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《机械工程前沿(英文)》 2022年 第17卷 第3期 doi: 10.1007/s11465-022-0686-2
关键词: autonomous excavation unmanned electric shovel point cloud excavation trajectory planning
Multiobjective trajectory optimization of intelligent electro-hydraulic shovel
《机械工程前沿(英文)》 2022年 第17卷 第4期 doi: 10.1007/s11465-022-0706-2
关键词: trajectory planning electro-hydraulic shovel cubic polynomial S-curve multiobjective optimization entropy weight technique
M. NAZEMIZADEH, H. N. RAHIMI, K. AMINI KHOIY
《机械工程前沿(英文)》 2012年 第7卷 第1期 页码 23-28 doi: 10.1007/s11465-012-0304-9
This paper presents an optimal control strategy for optimal trajectory planning of mobile robots by considering nonlinear dynamic model and nonholonomic constraints of the system. The nonholonomic constraints of the system are introduced by a nonintegrable set of differential equations which represent kinematic restriction on the motion. The Lagrange’s principle is employed to derive the nonlinear equations of the system. Then, the optimal path planning of the mobile robot is formulated as an optimal control problem. To set up the problem, the nonlinear equations of the system are assumed as constraints, and a minimum energy objective function is defined. To solve the problem, an indirect solution of the optimal control method is employed, and conditions of the optimality derived as a set of coupled nonlinear differential equations. The optimality equations are solved numerically, and various simulations are performed for a nonholonomic mobile robot to illustrate effectiveness of the proposed method.
关键词: mobile robot trajectory planning nonlinear dynamic optimal control
基于逆强化学习理论的自适应行车场景的拟人化避障轨迹规划研究 Article
武健, 闫扬, 刘玉龙, 刘亚辉
《工程(英文)》 2024年 第33卷 第2期 页码 133-145 doi: 10.1016/j.eng.2023.07.018
The forward design of trajectory planning strategies requires preset trajectory optimization functions, resulting in poor adaptability of the strategy and an inability to accurately generate obstacle avoidance trajectories that conform to real driver behavior habits. In addition, owing to the strong time-varying dynamic characteristics of obstacle avoidance scenarios, it is necessary to design numerous trajectory optimization functions and adjust the corresponding parameters. Therefore, an anthropomorphic obstacle-avoidance trajectory planning strategy for adaptive driving scenarios is proposed. First, numerous expert-demonstrated trajectories are extracted from the HighD natural driving dataset. Subsequently, a trajectory expectation feature-matching algorithm is proposed that uses maximum entropy inverse reinforcement learning theory to learn the extracted expert-demonstrated trajectories and achieve automatic acquisition of the optimization function of the expert-demonstrated trajectory. Furthermore, a mapping model is constructed by combining the key driving scenario information that affects vehicle obstacle avoidance with the weight of the optimization function, and an anthropomorphic obstacle avoidance trajectory planning strategy for adaptive driving scenarios is proposed. Finally, the proposed strategy is verified based on real driving scenarios. The results show that the strategy can adjust the weight distribution of the trajectory optimization function in real time according to the “emergency degree” of obstacle avoidance and the state of the vehicle. Moreover, this strategy can generate anthropomorphic trajectories that are similar to expert-demonstrated trajectories, effectively improving the adaptability and acceptability of trajectories in driving scenarios.
关键词: Obstacle avoidance trajectory planning Inverse reinforcement theory Anthropomorphic Adaptive driving scenarios
Energy-efficient trajectory planning for a multi-UAV-assisted mobile edge computing system
Pei-qiu Huang, Yong Wang, Ke-zhi Wang,pqhuang@csu.edu.cn,ywang@csu.edu.cn,kezhi.wang@northumbria.ac.uk
《信息与电子工程前沿(英文)》 2020年 第21卷 第12期 页码 1671-1814 doi: 10.1631/FITEE.2000315
通用最优轨迹规划——基于最小作用量原理实现自动驾驶 Article
黄荷叶, 刘艺璁, 刘金鑫, 杨奇松, 王建强, David Abbink, Arkady Zgonnikov
《工程(英文)》 2024年 第33卷 第2期 页码 63-76 doi: 10.1016/j.eng.2023.10.001
本研究提出了一种通用的智能车辆最优轨迹规划(GOTP)框架,能够有效地避开障碍物,引导智能车辆安全高效地完成驾驶任务。首先使用五阶贝塞尔曲线生成并平滑沿道路中心线的参考路径。为了使生成的曲线的曲率尽可能连续,笛卡尔坐标被变换。在曲线坐标系中,考虑道路约束、车辆运动学约束,通过采样生成有限的多项式候选轨迹集合。并在选择最优轨迹时,模拟驾驶人驾驶行为,总结驾驶人”趋利避害”操纵特性,提出了基于最小作用量原理的统一自适应目标函数。最后,基于滚动时域优化的思想,输出最优轨迹规划框架,能够协同规划过程动态多性能目标,并选择满足完备性、最优性和智能化的轨迹。大量的仿真和实验结果证明了该框架的可行性和有效性,能有效避开动态和静态障碍物,适用于多源交互交通参与者的各种场景。同时,与驾驶人操纵轨迹对比,所提出的框架能够满足实时安全规划需求。
基于候选曲线的公路轨迹规划中的智能计算量分配 Article
Xiao-xin FU,Yong-heng JIANG,De-xian HUANG,Jing-chun WANG,Kai-sheng HUANG
《信息与电子工程前沿(英文)》 2016年 第17卷 第6期 页码 553-565 doi: 10.1631/FITEE.1500269
