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Research on Anthropomorphic Obstacle Avoidance Trajectory Planning for Adaptive Driving Scenarios Based

Jian Wu,Yang Yan,Yulong Liu,Yahui Liu,

《工程（英文）》 doi: 10.1016/j.eng.2023.07.018

摘要： 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    

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移动机器人障碍躲避的最佳路径

郭戈

《中国工程科学》 2003年 第5卷 第5期   页码 70-75

摘要：

提出一种以障碍物和机器人位置和速度等信息为基础的障碍躲避方法，重点探讨了确定性环境下障碍躲避和转弯过程中机器人应遵循的合理路径问题，并通过证明指出了给定环境条件下实现转弯和障碍躲避的最佳路径。仿真实验表明，该结论简单有效，便于实施，具有较高的应用价值。

关键词： 移动机器人     超声波传感器     障碍躲避     路径规划     最佳路径    

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Obstacle-circumventing adaptive control of a four-wheeled mobile robot subjected to motion uncertainties

《机械工程前沿（英文）》 2023年 第18卷 第3期 doi: 10.1007/s11465-023-0753-3

摘要： To achieve the collision-free trajectory tracking of the four-wheeled mobile robot (FMR), existing methods resolve the tracking control and obstacle avoidance separately. Guaranteeing the synergistic robustness and smooth navigation of mobile robots subjected to motion uncertainties in a dynamic environment using this non-cooperative processing method is difficult. To address this challenge, this paper proposes an obstacle-circumventing adaptive control (OCAC) framework. Specifically, a novel anti-disturbance terminal slide mode control with adaptive gains is formulated, incorporating specified control laws for different stages. This formulation guarantees rapid convergence and simultaneous chattering elimination. By introducing sub-target points, a new sub-target dynamic tracking regression obstacle avoidance strategy is presented to transfer the obstacle avoidance problem into a dynamic tracking one, thereby reducing the burden of local path searching while ensuring system stability during obstacle circumvention. Comparative experiments demonstrate that the proposed OCAC method can strengthen the convergence and obstacle avoidance efficiency of the concerned FMR system.

关键词： four-wheeled mobile robot     obstacle-circumventing adaptive control     adaptive anti-disturbance terminal sliding mode control     sub-target dynamic tracking regression obstacle avoidance    

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autonomous mining: design and development of an unmanned electric shovel via point cloud-based optimal trajectoryplanning

《机械工程前沿（英文）》 2022年 第17卷 第3期 doi: 10.1007/s11465-022-0686-2

摘要： With the proposal of intelligent mines, unmanned mining has become a research hotspot in recent years. In the field of autonomous excavation, environmental perception and excavation trajectory planning are two key issues because they have considerable influences on operation performance. In this study, an unmanned electric shovel (UES) is developed, and key robotization processes consisting of environment modeling and optimal excavation trajectory planning are presented. Initially, the point cloud of the material surface is collected and reconstructed by polynomial response surface (PRS) method. Then, by establishing the dynamical model of the UES, a point to point (PTP) excavation trajectory planning method is developed to improve both the mining efficiency and fill factor and to reduce the energy consumption. Based on optimal trajectory command, the UES performs autonomous excavation. The experimental results show that the proposed surface reconstruction method can accurately represent the material surface. On the basis of reconstructed surface, the PTP trajectory planning method rapidly obtains a reasonable mining trajectory with high fill factor and mining efficiency. Compared with the common excavation trajectory planning approaches, the proposed method tends to be more capable in terms of mining time and energy consumption, ensuring high-performance excavation of the UES in practical mining environment.

关键词： autonomous excavation     unmanned electric shovel     point cloud     excavation trajectory planning    

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General Optimal Trajectory Planning: Enabling Autonomous Vehicles with the Principle of Least Action

Heye Huang,Yicong Liu,Jinxin Liu,Qisong Yang,Jianqiang Wang,David Abbink,Arkady Zgonnikov,

