Assessment of Wet Season Precipitation in the Central United States by the Regional Climate Simulation of the WRFG Member in NARCCAP and Its Relationship with Large-Scale Circulation Biases

Assessment of past-climate simulations of regional climate models(RCMs)is important for understanding the reliability of RCMs when used to project future regional climate.Here,we assess the performance and discuss possible causes of biases in a WRF-based RCM with a grid spacing of 50 km,named WRFG,from the North American Regional Climate Change Assessment Program(NARCCAP)in simulating wet season precipitation over the Central United States for a period when observational data are available.The RCM reproduces key features of the precipitation distribution characteristics during late spring to early summer,although it tends to underestimate the magnitude of precipitation.This dry bias is partially due to the model’s lack of skill in simulating nocturnal precipitation related to the lack of eastward propagating convective systems in the simulation.Inaccuracy in reproducing large-scale circulation and environmental conditions is another contributing factor.The too weak simulated pressure gradient between the Rocky Mountains and the Gulf of Mexico results in weaker southerly winds in between,leading to a reduction of warm moist air transport from the Gulf to the Central Great Plains.The simulated low-level horizontal convergence fields are less favorable for upward motion than in the NARR and hence,for the development of moist convection as well.Therefore,a careful examination of an RCM’s deficiencies and the identification of the source of errors are important when using the RCM to project precipitation changes in future climate scenarios.

Large-scale spatial data visualization method based on augmented reality

Background A task assigned to space exploration satellites involves detecting the physical environment within a certain space.However,space detection data are complex and abstract.These data are not conducive for researchers'visual perceptions of the evolution and interaction of events in the space environment.Methods A time-series dynamic data sampling method for large-scale space was proposed for sample detection data in space and time,and the corresponding relationships between data location features and other attribute features were established.A tone-mapping method based on statistical histogram equalization was proposed and applied to the final attribute feature data.The visualization process is optimized for rendering by merging materials,reducing the number of patches,and performing other operations.Results The results of sampling,feature extraction,and uniform visualization of the detection data of complex types,long duration spans,and uneven spatial distributions were obtained.The real-time visualization of large-scale spatial structures using augmented reality devices,particularly low-performance devices,was also investigated.Conclusions The proposed visualization system can reconstruct the three-dimensional structure of a large-scale space,express the structure and changes in the spatial environment using augmented reality,and assist in intuitively discovering spatial environmental events and evolutionary rules.

Boundary Gap Based Reactive Navigation in Unknown Environments

Due to the requirements for mobile robots to search or rescue in unknown environments,reactive navigation which plays an essential role in these applications has attracted increasing interest.However,most existing reactive methods are vulnerable to local minima in the absence of prior knowledge about the environment.This paper aims to address the local minimum problem by employing the proposed boundary gap(BG)based reactive navigation method.Specifically,the narrowest gap extraction algorithm(NGEA)is proposed to eliminate the improper gaps.Meanwhile,we present a new concept called boundary gap which enables the robot to follow the obstacle boundary and then get rid of local minima.Moreover,in order to enhance the smoothness of generated trajectories,we take the robot dynamics into consideration by using the modified dynamic window approach(DWA).Simulation and experimental results show the superiority of our method in avoiding local minima and improving the smoothness.

Path Planning Approach in Unknown Environment

This paper presents a new algorithm of path planning for mobile robots, which utilises the characteristics of the obstacle border and fuzzy logical reasoning. The environment topology or working space is described by the time-variable grid method that can be further described by the moving obstacles and the variation of path safety. Based on the algorithm, a new path planning approach for mobile robots in an unknown environment has been developed. The path planning approach can let a mobile robot find a safe path from the current position to the goal based on a sensor system. The two types of machine learning： advancing learning and exploitation learning or trial learning are explored, and both are applied to the learning of mobile robot path planning algorithm. Comparison with A＊ path planning approach and various simulation results are given to demonstrate the efficiency of the algorithm. This path planning approach can also be applied to computer games.

