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关键词

跟踪 2

轨迹跟踪 2

DPP）；分布式功率转换器；开关电容转换器 1

GPS轨迹 1

Hilare 机器人 1

MCDB 1

MPPT）；差分功率处理（Differential power processing 1

MSJPDA 1

OFDM 1

Spark 1

S频段 1

三维过程；三元数；最小均方；卡尔曼滤波器 1

下肢外骨骼机器人；人机交互；运动学习；轨迹生成；运动基元；黑盒优化 1

下肢机器人；捕获点；步态相位；人机系统平衡 1

主题模型 1

乘客热点预测 1

二分追踪；多智能体系统；异步脉冲；一致性 1

二肽基肽酶4 1

人-机交互 1

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Output tracking control of mobile manipulators based on dynamical sliding-mode control

WUYuxiang, FENG Ying, HU Yueming

《机械工程前沿（英文）》 2007年 第2卷 第1期   页码 110-115 doi: 10.1007/s11465-007-0019-5

摘要： A dynamical sliding-mode controller is devised to track the output of mobile manipulators. During the investigation, firstly a reduced dynamic model considering the dynamics of the driving motor is developed for mobile manipulators. Then, the system is decomposed into four lower-dimensional subsystems by means of diffeomorphism and nonlinear input transformation. Moreover, a design method of the dynamical sliding-mode controller that is applied to the output tracking of mobile manipulators is proposed. The simulation results indicate that the dynamical sliding-mode controller can not only track the given trajectory correctly but also reduce the chattering of sliding-mode control system considerably.

关键词： lower-dimensional     nonlinear     trajectory     tracking     diffeomorphism    

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基于车辆动力学的轨迹跟踪器设计

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

摘要：

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。

关键词： 轨迹跟踪     反馈线性化     导航    

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Enhancing the terrain adaptability of a multirobot cooperative transportation system via novel connectors and optimized cooperative strategies

《机械工程前沿（英文）》 2023年 第18卷 第3期 doi: 10.1007/s11465-023-0754-2

摘要： Given limited terrain adaptability, most existing multirobot cooperative transportation systems (MRCTSs) mainly work on flat pavements, restricting their outdoor applications. The connectors’ finite deformation capability and the control strategies’ limitations are primarily responsible for this phenomenon. This study proposes a novel MRCTS based on tracked mobile robots (TMRs) to improve terrain adaptability and expand the application scenarios of MRCTSs. In structure design, we develop a novel 6-degree-of-freedom passive adaptive connector to link multiple TMRs and the transported object (the communal payload). In addition, the connector is set with sensors to measure the position and orientation of the robot with respect to the object for feedback control. In the control strategy, we present a virtual leader–physical follower collaborative paradigm. The leader robot is imaginary to describe the movement of the entire system and manage the follower robots. All the TMRs in the system act as follower robots to transport the object cooperatively. Having divided the whole control structure into the leader robot level and the follower robot level, we convert the motion control of the two kinds of robots to trajectory tracking control problems and propose a novel double closed-loop kinematics control framework. Furthermore, a control law satisfying saturation constraints is derived to ensure transportation stability. An adaptive control algorithm processes the wheelbase uncertainty of the TMR. Finally, we develop a prototype of the TMR-based MRCTS for experiments. In the trajectory tracking experiment, the developed MRCTS with the proposed control scheme can converge to the reference trajectory in the presence of initial tracking errors in a finite time. In the outdoor experiment, the proposed MRCTS consisting of four TMRs can successfully transport a payload weighing 60 kg on an uneven road with the single TMR’s maximum load limited to 15 kg. The experimental results demonstrate the effectiveness of the structural design and control strategies of the TMR-based MRCTS.

关键词： multirobot system     cooperative transportation     terrain adaptability     trajectory tracking     collaborative paradigm     uneven road    

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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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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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速度约束条件下基于步进电机驱动的Hilare 机器人航点导航的控制 Article

Robins Mathew,Somashekhar S. Hiremath

《工程（英文）》 2018年 第4卷 第4期   页码 491-499 doi: 10.1016/j.eng.2018.07.013

摘要：

在障碍物密集的环境中，找到一条从初始位置到目标位置的最优轨迹，并控制一台Hilare 机器人沿着该轨迹行驶仍是一项具有挑战性的任务。为了完成这个任务，控制环中通常需要加入路径规划器以及轨迹跟踪控制器。本文的目的是在一台由步进电机驱动的Hilare 机器人上实现轨迹跟踪控制的任务。其中，轨迹由航点集合表示。在设计过程中，控制器需要考虑处理方向连续的离散航点，并且需要考虑不同的执行器速度约束。本文利用多目标粒子群优化（multi-objective particle swarm optimization, MOPSO）的方法来调整控制器的参数。MOPSO 通过最小化移动机器人在追踪预定义轨迹时的平均航迹误差以及平均线速度误差来得到最优的控制器参数。实验中，移动机器人被控制从起始点沿着一条由航点表示的轨迹行驶到达目标点。实验同样给出对路径规划器生成的轨迹，以及自定义轨迹的跟踪结果。基于移动机器人的实验结果验证了本文方法对不同形式轨迹跟踪的有效性。

关键词： 轨迹跟踪     自适应控制     航点导航     Hilare 机器人     粒子群优化     随机路图    

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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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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

摘要： Train speed trajectory optimization is a significant issue in railway traffic systems, and it plays a key role in determining energy consumption and travel time of trains. Due to the complexity of real-world operational environments, a variety of factors can lead to the uncertainty in energy-consumption. To appropriately characterize the uncertainties and generate a robust speed trajectory, this study specifically proposes distance-speed networks over the inter-station and treats the uncertainty with respect to energy consumption as discrete sample-based random variables with correlation. The problem of interest is formulated as a stochastic constrained shortest path problem with travel time threshold constraints in which the expected total energy consumption is treated as the evaluation index. To generate an approximate optimal solution, a Lagrangian relaxation algorithm combined with dynamic programming algorithm is proposed to solve the optimal solutions. Numerical examples are implemented and analyzed to demonstrate the performance of proposed approaches.

