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HQD-RRT^(*):a high-quality path planner for mobile robot in dynamic environment

The Journal of China Universities of Posts and Telecommunications 2022年第3期29卷 69-80页

作者： Li Qinghua Wang Jiahui Li Haiming Feng Chao School of Electronic and Information Engineering(Department of Physics) Qilu University of Technology (Shandong Academy of Sciences)Jinan 250353China Jinan Engineering Laboratory of Human-Machine Inteligent Cooperation Qilu University of Technology Shandong Academy of Sciences)Jinan 250353China School of Electrical Engineering and Automation Qilu University of Technology(Shandong Academy of Sciences)Jinan 250353China

Mobile robots have been used for many industrial scenarios which can realize automated manufacturing process instead of human workers. To improve the quality of the optimal rapidly-exploring random tree(RRT^(*)) for planning path in dynamic environment, a high-quality dynamic rapidly-exploring random tree(HQD-RRT^(*)) algorithm is proposed in this paper, which generates a high-quality solution with optimal path length in dynamic environment. This method proceeds in two stages: initial path generation and path re-planning. Firstly, the initial path is generated by an improved smart rapidly-exploring random tree(RRT^(*)-SMART) algorithm, and the state tree information is stored as prior knowledge. During the process of path execution, a strategy of obstacle avoidance is proposed to avoid moving obstacles. The cost and smoothness of path are considered to re-plan the initial path to improve the path quality in this strategy. Compared with related work, a higher-quality path in dynamic environment can be achieved in this paper. HQD-RRT^(*) algorithm can obtain an optimal path with better stability. Simulations on the static and dynamic environment are conducted to clarify the efficiency of HQD-RRT^(*) in avoiding unknown obstacles.

关键词： path planning dynamic environment smart rapidly-exploring random tree(RRT^(*)-SMART) re-planning

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Vision‐audio fusion SLAM in dynamic environments

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CAAI Transactions on Intelligence Technology 2023年第4期8卷 1364-1373页

作者： Tianwei Zhang Huayan Zhang Xiaofei Li Shenzhen Institute of Artificial Intelligence and Robotics for Society ShenzhenChina Department of Robotics Ritsumeikan UniversityShigaJapan Westlake University and Westlake Institute for Advanced Study HangzhouChina

Moving humans,agents,and subjects bring many challenges to robot self‐localisation and environment perception.To adapt to dynamic environments,SLAM researchers typically apply several deep learning image segmentation models to eliminate these moving obstacles.However,these moving obstacle segmentation methods cost too much computation resource for the onboard processing of mobile robots.In the current industrial environment,mobile robot collaboration scenario,the noise of mobile robots could be easily found by on‐board audio‐sensing processors and the direction of sound sources can be effectively acquired by sound source estimation algorithms,but the distance estimation of sound sources is difficult.However,in the field of visual perception,the 3D structure information of the scene is relatively easy to obtain,but the recognition and segmentation of moving objects is more difficult.To address these problems,a novel vision‐audio fusion method that combines sound source localisation methods with a visual SLAM scheme is proposed,thereby eliminating the effect of dynamic obstacles on multi‐agent systems.Several heterogeneous robots experiments in different dynamic scenes indicate very stable self‐localisation and environment reconstruction performance of our method.

关键词： dynamic environment intelligent robots sensor fusion

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DRO-SLAM: real-time object-aware SLAM for navigation robots and autonomous driving in dynamic environments

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The Journal of China Universities of Posts and Telecommunications 2023年第3期30卷 14-24页

作者： Wang Zixian Zhang Miao Yan Danfeng School of Computer Science Beijing University of Posts and TelecommunicationsBeijing 100876China

Traditional simultaneous localization and mapping(SLAM) mostly performs under the assumption of an ideal static environment, which is not suitable for dynamic environments in the real world. dynamic real-time object-aware SLAM(DRO-SLAM) is proposed in this paper, which is a visual SLAM that can realize simultaneous localizing and mapping and tracking of moving objects indoor and outdoor at the same time. It can use target recognition, oriented fast and rotated brief(ORB) feature points, and optical flow assistance to track multi-target dynamic objects and remove them during dense point cloud reconstruction while estimating their pose. By verifying the algorithm effect on the public dataset and comparing it with other methods, it can be obtained that the proposed algorithm has certain guarantees in real-time and accuracy, it also provides more functions. DRO-SLAM can provide the solution to automatic navigation which can realize lightweight deployment, provide more vehicles, pedestrians and other environmental information for navigation.