Adaptive Decentralized Asymptotic Tracking Control for Large-Scale Nonlinear Systems With Unknown Strong Interconnections

An adaptive decentralized asymptotic tracking control scheme is developed in this paper for a class of large-scale nonlinear systems with unknown strong interconnections,unknown time-varying parameters,and disturbances.First,by employing the intrinsic properties of Gaussian functions for the interconnection terms for the first time,all extra signals in the framework of decentralized control are filtered out,thereby removing all additional assumptions imposed on the interconnec-tions,such as upper bounding functions and matching conditions.Second,by introducing two integral bounded functions,asymptotic tracking control is realized.Moreover,the nonlinear filters with the compensation terms are introduced to circumvent the issue of“explosion of complexity”.It is shown that all the closed-loop signals are bounded and the tracking errors converge to zero asymptotically.In the end,a simulation example is carried out to demonstrate the effectiveness of the proposed approach.

Safe Navigation for UAV-Enabled Data Dissemination by Deep Reinforcement Learning in Unknown Environments

Unmanned aerial vehicles(UAVs) are increasingly considered in safe autonomous navigation systems to explore unknown environments where UAVs are equipped with multiple sensors to perceive the surroundings. However, how to achieve UAVenabled data dissemination and also ensure safe navigation synchronously is a new challenge. In this paper, our goal is minimizing the whole weighted sum of the UAV’s task completion time while satisfying the data transmission task requirement and the UAV’s feasible flight region constraints. However, it is unable to be solved via standard optimization methods mainly on account of lacking a tractable and accurate system model in practice. To overcome this tough issue,we propose a new solution approach by utilizing the most advanced dueling double deep Q network(dueling DDQN) with multi-step learning. Specifically, to improve the algorithm, the extra labels are added to the primitive states. Simulation results indicate the validity and performance superiority of the proposed algorithm under different data thresholds compared with two other benchmarks.

A State Estimation Method for Sound Environment System with Unknown Observation Mechanism by Introducing Fuzzy Inference

The observed phenomena in real sound environment system often contain uncertainty such as the additional external noise with unknown statistics. Furthermore, there is complex nonlinear relationship between the specific signal and the observations, and it cannot be exactly expressed in any definite functional form. In these situations, it is one of reasonable analysis methods to treat the objective sound environment system as a fuzzy system. In this study, a state estimation method for a specific signal under the existence of an unknown observation mechanism and external noise of unknown statistics is proposed by introducing fuzzy inference. The effectiveness of the proposed theoretical method is experimentally confirmed by applying it to the actually observed data in the sound environment.

Integrated controlling technique of ecological environment in Shendong Mining Area

To enclose the interactive relation between the underground mining with suitable protection for surface ecological environments and surface prevention of ecological environments adapting to mining disturbing was researched and developed core of this technique. There are three aspects of controlling ecological environments, to dispose and renew before exploitation, to protect surface ecological environments in the exploitative process and to repair and build up after exploitation. Based on the moving law of overburden strata in shallow seam, the surface subsidence law and the growth law of vegetation in subsidence mine area, the integrated controlling technique has been developed synthetically by methods of theoretic analysis, laboratory simulation, numerical calculation, commercial test etc.. It includes the key techniques of aquifer-protective mining, filtering and purging of mine water through goaf, preventing and extinguishing fire in shallow seam no-rock roadway layout and waste disposal in underground, frame-building ecological functional sphere before exploitation, frame-building the ecological cycle using system after mining and so on.

Study on the Marine Sedimentary Environment Evolution of the Southern Laizhou Bay Under the Impact of Port Projects

The southern Laizhou Bay is mainly composed of silt-sandy coasts with diverse landforms, and its marine hydrodynamic environment is sensitive to human activities. Marine hydrodynamic and sedimentary environments of the study area have changed under the influence of large-scale port projects in recent years. In this paper, the evolution of hydrodynamic environment, deposition rate, and geochemical characteristics were studied based on sediment grain size, element analysis and ^(210)Pb dating of two cores, in order to analyze the influence of Weifang Port on marine environmental evolution, and provide theoretical and practical basis for protecting marine environment in developing marine resources reasonably. Results showed that sediments of the two cores were relatively coarser and mainly composed of silty sand. Sediments above 230 cm in core WF1 and 218 cm in core WF2 were deposited since 1855 when the Yellow River appeared to deposit its sediments within the modern active delta, and the average deposition rate was between 0.3 and 0.5 cm a^(-1). Implement of Weifang Port projects in 1997 and 2007 created great influence on the sedimentary environment evolution in the surrounding waters, and the deposition rate was significantly increased. The average annual deposition rates were 5.1 cm and 3.5 cm in WF1 and WF2 respectively between 1997 and 2007. Content of heavy metals in sediments showed no obvious change in the vertical, indicating that the heavy metals were less affected by human activity and there was no significant accumulation of such metals in the study area.