关键词： train speed trajectory optimization     railway operation     stochastic programming    

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

《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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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

摘要： This research formulates a path-following control problem subjected to wheel slippage and skid and solves it using a logic-based control scheme for a wheeled mobile robot (WMR). The novelty of the proposed scheme lies in its methodology that considers both longitudinal and lateral slip components. Based on the derived slip model, the controller for longitudinal motion slip has been synthesized. Various control parameters have been studied to investigate their effects on the performance of the controller resulting in selection of their optimum values. The designed controller for lateral slip or skid is based on the proposed side friction model and skid check condition. Considering a car-like WMR, simulation results demonstrate the effectiveness of the proposed control scheme. The robot successfully followed the desired circular trajectory in the presence of wheel slippage and skid. This research finds its potential in various applications involving WMR navigation and control.

关键词： Robot modeling     Robot navigation     Slip and skid control     Wheeled mobile robots    

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Real-time immune-inspired optimum state-of-charge trajectory estimation using upcoming route information

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

摘要：

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    

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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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Dynamic Target Tracking of Unmanned Aerial Vehicles Under Unpredictable Disturbances

Yanjie Chen,Yangning Wu,Limin Lan,Hang Zhong,Zhiqiang Miao,Hui Zhang,Yaonan Wang,

《工程（英文）》 doi: 10.1016/j.eng.2023.05.017

摘要： This study proposes an image-based visual servoing (IBVS) method based on a velocity observer for an unmanned aerial vehicle (UAV) for tracking a dynamic target in Global Positioning System (GPS)-denied environments. The proposed method derives the simplified and decoupled image dynamics of underactuated UAVs using a constructed virtual camera and then considers the uncertainties caused by the unpredictable rotations and velocities of the dynamic target. A novel image depth model that extends the IBVS method to track a rotating target with arbitrary orientations is proposed. The depth model ensures image feature accuracy and image trajectory smoothness in rotating target tracking. The relative velocities of the UAV and the dynamic target are estimated using the proposed velocity observer. Thanks to the velocity observer, translational velocity measurements are not required, and the control chatter caused by noise-containing measurements is mitigated. An integral-based filter is proposed to compensate for unpredictable environmental disturbances in order to improve the anti-disturbance ability. The stability of the velocity observer and IBVS controller is analyzed using the Lyapunov method. Comparative simulations and multistage experiments are conducted to illustrate the tracking stability, anti-disturbance ability, and tracking robustness of the proposed method with a dynamic rotating target.

关键词： Unmanned aerial vehicle     Visual servoing     Velocity observer     Target tracking    

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Wheel tracking methods to evaluate moisture sensitivity of hot-mix asphalt mixtures

Jie HAN,Harihar Shiwakoti

《结构与土木工程前沿（英文）》 2016年 第10卷 第1期   页码 30-43 doi: 10.1007/s11709-016-0318-1

摘要： Existing test methods to determine moisture sensitivity in hot-mix asphalt (HMA) mixtures are time consuming and inconsistent. This research focused on wheel tracking devices to evaluate moisture sensitivity. The Asphalt Pavement Analyzer (APA) and the Hamburg Wheel Tracking Device (HWTD) were used for this research. Compacted cylindrical samples were fabricated using a Superpave Gyratory compactor. This study selected two most commonly used mixtures, SM-12.5A with PG 64-22 binder in overlay projects and SM-19A mixtures with PG 64-22 binder for major modification projects at Kansas Department of Transportation. Test results show that APA tests could induce stripping in most samples without any anti-stripping agent, which could be identified visually. However, APA results did not indicate any stripping inflection point while the HWTD results showed stripping inflection points, which are important to identify stripping potential of mixtures. The APA results show that wet tests are severe at lower temperatures. The HWTD results show improvement in the performance using anti-stripping agents at later stage. The HWTD test is more effective as a rapid test method in case of determining moisture sensitivity. Laboratory results from this study should be verified and correlated with field performance.

关键词： hot-mix asphalt     moisture sensitivity     rutting     wheel tracking test    

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标题 作者 时间 类型 操作

Output tracking control of mobile manipulators based on dynamical sliding-mode control

WUYuxiang, FENG Ying, HU Yueming

期刊论文

基于车辆动力学的轨迹跟踪器设计

陈杨,刘大学,贺汉根,戴斌

期刊论文

Enhancing the terrain adaptability of a multirobot cooperative transportation system via novel connectors and optimized cooperative strategies

期刊论文

Multiobjective trajectory optimization of intelligent electro-hydraulic shovel

期刊论文

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

期刊论文

速度约束条件下基于步进电机驱动的Hilare 机器人航点导航的控制

Robins Mathew,Somashekhar S. Hiremath

期刊论文

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

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

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

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

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

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

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

Dynamic Target Tracking of Unmanned Aerial Vehicles Under Unpredictable Disturbances

Yanjie Chen,Yangning Wu,Limin Lan,Hang Zhong,Zhiqiang Miao,Hui Zhang,Yaonan Wang,

期刊论文

Wheel tracking methods to evaluate moisture sensitivity of hot-mix asphalt mixtures

Jie HAN,Harihar Shiwakoti

期刊论文