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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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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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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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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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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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Dynamic response surface methodology using Lasso regression for organic pharmaceutical synthesis

《化学科学与工程前沿（英文）》 2022年第16卷第2期页码 221-236 doi: 10.1007/s11705-021-2061-y

摘要： To study the dynamic behavior of a process, time-resolved data are collected at different time instants during each of a series of experiments, which are usually designed with the design of experiments or the design of dynamic experiments methodologies. For utilizing such time-resolved data to model the dynamic behavior, dynamic response surface methodology (DRSM), a data-driven modeling method, has been proposed. Two approaches can be adopted in the estimation of the model parameters: stepwise regression, used in several of previous publications, and Lasso regression, which is newly incorporated in this paper for the estimation of DRSM models. Here, we show that both approaches yield similarly accurate models, while the computational time of Lasso is on average two magnitude smaller. Two case studies are performed to show the advantages of the proposed method. In the first case study, where the concentrations of different species are modeled directly, DRSM method provides more accurate models compared to the models in the literature. The second case study, where the reaction extents are modeled instead of the species concentrations, illustrates the versatility of the DRSM methodology. Therefore, DRSM with Lasso regression can provide faster and more accurate data-driven models for a variety of organic synthesis datasets.

关键词： data-driven modeling pharmaceutical organic synthesis Lasso regression dynamic response surface methodology

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Multi-UAV cooperative target tracking with bounded noise for connectivity preservation

Rui Zhou, Yu Feng, Bin Di, Jiang Zhao, Yan Hu,jzhao@buaa.edu.cn

《信息与电子工程前沿（英文）》 2020年第21卷第10期页码 1413-1534 doi: 10.1631/FITEE.1900617

摘要： We investigate cooperative target tracking of multiple unmanned aerial vehicles (UAVs) with a limited communication range. This is an integration of UAV motion control, target state estimation, and network topology control. We first present the communication topology and basic notations for , and introduce the distributed . Then, convergence and boundedness of the estimation errors using the filter are analyzed, and potential functions are proposed for communication link maintenance and collision avoidance. By taking stable target tracking into account, a distributed potential function based UAV motion controller is discussed. Since only the estimation of the target state rather than the state itself is available for UAV motion control and UAV motion can also affect the accuracy of state estimation, it is clear that the UAV motion control and target state estimation are coupled. Finally, the stability and convergence properties of the coupled system under are analyzed in detail and demonstrated by simulations.

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一种面向地面区域检测和目标跟踪的多传感器系统协同调度方法 Research Article

张昀普,付强,单甘霖

《信息与电子工程前沿（英文）》 2023年第24卷第2期页码 245-258 doi: 10.1631/FITEE.2200121

摘要：本文提出一种面向多任务协同的多传感器系统协同调度方法，并将其应用于地面区域检测和目标跟踪。调度的目的是选择最佳的传感器来完成分配的作战任务，并获得最佳作战收益。首先建立区域检测模型，并提出检测风险的计算方法以量化在调度中的检测收益。然后结合道路约束信息和多普勒盲区信息建立地面目标跟踪模型，并引入后验克拉美罗下限评估未来时刻的跟踪精度。最后，考虑检测、跟踪和能耗控制的需求建立目标函数，通过求解目标函数，得到最优的传感器调度方案。仿真结果表明，所提传感器调度方法可以选择合适的传感器完成所需作战任务，并在区域检测、目标跟踪和能耗控制方面均具有良好性能。

关键词：传感器调度；区域检测；目标跟踪；道路约束；多普勒盲区

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Amodified variable rate particle filter for maneuvering target tracking

Yun-fei GUO,Kong-shuai FAN,Dong-liang PENG,Ji-an LUO,Han SHENTU

《信息与电子工程前沿（英文）》 2015年第16卷第11期页码 985-994 doi: 10.1631/FITEE.1500149

摘要： To address the problem of maneuvering target tracking, where the target trajectory has prolonged smooth regions and abrupt maneuvering regions, a modified variable rate particle filter (MVRPF) is proposed. First, a Cartesian-coordinate based variable rate model is presented. Compared with conventional variable rate models, the proposed model does not need any prior knowledge of target mass or external forces. Consequently, it is more convenient in practical tracking applications. Second, a maneuvering detection strategy is adopted to adaptively adjust the parameters in MVRPF, which helps allocate more state points at high maneuver regions and fewer at smooth regions. Third, in the presence of small measurement errors, the unscented particle filter, which is embedded in MVRPF, can move more particles into regions of high likelihood and hence can improve the tracking performance. Simulation results illustrate the effectiveness of the proposed method.