《工程（英文）》 doi: 10.1016/j.eng.2023.10.001

摘要： This study presents a general optimal trajectory planning (GOTP) framework for autonomous vehicles (AVs) that can effectively avoid obstacles and guide AVs to complete driving tasks safely and efficiently. Firstly, we employ the fifth-order Bezier curve to generate and smooth the reference path along the road centerline. Cartesian coordinates are then transformed to achieve the curvature continuity of the generated curve. Considering the road constraints and vehicle dynamics, limited polynomial candidate trajectories are generated and smoothed in a curvilinear coordinate system. Furthermore, in selecting the optimal trajectory, we develop a unified and auto-tune objective function based on the principle of least action by employing AVs to simulate drivers’ behavior and summarizing their manipulation characteristics of “seeking benefits and avoiding losses.” Finally, by integrating the idea of receding-horizon optimization, the proposed framework is achieved by considering dynamic multi-performance objectives and selecting trajectories that satisfy feasibility, optimality, and adaptability. Extensive simulations and experiments are performed, and the results demonstrate the framework’s feasibility and effectiveness, which avoids both dynamic and static obstacles and applies to various scenarios with multi-source interactive traffic participants. Moreover, we prove that the proposed method can guarantee real-time planning and safety requirements compared to drivers’ manipulation.

关键词： Autonomous vehicle     Trajectory planning     Multi-performance objectives     Principle of least action    

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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

摘要： Multiobjective trajectory planning is still face challenges due to certain practical requirements and multiple contradicting objectives optimized simultaneously. In this paper, a multiobjective trajectory optimization approach that sets energy consumption, execution time, and excavation volume as the objective functions is presented for the electro-hydraulic shovel (EHS). The proposed cubic polynomial S-curve is employed to plan the crowd and hoist speed of EHS. Then, a novel hybrid constrained multiobjective evolutionary algorithm based on decomposition is proposed to deal with this constrained multiobjective optimization problem. The normalization of objectives is introduced to minimize the unfavorable effect of orders of magnitude. A novel hybrid constraint handling approach based on -constraint and the adaptive penalty function method is utilized to discover infeasible solution information and improve population diversity. Finally, the entropy weight technique for order preference by similarity to an ideal solution method is used to select the most satisfied solution from the Pareto optimal set. The performance of the proposed strategy is validated and analyzed by a series of simulation and experimental studies. Results show that the proposed approach can provide the high-quality Pareto optimal solutions and outperforms other trajectory optimization schemes investigated in this article.

关键词： trajectory planning     electro-hydraulic shovel     cubic polynomial S-curve     multiobjective optimization     entropy weight technique    

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A new efficient optimal path planner for mobile robot based on Invasive Weed Optimization algorithm

Prases K. MOHANTY,Dayal R. PARHI

《机械工程前沿（英文）》 2014年 第9卷 第4期   页码 317-330 doi: 10.1007/s11465-014-0304-z

摘要：

Planning of the shortest/optimal route is essential for efficient operation of autonomous mobile robot or vehicle. In this paper Invasive Weed Optimization (IWO), a new meta-heuristic algorithm, has been implemented for solving the path planning problem of mobile robot in partially or totally unknown environments. This meta-heuristic optimization is based on the colonizing property of weeds. First we have framed an objective function that satisfied the conditions of obstacle avoidance and target seeking behavior of robot in partially or completely unknown environments. Depending upon the value of objective function of each weed in colony, the robot avoids obstacles and proceeds towards destination. The optimal trajectory is generated with this navigational algorithm when robot reaches its destination. The effectiveness, feasibility, and robustness of the proposed algorithm has been demonstrated through series of simulation and experimental results. Finally, it has been found that the developed path planning algorithm can be effectively applied to any kinds of complex situation.

关键词： mobile robot     obstacle avoidance     Invasive Weed Optimization     navigation    

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Trajectory planning of mobile robots using indirect solution of optimal control method in generalized

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

摘要：

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    

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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

摘要： We study a system assisted by (UAVs), where the UAVs act as edge servers to provide computing services for Internet of Things devices. Our goal is to minimize the energy consumption of this system by planning the trajectories of UAVs. This problem is difficult to address because when planning the trajectories, we need to consider not only the order of stop points (SPs), but also their deployment (including the number and locations) and the association between UAVs and SPs. To tackle this problem, we present an energy-efficient algorithm (TPA) which comprises three phases. In the first phase, a algorithm with a variable population size is adopted to update the number and locations of SPs at the same time. In the second phase, the
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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

