol 47, Pp 84-89 (2023)

Taking a new type of prefabricated building wall panel installation machine as the research object, the mechanism configuration and specific implementation are analyzed, the kinematic and dynamic mathematical models of the boom landing mechanism are

Externí odkaz: https://doaj.org/article/0bc9a3b975bd43be9e15f7e423482101

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Akademický článek

Research on Configuration Synthesis of Actuator for Underwater Vehicle Receiving and Placing System

Autor: Jiang Mingyang, Liu Hailong, Guo Chuandong, Chen Depeng, Zhang Lin

Publikováno v: Jixie chuandong, Pp 1-10 (2023)

Aiming at the problems of single structure form and limited transporting range of existing underwater vehicle receiving and placing system, comprehensive research on actuator configuration of the receiving and placing system are carried out with the

Externí odkaz: https://doaj.org/article/af9d2f7629f14961b11e3aa474195f66

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End-to-end Driving in High-Interaction Traffic Scenarios with Reinforcement Learning

Autor: Li, Yueyuan, Jiang, Mingyang, Zhang, Songan, Yuan, Wei, Wang, Chunxiang, Yang, Ming

Dynamic and interactive traffic scenarios pose significant challenges for autonomous driving systems. Reinforcement learning (RL) offers a promising approach by enabling the exploration of driving policies beyond the constraints of pre-collected data

Externí odkaz: http://arxiv.org/abs/2410.02253

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HOPE: A Reinforcement Learning-based Hybrid Policy Path Planner for Diverse Parking Scenarios

Autor: Jiang, Mingyang, Li, Yueyuan, Zhang, Songan, Chen, Siyuan, Wang, Chunxiang, Yang, Ming

Automated parking stands as a highly anticipated application of autonomous driving technology. However, existing path planning methodologies fall short of addressing this need due to their incapability to handle the diverse and complex parking scenar

Externí odkaz: http://arxiv.org/abs/2405.20579

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Tactics2D: A Highly Modular and Extensible Simulator for Driving Decision-making

Autor: Li, Yueyuan, Zhang, Songan, Jiang, Mingyang, Chen, Xingyuan, Qian, Yeqiang, Wang, Chunxiang, Yang, Ming

Simulation is a prospective method for generating diverse and realistic traffic scenarios to aid in the development of driving decision-making systems. However, existing simulators often fall short in diverse scenarios or interactive behavior models

Externí odkaz: http://arxiv.org/abs/2311.11058

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