A Rapid-exploration Random Tree(RRT)autonomous detection algorithm based on the multi-guide-node deflection strategy and Karto Simultaneous Localization and Mapping(SLAM)algorithm was proposed to solve the problems of...A Rapid-exploration Random Tree(RRT)autonomous detection algorithm based on the multi-guide-node deflection strategy and Karto Simultaneous Localization and Mapping(SLAM)algorithm was proposed to solve the problems of low efficiency of detecting frontier boundary points and drift distortion in the process of map building in the traditional RRT algorithm in the autonomous detection strategy of mobile robot.Firstly,an RRT global frontier boundary point detection algorithm based on the multi-guide-node deflection strategy was put forward,which introduces the reference value of guide nodes’deflection probability into the random sampling function so that the global search tree can detect frontier boundary points towards the guide nodes according to random probability.After that,a new autonomous detection algorithm for mobile robots was proposed by combining the graph optimization-based Karto SLAM algorithm with the previously improved RRT algorithm.The algorithm simulation platform based on the Gazebo platform was built.The simulation results show that compared with the traditional RRT algorithm,the proposed RRT autonomous detection algorithm can effectively reduce the time of autonomous detection,plan the length of detection trajectory under the condition of high average detection coverage,and complete the task of autonomous detection mapping more efficiently.Finally,with the help of the ROS-based mobile robot experimental platform,the performance of the proposed algorithm was verified in the real environment of different obstacles.The experimental results show that in the actual environment of simple and complex obstacles,the proposed RRT autonomous detection algorithm was superior to the traditional RRT autonomous detection algorithm in the time of detection,length of detection trajectory,and average coverage,thus improving the efficiency and accuracy of autonomous detection.展开更多
A line-feature based SLAM algorithm is presented in this paper to resolve the conflict between the requirements of computational complexity and information-richness within the point-feature based SLAM algorithm, All o...A line-feature based SLAM algorithm is presented in this paper to resolve the conflict between the requirements of computational complexity and information-richness within the point-feature based SLAM algorithm, All operations required for building and maintaining the map, such as model-setting, data association, and state-updating, are described and formulated. This approach has been programmed and successfully tested in the simulation work, and results are shown at the end of this paper.展开更多
FastSLAM is a popular framework which uses a Rao-Blackwellized particle filter to solve the simultaneous localization and mapping problem（SLAM）. However, in this framework there are two important potential limitatio...FastSLAM is a popular framework which uses a Rao-Blackwellized particle filter to solve the simultaneous localization and mapping problem（SLAM）. However, in this framework there are two important potential limitations, the particle depletion problem and the linear approximations of the nonlinear functions. To overcome these two drawbacks, this paper proposes a new FastSLAM algorithm based on revised genetic resampling and square root unscented particle filter（SR-UPF）. Double roulette wheels as the selection operator, and fast Metropolis-Hastings（MH） as the mutation operator and traditional crossover are combined to form a new resampling method. Amending the particle degeneracy and keeping the particle diversity are both taken into considerations in this method. As SR-UPF propagates the sigma points through the true nonlinearity, it decreases the linearization errors. By directly transferring the square root of the state covariance matrix, SR-UPF has better numerical stability. Both simulation and experimental results demonstrate that the proposed algorithm can improve the diversity of particles, and perform well on estimation accuracy and consistency.展开更多