摘要： 针对公路轨迹规划问题，本文将智能计算量分配（ICBA）引入基于候选曲线的规划算法——基于序优化的差分进化（OODE）算法，提出IOODE算法（I代表ICBA）。OODE分轨迹曲线和加速度变化两部分规划轨迹，采用差分进化（DE）算法通过求解子问题计算各候选曲线的评价，然后通过比较曲线评价从候选者中选取最优曲线。DE的迭代次数越多，曲线评价越准确。因此，本文考虑对不同曲线智能分配迭代计算量，以减少消耗的总计算量，同时保证所选中的最优曲线以足够高的概率是真实最优曲线。仿真结果显示，IOODE在保证求解质量不下降的前提下，比OODE快约20%。本文中提出的计算量分配框架也可应用于其他基于候选曲线的规划方法来提高算法效率。

关键词： 智能计算量分配；轨迹规划；公路规划；智能汽车；序优化    

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基于多目标社会学习鸽群优化的多无人机避障控制 Research

阮婉莹1,段海滨1,2

《信息与电子工程前沿（英文）》 2020年 第21卷 第5期   页码 649-808 doi: 10.1631/FITEE.2000066

摘要： 提出多目标社会学习鸽群优化（MSLPIO）方法，将其应用于无人机编队避障控制。该算法特点在于，每只鸽子在更新过程中并非向全局最优的鸽子学习，而是学习比自己占优的任何鸽子。在地图指南针算子和地标算子中引入社会学习因子。此外，为避免参数设置的盲目性，采用维数相关的参数设置方法。本文模拟了5架飞机在复杂障碍环境下的飞行过程，实验结果验证了该方法的有效性。与改进的多目标鸽群优化算法和改进的非占优排序遗传算法相比，MSLPIO具有更好的收敛性。

关键词： 无人机；避障；鸽群优化；多目标社会学习鸽群优化    

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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

《机械工程前沿（英文）》 2022年 第17卷 第1期 doi: 10.1007/s11465-021-0658-y

摘要： When free-floating space robots perform space tasks, the satellite base attitude is disturbed by the dynamic coupling. The disturbance of the base orientation may affect the communication between the space robot and the control center on earth. In this paper, the enhanced bidirectional approach is proposed to plan the manipulator trajectory and eliminate the final base attitude variation. A novel acceleration level state equation for the nonholonomic problem is proposed, and a new intermediate variable-based Lyapunov function is derived and solved for smooth joint trajectory and restorable base trajectories. In the method, the state equation is first proposed for dual-arm robots with and without end constraints, and the system stability is analyzed to obtain the system input. The input modification further increases the system stability and simplifies the calculation complexity. Simulations are carried out in the end, and the proposed method is validated in minimizing final base attitude change and trajectory smoothness. Moreover, the minute internal force during the coordinated operation and the considerable computing efficiency increases the feasibility of the method during space tasks.

关键词： free-floating space robot     dual arm     coordinated operation     base attitude restoration     bidirectional approach    

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Dynamic prediction of moving trajectory in pipe jacking: GRU-based deep learning framework

《结构与土木工程前沿（英文）》   页码 994-1010 doi: 10.1007/s11709-023-0942-5

摘要： The moving trajectory of the pipe-jacking machine (PJM), which primarily determines the end quality of jacked tunnels, must be controlled strictly during the entire jacking process. Developing prediction models to support drivers in performing rectifications in advance can effectively avoid considerable trajectory deviations from the designed jacking axis. Hence, a gated recurrent unit (GRU)-based deep learning framework is proposed herein to dynamically predict the moving trajectory of the PJM. In this framework, operational data are first extracted from a data acquisition system; subsequently, they are preprocessed and used to establish GRU-based multivariate multistep-ahead direct prediction models. To verify the performance of the proposed framework, a case study of a large pipe-jacking project in Shanghai and comparisons with other conventional models (i.e., long short-term memory (LSTM) network and recurrent neural network (RNN)) are conducted. In addition, the effects of the activation function and input time-step length on the prediction performance of the proposed framework are investigated and discussed. The results show that the proposed framework can dynamically and precisely predict the PJM moving trajectory during the pipe-jacking process, with a minimum mean absolute error and root mean squared error (RMSE) of 0.1904 and 0.5011 mm, respectively. The RMSE of the GRU-based models is lower than those of the LSTM- and RNN-based models by 21.46% and 46.40% at the maximum, respectively. The proposed framework is expected to provide an effective decision support for moving trajectory control and serve as a foundation for the application of deep learning in the automatic control of pipe jacking.