关键词： simultaneous localization and mapping(SLAM) object tracking stereo vision dynamic environment data association

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Behavior-based Autonomous Navigation and Formation Control of Mobile Robots in Unknown Cluttered dynamic environments with dynamic Target Tracking

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International Journal of Automation and computing 2021年第5期18卷 766-786页

作者： Nacer Hacene Boubekeur Mendil Laboratory of Industrial Technology and Information(LITI) Faculty of TechnologyUniversity of BejaiaBejaia 06000Algeria Department of Automatics and Electromechanics Faculty of Science and TechnologyUniversity of GhardaiaGhardaia 47000Algeria

While different species in nature have safely solved the problem of navigation in a dynamic environment, this remains a challenging task for researchers around the world. The paper addresses the problem of autonomous navigation in an unknown dynamic environment for a single and a group of three wheeled omnidirectional mobile robots(TWOMRs). The robot has to track a dynamic target while avoiding dynamic obstacles and dynamic walls in an unknown and very dense environment. It adopts a behavior-based controller that consists of four behaviors: "target tracking", "obstacle avoidance", "dynamic wall following" and "avoid robots". The paper considers the problem of kinematic saturation. In addition, it introduces a strategy for predicting the velocity of dynamic obstacles based on two successive measurements of the ultrasonic sensors to calculate the velocity of the obstacle expressed in the sensor frame. Furthermore, the paper proposes a strategy to deal with dynamic walls even when they have U-like or V-like shapes. The approach can also deal with the formation control of a group of robots based on the leader-follower structure and the behavior-based control, where the robots have to get together and maintain a given formation while navigating toward the target, avoiding obstacles and walls in a dynamic environment. The effectiveness of the proposed approaches is demonstrated via simulation.

关键词： Behavior-based autonomous navigation dynamic obstacles and walls dynamic target tracking formation control of multi-robot systems dynamic environment

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A novel algorithm for SLAM in dynamic environments using landscape theory of aggregation

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Journal of Central South University 2016年第10期23卷 2587-2594页

作者：华承昊窦丽华方浩付浩 School of Automation Beijing Institute of Technology College of Mechatronic Engineering and Automation National University of Defense Technology

To tackle the problem of simultaneous localization and mapping(SLAM) in dynamic environments, a novel algorithm using landscape theory of aggregation is presented. By exploiting the coherent explanation how actors form alignments in a game provided by the landscape theory of aggregation, the algorithm is able to explicitly deal with the ever-changing relationship between the static objects and the moving objects without any prior models of the moving objects. The effectiveness of the method has been validated by experiments in two representative dynamic environments: the campus road and the urban road.

关键词： mobile robot simultaneous localization and mapping(SLAM) dynamic environment landscape theory of aggregation iterative closest point

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Research on SLAM System incorporating Weakly-Supervised Learning in dynamic environment

Research on SLAM System incorporating Weakly-Supervised Lear...

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第32届中国控制与决策会议

作者： Yi Cheng Renyan Fang School of Electrical Engineering and Automation Tiangong University

Based on Simultaneous Localization and Mapping（SLAM） and deep learning,a SLAM system incorporating weak supervised learning semantic segmentation is proposed for navigation and positioning of mobile robots in dynamic environment.After obtaining the image from the camera,the weakly supervised learning semantic segmentation network SEC was used to segment the semantic information,and then the obtained semantic information was used to eliminate the dynamic feature points,so as to improve the accuracy of pose estimation of the system.Compared to traditional orb-slam2,this article shows an overall improvement in performance on publicly available high dynamic sequence data sets.The results show that after improvement,both absolute error,relative drift and rotational drift are significantly reduced,which proves that compared with orb-slam2,this method can improve the accuracy of pose estimation in dynamic environment.

关键词： dynamic environment ORB-SLAM2 Weakly-Supervised Learning Semantic Segmentation

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Improved particle swarm optimization algorithm in dynamic environment

Improved particle swarm optimization algorithm in dynamic en...