传统的温室作业方式依赖于人工操作,工作效率低且难以保证作业的质量和稳定性。温室自主导航系统可以实现温室内自动化导航和作业,提高温室作物的生产效率和品质。因此,设计一种定位与地图构建(Simultaneous Localization And Mapping, ...传统的温室作业方式依赖于人工操作,工作效率低且难以保证作业的质量和稳定性。温室自主导航系统可以实现温室内自动化导航和作业,提高温室作物的生产效率和品质。因此,设计一种定位与地图构建(Simultaneous Localization And Mapping, SLAM)技术下的温室自主导航系统,可利用激光雷达等传感器实时构建温室内的地图,并利用SLAM算法实现自主定位与导航。为了提高系统的鲁棒性和性能,提出了一种基于改进粒子滤波算法的姿态估计方法。试验结果表明:该温室自主导航系统能够高效准确地实现温室内的自主导航任务,为农业生产提供了一种新的自动化解决方案。展开更多
[目的/意义]在大田作业、野外放牧、果园采收等典型农业应用场景下,多机器人(包括移动式智能农机装备等)高精度快速协同同步定位与建图(Simultaneous Localization and Mapping,SLAM)是智慧农业乃至无人农场的关键基础和核心支撑。与单...[目的/意义]在大田作业、野外放牧、果园采收等典型农业应用场景下,多机器人(包括移动式智能农机装备等)高精度快速协同同步定位与建图(Simultaneous Localization and Mapping,SLAM)是智慧农业乃至无人农场的关键基础和核心支撑。与单机器人SLAM相比,多机器人协同SLAM具有精度高、范围广、实时性强、扩展性好等优势,但在农业种植和养殖等自然复杂环境下,由于场景动态可变、地形复杂多变、环境丰富多样、通信约束受限等多重因素叠加影响,尚存在诸多问题与挑战。[进展]现有研究主要是从通用基础技术的视角对多机器人SLAM的研究脉络、优缺点、适用条件和关键核心问题等方面进行总结归纳,但缺乏针对农业复杂场景特性的剖析。本研究面向农业复杂场景的主要特征,以“多传感器数据融合—协同定位—协同建图—回环检测”为关键技术主线,分析了多机器人协同SLAM的优缺点及其在农业领域的适用性;从多机器人协同作业的视角,明晰了集中式、分布式和混合式三种主要协同框架的优势、局限性及适用的典型农业应用场景;进而探讨了农业复杂场景下多机器人SLAM存在的多传感器融合精度偏低、协同通信环境受限、相对位姿估计准确性不高等突出问题。[结论/展望]从优化数据融合底层算法、融合深度学习和强化学习、引入大语言模型、应用数字孪生技术等方面,对农业复杂环境下多机器人SLAM的未来发展方向和趋势进行了展望。展开更多
基金This research was funded by National Natural Science Foundation of China(No.62063006)Guangxi Science and Technology Major Program(No.2022AA05002)+2 种基金Key Laboratory of AI and Information Processing(Hechi University),Education Department of Guangxi Zhuang Autonomous Region(No.2022GXZDSY003)Guangxi Key Laboratory of Spatial Information and Geomatics(Guilin University of Technology)(No.21-238-21-16)Innovation Project of Guangxi Graduate Education(No.YCSW2023352).
文摘A Rapid-exploration Random Tree(RRT)autonomous detection algorithm based on the multi-guide-node deflection strategy and Karto Simultaneous Localization and Mapping(SLAM)algorithm was proposed to solve the problems of low efficiency of detecting frontier boundary points and drift distortion in the process of map building in the traditional RRT algorithm in the autonomous detection strategy of mobile robot.Firstly,an RRT global frontier boundary point detection algorithm based on the multi-guide-node deflection strategy was put forward,which introduces the reference value of guide nodes’deflection probability into the random sampling function so that the global search tree can detect frontier boundary points towards the guide nodes according to random probability.After that,a new autonomous detection algorithm for mobile robots was proposed by combining the graph optimization-based Karto SLAM algorithm with the previously improved RRT algorithm.The algorithm simulation platform based on the Gazebo platform was built.The simulation results show that compared with the traditional RRT algorithm,the proposed RRT autonomous detection algorithm can effectively reduce the time of autonomous detection,plan the length of detection trajectory under the condition of high average detection coverage,and complete the task of autonomous detection mapping more efficiently.Finally,with the help of the ROS-based mobile robot experimental platform,the performance of the proposed algorithm was verified in the real environment of different obstacles.The experimental results show that in the actual environment of simple and complex obstacles,the proposed RRT autonomous detection algorithm was superior to the traditional RRT autonomous detection algorithm in the time of detection,length of detection trajectory,and average coverage,thus improving the efficiency and accuracy of autonomous detection.