关键词： Maneuvering target tracking Prolonged smooth regions Variable rate model Maneuver detection

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水下移动传感器网络的高能效节点定位和目标跟踪 None

Hua-yan CHEN, Mei-qin LIU, Sen-lin ZHANG

《信息与电子工程前沿（英文）》 2018年第19卷第8期页码 999-1012 doi: 10.1631/FITEE.1700598

摘要：水下移动传感器网络（UMSNs）不要固定，易实现快速部署，其节点可随洋流飘动扩散，易实现大范围的监测。因此，UMSNs是远海环境下实现短期、迅速、大范围组网监测跟踪的有效手段。UMSNs目标跟踪的挑战在于高精度的目标跟踪效果需要以高精度的实时节点定位为前提，而UMSNs的节点位置是不断变化的。为获得高精度的实时节点定位结果，运用传统的定位方案对其进行连续定位将会消耗大量能量。为减少UMSNs目标跟踪过程中节点定位所消耗能量，利用节点移动在时间空间的相关性，结合多步Levinson-Durbin线性预测算法提出高精度的长周期节点位置预测算法（HLMP）。通过高精度位置预测减少节点对实时通信定位的依赖，进而减少网络能耗。根据目标跟踪过程中节点与目标的相关性，提出同时定位跟踪算法（SLAT），进一步提高节点的定位精度。仿真结果表明，HLMP算法能够在低能耗的情况下显着提高定位精度，SLAT算法进一步降低节点定位误差。更高的定位精度将同步提高目标跟踪性能。

关键词：水下移动传感器网络；高能效；节点定位；目标跟踪

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基于并行处理机制的多基地雷达多目标跟踪算法

徐洪奎,王东进,陈卫东

《中国工程科学》 2007年第9卷第11期页码 151-156

摘要：

针对用多基地雷达系统跟踪近距离多高速机动目标的场合，提出了一种基于并行处理机制的跟踪算法。该算法将整个观测数据的关联过程拆分成若干个并行子关联进程，并将确定目标的跟踪过程拆分成两个并行的三维子进程，同时对距离和拟测值及多普勒速度拟测值进行迭代滤波。仿真结果表明，该算法不仅能够在杂波中快速精确地跟踪多个机动目标，而且具有很好的收敛特性和稳定性。

关键词：多基地雷达系统多目标跟踪并行处理 MSJPDA 并行扩展卡尔曼滤波

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Anovel approach of noise statistics estimate using H∞ filter in target tracking

Xie WANG,Mei-qin LIU,Zhen FAN,Sen-lin ZHANG

《信息与电子工程前沿（英文）》 2016年第17卷第5期页码 449-457 doi: 10.1631/FITEE.1500262

摘要： Noise statistics are essential for estimation performance. In practical situations, however, a priori information of noise statistics is often imperfect. Previous work on noise statistics identification in linear systems still requires initial prior knowledge of the noise. A novel approach is presented in this paper to solve this paradox. First, we apply the H∞ filter to obtain the system state estimates without the common assumptions about the noise in conventional adaptive filters. Then by applying state estimates obtained from the H∞ filter, better estimates of the noise mean and covariance can be achieved, which can improve the performance of estimation. The proposed approach makes the best use of the system knowledge without a priori information with modest computation cost, which makes it possible to be applied online. Finally, numerical examples are presented to show the efficiency of this approach.

关键词： Noise estimate H∞ filter Target tracking

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基于深度前馈神经网络的多基地外辐射源雷达高精度目标跟踪 Research Article

徐宝兄,易建新,程丰,龚子平,万显荣

《信息与电子工程前沿（英文）》 2023年第24卷第8期页码 1214-1230 doi: 10.1631/FITEE.2200260

摘要：在雷达系统中，目标跟踪误差主要来自运动模型和非线性量测。在评估跟踪算法时，其跟踪精度是主要衡量准则。为提高跟踪精度，本文将跟踪问题表述为从量测到目标状态的回归模型，提出一种基于改进深度前馈神经网络（MDFNN）的跟踪算法。所提MDFNN跟踪算法引入一种滤波层来描述输入量测序列的时序关系，并分析了最优量测序列长度。仿真和实测的外辐射源雷达数据测试表明，在所考虑的场景下，所提算法跟踪精度优于基于扩展卡尔曼滤波器（EKF）、无迹卡尔曼滤波器（UKF）和递归神经网络（RNN）的跟踪方法。

关键词：深度前馈神经网络；滤波层；外辐射源雷达；目标跟踪；跟踪精度

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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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一种跟踪性能不占优的多无人机协同目标跟踪方法 Research Articles

郑之,蔡舜诚

《信息与电子工程前沿（英文）》 2021年第22卷第10期页码 1334-1350 doi: 10.1631/FITEE.2000362

摘要：目标跟踪是无人机领域研究热点之一。本文针对无人机跟踪性能不占优，以及目标具有灵活、智能运动特征的情形，研究了多无人机协同目标跟踪问题。提出一种基于目标意图估计的多无人机协同跟踪策略。首先设计了一种具有降维和最大感知覆盖约束的轨迹特征提取方法，以降低无人机跟踪代价，并对目标典型的3类运动模式，根据环境和目标轨迹主要特征，设计了一种意图估计方法；然后，设计了一种在障碍物环境中基于最小可达距离和最小转角代价的MDA-Voronoi图，证明分析了目标被感知的概率；接着，设计了无人机的协同跟踪策略，以减小目标跟踪丢失的间隙，增加目标被感知的时间；通过纳什Q学习方法，在奖励函数中考虑了避障、跟踪代价、感知质量、飞行约束等因素，将最优动作策略作为无人机的控制输入。最后，通过仿真验证了本文方法能在无人机跟踪性能不占优的情况下提高跟踪质量。

关键词：协同跟踪；意图估计；MDA-Voronoi图；多无人机；性能不占优

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标题作者时间类型操作

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

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

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

移动机器人障碍躲避的最佳路径