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第26届中国控制与决策会议

作者： Changcheng Xiang Xuegang Tan Yi Yang Key Laboratory of Biologic Resources Protection and Utilization Hubei Minzu University College of Forestry and Horticulture Hubei Minzu University College of Science Hubei Minzu University

In this paper,The improved Particle Swarm Optimization in dynamic objective function environment（DOFPSO） is purposed. The dynamic environment will change with the time t. The DOFPSO algorithm discuss that how to determine changes of the time（environment） and how to keep population diversity. The improved algorithm has the ability to fast response the change of environment and could find the best fitness value quickly. The results of experiment indicate that DOFPSO is more effective than particle swarm optimization（PSO） and restart method particle swarm optimization（RMPSO） in the response of change of environment and fast convergence.

关键词： Particle Swarm Optimization dynamic environment Convergence

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Improved Visual Odometry Based on SSD Algorithm in dynamic environment

Improved Visual Odometry Based on SSD Algorithm in Dynamic E...

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第三十九届中国控制会议

作者： Enbao Wang Yali Zhou Qizhi Zhang School of Automation Beijing Information Science ＆ Technology University

In recent years, Simultaneous Localization and Mapping（SLAM） have made great progress in the field of robots.However, the majority of SLAM algorithms are currently designed and processed in static environments. In the dynamic environment, due to the existence of moving objects, the SLAM system is easy to mismatching the pose estimation, which will affect its positioning accuracy and mapping accuracy. In this paper, the traditional Oriented FAST and Rotated Brief（ORB）-SLAM2 system is improved to deal with these problems. Firstly, the Single Shot MultiBox Detector（SSD）algorithm is used to detect the dynamic targets in the scene, and then the multi-layer optical flow is constructed to remove the dynamic feature points by optical flow tracking. Secondly, the static feature points in the scene are matched and the pose are calculated to solve the problems caused by a large number of matches, such as large system computation and low operating efficiency. Finally, testing on the TUM datasets shows that the absolute trajectory error and the relative pose error of the improved ORB-SLAM2 system are significantly reduced compared to the traditional ORB-SLAM2 system.

关键词： Visual Odometry dynamic environment ORB-SLAM2 SSD Multi-layer Optical Flow

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跨界知识搜索与本土化战略

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世界经济导刊 2022年第12期

作者：于光辉李元旭张鹏

本文基于跨界搜索理论和资源基础观，结合中国跨国企业海外子公司的本土化实践，考察了跨界知识搜索与海外子公司本土化战略之间的关系，建立了跨界知识搜索与本土化战略关系的理论模型，利用186家中国跨国企业海外子公司的问卷数据进... 详细信息

本文基于跨界搜索理论和资源基础观，结合中国跨国企业海外子公司的本土化实践，考察了跨界知识搜索与海外子公司本土化战略之间的关系，建立了跨界知识搜索与本土化战略关系的理论模型，利用186家中国跨国企业海外子公司的问卷数据进行了实证检验。研究发现：跨界知识搜索对于海外子公司的本土化战略具有正向作用；环境动态性在海外子公司的跨界知识搜索与本土化战略之间起到了部分调节作用，即环境动态性正向调节了跨界制度知识搜索与本土化战略之间的关系。

关键词：跨界知识搜索本土化战略动态环境海外子公司　　boundary-spanning search of knowledge localization strategy dynamic environment overseas subsidiaries

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Robust Visual SLAM Algorithm for dynamic Indoor environments

Robust Visual SLAM Algorithm for Dynamic Indoor Environments

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第40届中国控制会议

作者： Lijin Fang Keqi Wang Yingcai Wan School of Robot Science and Engineering Northeastern University

In real scenes,traditional visual SLAM algorithm is limited by the assumption of static environment.Due to the influence of moving objects,the traditional visual odometry makes a large number of mismatches and makes the system unable to run statically in real scenes.In this paper,a robust visual SLAM algorithm for indoor dynamic environment is proposed based on deep learning.Firstly,the object detection network is used to detect the dynamic objects and determine the moving objects in the surrounding environment.Secondly,a semantic data association method and key-frame selection strategy are proposed for the tracking thread and the local graph thread to reduce the influence of moving objects on the algorithm accuracy.Finally,the TUM open datasets is validated.Experimental results show that the proposed method reduces the mean root mean square error（RMSE） of ORB-SLAM2 by more than 94% in dynamic scenarios compared to the previous improved ORB-SLAM2.In addition,compared to other deep learning based SLAM systems,the proposed method has a better balance of speed and accuracy in dynamic environments.

关键词： simultaneous localization and mapping dynamic environment object detection motion removal

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