基于启发式映射法的未知三维环境路径规划

针对全局环境的未知性和常用规划算法易得到局部最优解的特性,提出一种基于启发式映射法的未知三维环境路径规划算法。在分段规划的策略下,使用启发式映射法来获取分段的子目标,克服了常规直线映射法在一定情况下难以确定目标的缺陷,结合三维环境下的A^(*)算法完成了移动智能体的规划任务。通过MATLAB进行仿真,与直线映射法相比,得到了更短的路径长度,且运行时间更短,可以较好地解决未知三维环境的路径规划问题。

神经网络阻尼比模型及工业机器人导纳控制

在工业机器人打磨过程中,环境刚度随未知环境的变化,将对力控制精度产生不利的影响,针对环境刚度变化的问题,该文提出一种基于神经网络阻尼比模型的自适应导纳控制方法。在导纳控制设计中,根据力误差与系统阻尼比之间的机理关系,设计激励函数,构造神经网络阻尼比模型;通过该模型使阻尼比在线调整,适应末端环境的刚度变化,实现力到位置自适应转换的导纳控制。与常规导纳控制进行仿真比较,结果表明所提出的力控制策略力误差更小,响应速度更快,能适应变刚度的未知打磨环境。

基于神经网络参数自学习的阻抗控制

针对机器人在打磨过程中环境刚度和位置未知,传统的阻抗控制难以有效保持打磨质量的问题,提出了一种基于调节参数神经网络自学习的阻抗控制。由于基于李雅普诺夫稳定性理论设计的阻尼参数补偿方法中调节参数的选取直接影响系统的控制性能,根据阻尼补偿的数学描述,构建神经网络,用于其参数自适应调节,设计不同的激励函数用于反映阻尼在多种因素影响下变化的特征。通过所搭建的神经网络在线学习,实现参数的优化,以适应打磨过程环境变化。在斜面、平面及曲面等不同环境下,考虑其刚度突变、刚度动态变化、期望力动态变化等因素的仿真实验,结果表明所提出的控制方法与传统控制方法相比,具有更小的超调和稳态误差,并能够适应环境参数未知的情况,明显提高打磨质量和效率。

面向未知环境探索的分层架构路径规划算法

为实现无人平台在未知环境下的高效路径探索,该文提出了一种以“感知-规划-控制”分层架构为基础的路径规划算法。在感知层通过Cartographer建图算法实时构建未知环境的二维栅格地图。在规划层,通过Canny边缘检测、基于密度的聚类算法、效能函数评估选择最佳探索目标点,并在规划的效能函数中引入探索方向延续性概念,克服了传统路径规划反复探索已知环境的难题。在控制层,通过概率路线图算法规划从当前位姿到目标点的最短路径,并通过纯跟踪算法和向量直方图算法实现了路径无碰撞跟踪。3种典型环境下的仿真实验表明,所提出的算法在不同环境下均具有较高的探索效率和完成度。

移动机器人沿墙算法研究综述

沿墙算法是一种局部路径规划算法,算法的全局收敛性往往优于其他传统方法。为了促进沿墙算法在路径规划领域中的研究,综述了运用沿墙算法规划路径的移动机器人的最新进展和研究成果。根据沿墙算法的避障策略与实现,将沿墙算法分为基于目标方向角的沿墙算法、基于M-Line的沿墙算法、基于最优避障方向的沿墙算法、基于历史信息的沿墙算法以及基于多算法融合的沿墙算法,并对各类算法优缺点进行总结。然而在实际环境中,由于仿真环境的理想化、行为协作机制的僵硬化及传感器的干扰性,沿墙算法在实际应用中与理论结果间存在较大差距。考虑到现有算法仍存在诸多不足,从开发适用于复杂实际环境的沿墙算法、探索更高效合理的多行为切换机制及降低实际环境中传感器噪声影响这3个方面展望了沿墙路径规划算法的未来发展趋势。

Obstacle Avoidance Capability for Multi-Target Path Planning in Different Styles of Search