Zongwu XIE1 , Xiaoyu ZHAO1 , Zainan JIANG1 , Haitao YANG2 , Chongyang LI1
《机械工程前沿(英文)》 2022年 第17卷 第1期 doi: 10.1007/s11465-021-0658-y
关键词: free-floating space robot dual arm coordinated operation base attitude restoration bidirectional approach
Dynamic prediction of moving trajectory in pipe jacking: GRU-based deep learning framework
《结构与土木工程前沿(英文)》 2023年 第17卷 第7期 页码 994-1010 doi: 10.1007/s11709-023-0942-5
关键词: dynamic prediction moving trajectory pipe jacking GRU deep learning
不确定路面附着系数条件下一种基于双层非线性模型预测控制的自动驾驶卡车轨迹规划方法 Research Articles
王鸿超1,张伟伟1,吴训成1,曹昊天2,高巧明3,罗素云1
《信息与电子工程前沿(英文)》 2020年 第21卷 第7期 页码 963-1118 doi: 10.1631/FITEE.1900185
Robust train speed trajectory optimization: A stochastic constrained shortest path approach
Li WANG, Lixing YANG, Ziyou GAO, Yeran HUANG
《工程管理前沿(英文)》 2017年 第4卷 第4期 页码 408-417 doi: 10.15302/J-FEM-2017042
关键词: train speed trajectory optimization railway operation stochastic programming
Longitudinal and lateral slip control of autonomous wheeled mobile robot for trajectory tracking
Hamza KHAN,Jamshed IQBAL,Khelifa BAIZID,Teresa ZIELINSKA
《信息与电子工程前沿(英文)》 2015年 第16卷 第2期 页码 166-172 doi: 10.1631/FITEE.1400183
关键词: Robot modeling Robot navigation Slip and skid control Wheeled mobile robots
Ahmad MOZAFFARI,Mahyar VAJEDI,Nasser L. AZAD
《机械工程前沿(英文)》 2015年 第10卷 第2期 页码 154-167 doi: 10.1007/s11465-015-0336-z
The main proposition of the current investigation is to develop a computational intelligence-based framework which can be used for the real-time estimation of optimum battery state-of-charge (SOC) trajectory in plug-in hybrid electric vehicles (PHEVs). The estimated SOC trajectory can be then employed for an intelligent power management to significantly improve the fuel economy of the vehicle. The devised intelligent SOC trajectory builder takes advantage of the upcoming route information preview to achieve the lowest possible total cost of electricity and fossil fuel. To reduce the complexity of real-time optimization, the authors propose an immune system-based clustering approach which allows categorizing the route information into a predefined number of segments. The intelligent real-time optimizer is also inspired on the basis of interactions in biological immune systems, and is called artificial immune algorithm (AIA). The objective function of the optimizer is derived from a computationally efficient artificial neural network (ANN) which is trained by a database obtained from a high-fidelity model of the vehicle built in the Autonomie software. The simulation results demonstrate that the integration of immune inspired clustering tool, AIA and ANN, will result in a powerful framework which can generate a near global optimum SOC trajectory for the baseline vehicle, that is, the Toyota Prius PHEV. The outcomes of the current investigation prove that by taking advantage of intelligent approaches, it is possible to design a computationally efficient and powerful SOC trajectory builder for the intelligent power management of PHEVs.
关键词: trip information preview intelligent transportation state-of-charge trajectory builder immune systems artificial neural network
Family planning technical services in China
Shang-Chun WU
《医学前沿(英文)》 2010年 第4卷 第3期 页码 285-289 doi: 10.1007/s11684-010-0097-3
陈杨,刘大学,贺汉根,戴斌
《中国工程科学》 2007年 第9卷 第11期 页码 68-73
轨迹跟踪是移动机器人导航中的核心问题之一。针对非完整运动约束车辆,利用反馈线性化方法设计了轨迹跟踪器,仿真研究了跟踪算法的鲁棒性。最后,介绍了工程实现中参数观测器设计等相关问题。
标题 作者 时间 类型 操作
autonomous mining: design and development of an unmanned electric shovel via point cloud-based optimal trajectoryplanning
期刊论文
Trajectory planning of mobile robots using indirect solution of optimal control method in generalized
M. NAZEMIZADEH, H. N. RAHIMI, K. AMINI KHOIY
期刊论文
Energy-efficient trajectory planning for a multi-UAV-assisted mobile edge computing system
Pei-qiu Huang, Yong Wang, Ke-zhi Wang,pqhuang@csu.edu.cn,ywang@csu.edu.cn,kezhi.wang@northumbria.ac.uk
期刊论文
基于候选曲线的公路轨迹规划中的智能计算量分配
Xiao-xin FU,Yong-heng JIANG,De-xian HUANG,Jing-chun WANG,Kai-sheng HUANG
期刊论文
Trajectory planning and base attitude restoration of dual-arm free-floating space robot by enhanced bidirectional
Zongwu XIE1 , Xiaoyu ZHAO1 , Zainan JIANG1 , Haitao YANG2 , Chongyang LI1
期刊论文
Robust train speed trajectory optimization: A stochastic constrained shortest path approach
Li WANG, Lixing YANG, Ziyou GAO, Yeran HUANG
期刊论文
Longitudinal and lateral slip control of autonomous wheeled mobile robot for trajectory tracking
Hamza KHAN,Jamshed IQBAL,Khelifa BAIZID,Teresa ZIELINSKA
期刊论文
Real-time immune-inspired optimum state-of-charge trajectory estimation using upcoming route information
Ahmad MOZAFFARI,Mahyar VAJEDI,Nasser L. AZAD
期刊论文