关键词： dynamic prediction     moving trajectory     pipe jacking     GRU     deep learning    

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Parasitic rotation evaluation and avoidance of 3-UPU parallel mechanism

Haibo QU, Yuefa FANG, Sheng GUO

《机械工程前沿（英文）》 2012年 第7卷 第2期   页码 210-218 doi: 10.1007/s11465-012-0317-4

摘要：

Based on the prototype of 3-UPU (universal-prismatic-universal joint) parallel mechanism proposed by Tsai [ ], the parasitic rotation evaluation is performed and calculated the bound of instability of SNU Seoul National University 3-UPU parallel mechanism. Through analysis of the terminal constraint system of the 3-UPU parallel mechanism, the equation about the parasitic rotation and limited clearance is presented. Then the norm of possible parasitic rotation is employed to evaluate the mechanism stability with limited clearance. The higher this number the worst is the pose, the lower it is the best it is. And the contour atlas of parasitic rotation is obtained, which can be used for further analysis and design. With the practice experiment result of SUN 3-UPU parallel mechanism, we find it’s bound of instability, which indicates there will appear the parasitic rotation when the number exceeds the bound. Finally, the method for avoidance of possible parasitic motions is presented by adding redundantly actuated limbs.

关键词： parallel mechanism     3-UPU (universal-prismatic-universal joint)     parasitic motions     limited clearance     redundantly actuated limbs    

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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

摘要： 提出一种双层控制算法以规划配备四轮轮毂电机的自动驾驶卡车的行驶轨迹。该控制算法主要由主层非线性模型预测控制（MLN-MPC）算法和次层非线性模型预测控制（SLN-MPC）算法组成，其中，MLN-MPC控制算法用于规划合理的卡车行驶轨迹，SLN-MPC控制算法将车轮纵向滑移率限制在稳定区域，避免卡车在驱动过程中发生过度打滑。总体而言，该控制算法为一个闭环控制系统。在离线仿真环境下，通过AMESim、Simulink、dSPACE和TruckSim仿真软件联合仿真。仿真结果表明，本文所提算法能规划一条合理的车辆避障行驶轨迹，在不确定路面附着系数条件下能将车辆纵向滑移率控制在合理范围。此外，为评估该算法在实际应用中的可行性，在联合仿真系统中加入驾驶员模型验证该算法的稳定性与鲁棒性。与传统的基于PID控制算法相比，该算法具有更低的计算能耗。

关键词： 自动驾驶卡车；轨迹规划；非线性模型预测控制；纵向滑移率    

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标题 作者 时间 类型 操作

Research on Anthropomorphic Obstacle Avoidance Trajectory Planning for Adaptive Driving Scenarios Based

Jian Wu,Yang Yan,Yulong Liu,Yahui Liu,

期刊论文

移动机器人障碍躲避的最佳路径

郭戈

期刊论文

Obstacle-circumventing adaptive control of a four-wheeled mobile robot subjected to motion uncertainties

期刊论文

autonomous mining: design and development of an unmanned electric shovel via point cloud-based optimal trajectoryplanning

期刊论文

General Optimal Trajectory Planning: Enabling Autonomous Vehicles with the Principle of Least Action

Heye Huang,Yicong Liu,Jinxin Liu,Qisong Yang,Jianqiang Wang,David Abbink,Arkady Zgonnikov,

期刊论文

Multiobjective trajectory optimization of intelligent electro-hydraulic shovel

期刊论文

A new efficient optimal path planner for mobile robot based on Invasive Weed Optimization algorithm

Prases K. MOHANTY,Dayal R. PARHI

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

基于多目标社会学习鸽群优化的多无人机避障控制

阮婉莹1,段海滨1,2

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

Dynamic prediction of moving trajectory in pipe jacking: GRU-based deep learning framework

期刊论文

Parasitic rotation evaluation and avoidance of 3-UPU parallel mechanism

Haibo QU, Yuefa FANG, Sheng GUO

期刊论文

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

王鸿超1,张伟伟1,吴训成1,曹昊天2,高巧明3,罗素云1

期刊论文