This study investigates robot path planning for multiple agents,focusing on the critical requirement that agents can pursue concurrent pathways without collisions.Each agent is assigned a task within the environment to reach a designated destination.When the map or goal changes unexpectedly,particularly in dynamic and unknown environments,it can lead to potential failures or performance degradation in various ways.Additionally,priority inheritance plays a significant role in path planning and can impact performance.This study proposes a ConflictBased Search(CBS)approach,introducing a unique hierarchical search mechanism for planning paths for multiple robots.The study aims to enhance flexibility in adapting to different environments.Three scenarios were tested,and the accuracy of the proposed algorithm was validated.In the first scenario,path planning was applied in unknown environments,both stationary and mobile,yielding excellent results in terms of time to arrival and path length,with a time of 2.3 s.In the second scenario,the algorithm was applied to complex environments containing sharp corners and unknown obstacles,resulting in a time of 2.6 s,with the algorithm also performing well in terms of path length.In the final scenario,the multi-objective algorithm was tested in a warehouse environment containing fixed,mobile,and multi-targeted obstacles,achieving a result of up to 100.4 s.Based on the results and comparisons with previous work,the proposed method was found to be highly effective,efficient,and suitable for various environments.

基于模糊控制算法的矿山爆破机器人路径规划

针对矿山爆破机器人工作环境的复杂性和不确定性,为提高其在这种挑战性环境中的自主操作性和安全性,结合视觉导航原理和模糊控制算法提出一种未知环境下的局部路径规划策略。该策略充分利用机器人搭载的摄像头和激光雷达进行环境探测,实时获取障碍物和目标信息,通过模糊控制器调整机器人的运动偏转角度及步长,从而有效地避开障碍物并到达预定目标点。仿真结果表明,该策略不但能使机器人以最优的路径到达目标点,而且显著降低了机器人在多障碍物环境下发生碰撞的风险,提高了矿山爆破机器人的工作效率和安全性。

未知环境下基于视觉与激光融合的SLAM系统研究与实现

在未知环境中实现机器人的自主导航与环境建图是一个极具挑战的研究课题,SLAM是应对这一挑战的关键技术之一。基于研究现状分析、系统方案设计、技术实现与实验验证等内容,结合视觉与激光各自的优势,设计一种未知环境下基于视觉与激光融合的SLAM系统。通过针对关键帧和滑动窗口的平衡选取策略以及分类优化策略对初始位姿和特征点进行优化,提高了系统的鲁棒性,可以输出高精度地图;通过实现以智能小车为载体的演示系统验证了所提出算法的可行性和实用性。

未知环境下桥式起重机智能避障路径规划研究

文中针对桥式起重机工作空间的环境信息未知情况下的路径规划,提出了改进人工蜂群算法和滚动规划策略相结合的智能避障路径规划新方法。通过分析并借鉴机器人在未知环境下的路径规划方法,以人工蜂群智能算法为基础,改变蜜源初始化机制以及观察蜂跟随机制,并结合滚动规划策略,提出子目标点选取机制,为了避免起重机多次升降,固定其吊装高度进行局部路径规划。保证了起重机在全局环境信息未知条件下能依据不断更新的窗口信息智能避障安全到达目标点,提高了算法的搜索精度和吊装效率,仿真结果验证了该方案的可行性和有效性。

一种未知环境下移动机器人自主导航方法

为提高未知环境下移动机器人自主导航性能,提出了一种未知环境下目标驱动的自主导航方法。不依赖实时更新的地图信息,移动机器人直接从激光雷达读数中构建候选目标点,并根据机器人自身位置和终点位置从候选目标点中选择最优点作为局部目标点。改进的基于深度强化学习的局部规划器从输入信息中直接输出动作信号,实现端到端的控制,使得移动机器人能够快速、安全地到达局部目标点,直至到达终点。实验表明,所提导航方法能够在未知复杂的环境下,可靠且高效地完成导航任务。与最近边界导航方法相比,平均路径长度减少了6.63%,平均运行时间缩短了19.11%,具有成功率高、路径短、速度快等优点。

基于改进DWA和人工势场法的编队控制研究

针对动态窗口法(DWA)在多机器人编队控制中缺乏个体间避碰策略,以及编队成员间的相互作用导致无法有效到达目标点等问题,提出了一种融合改进的DWA和人工势场法的编队控制算法。通过三项关键改进来增强编队控制能力:首先,融合人工势场法实现个体间有效的避碰;其次,在滑动窗口设计引入转向约束规则,增强机器人朝目标导航和未知环境下的全局搜索能力;最后重构评价函数,实现在远距离时优先考虑目标导航,在近距离时强化探索能力。实验表明改进后的DWA提高了多机器人体编队控制的效果和避碰性能,提升了机器人的导航效率及其在复杂环境下的避障和避碰性能。