基金Supported by National Natural Science Foundation of P. R. China (60475031)
文摘A line-feature based SLAM algorithm is presented in this paper to resolve the conflict between the requirements of computational complexity and information-richness within the point-feature based SLAM algorithm, All operations required for building and maintaining the map, such as model-setting, data association, and state-updating, are described and formulated. This approach has been programmed and successfully tested in the simulation work, and results are shown at the end of this paper.
基金supported by National Natural Science Foundation of China(No.61101197)Research Fund for the Doctoral Program of Higher Education of China(No.20093219120025)
文摘FastSLAM is a popular framework which uses a Rao-Blackwellized particle filter to solve the simultaneous localization and mapping problem（SLAM）. However, in this framework there are two important potential limitations, the particle depletion problem and the linear approximations of the nonlinear functions. To overcome these two drawbacks, this paper proposes a new FastSLAM algorithm based on revised genetic resampling and square root unscented particle filter（SR-UPF）. Double roulette wheels as the selection operator, and fast Metropolis-Hastings（MH） as the mutation operator and traditional crossover are combined to form a new resampling method. Amending the particle degeneracy and keeping the particle diversity are both taken into considerations in this method. As SR-UPF propagates the sigma points through the true nonlinearity, it decreases the linearization errors. By directly transferring the square root of the state covariance matrix, SR-UPF has better numerical stability. Both simulation and experimental results demonstrate that the proposed algorithm can improve the diversity of particles, and perform well on estimation accuracy and consistency.
文摘传统的温室作业方式依赖于人工操作,工作效率低且难以保证作业的质量和稳定性。温室自主导航系统可以实现温室内自动化导航和作业,提高温室作物的生产效率和品质。因此,设计一种定位与地图构建(Simultaneous Localization And Mapping, SLAM)技术下的温室自主导航系统,可利用激光雷达等传感器实时构建温室内的地图,并利用SLAM算法实现自主定位与导航。为了提高系统的鲁棒性和性能,提出了一种基于改进粒子滤波算法的姿态估计方法。试验结果表明:该温室自主导航系统能够高效准确地实现温室内的自主导航任务,为农业生产提供了一种新的自动化解决方案。
文摘[目的/意义]在大田作业、野外放牧、果园采收等典型农业应用场景下,多机器人(包括移动式智能农机装备等)高精度快速协同同步定位与建图(Simultaneous Localization and Mapping,SLAM)是智慧农业乃至无人农场的关键基础和核心支撑。与单机器人SLAM相比,多机器人协同SLAM具有精度高、范围广、实时性强、扩展性好等优势,但在农业种植和养殖等自然复杂环境下,由于场景动态可变、地形复杂多变、环境丰富多样、通信约束受限等多重因素叠加影响,尚存在诸多问题与挑战。[进展]现有研究主要是从通用基础技术的视角对多机器人SLAM的研究脉络、优缺点、适用条件和关键核心问题等方面进行总结归纳,但缺乏针对农业复杂场景特性的剖析。本研究面向农业复杂场景的主要特征,以“多传感器数据融合—协同定位—协同建图—回环检测”为关键技术主线,分析了多机器人协同SLAM的优缺点及其在农业领域的适用性;从多机器人协同作业的视角,明晰了集中式、分布式和混合式三种主要协同框架的优势、局限性及适用的典型农业应用场景;进而探讨了农业复杂场景下多机器人SLAM存在的多传感器融合精度偏低、协同通信环境受限、相对位姿估计准确性不高等突出问题。[结论/展望]从优化数据融合底层算法、融合深度学习和强化学习、引入大语言模型、应用数字孪生技术等方面,对农业复杂环境下多机器人SLAM的未来发展方向和趋势进行了展望。
