Fine-grained grid computing model for Wi-Fi indoor localization in complex environments

The fingerprinting-based approach using the wireless local area network(WLAN)is widely used for indoor localization.However,the construction of the fingerprint database is quite time-consuming.Especially when the position of the access point(AP)or wall changes,updating the fingerprint database in real-time is difficult.An appropriate indoor localization approach,which has a low implementation cost,excellent real-time performance,and high localization accuracy and fully considers complex indoor environment factors,is preferred in location-based services(LBSs)applications.In this paper,we proposed a fine-grained grid computing(FGGC)model to achieve decimeter-level localization accuracy.Reference points(RPs)are generated in the grid by the FGGC model.Then,the received signal strength(RSS)values at each RP are calculated with the attenuation factors,such as the frequency band,three-dimensional propagation distance,and walls in complex environments.As a result,the fingerprint database can be established automatically without manual measurement,and the efficiency and cost that the FGGC model takes for the fingerprint database are superior to previous methods.The proposed indoor localization approach,which estimates the position step by step from the approximate grid location to the fine-grained location,can achieve higher real-time performance and localization accuracy simultaneously.The mean error of the proposed model is 0.36 m,far lower than that of previous approaches.Thus,the proposed model is feasible to improve the efficiency and accuracy of Wi-Fi indoor localization.It also shows high-accuracy performance with a fast running speed even under a large-size grid.The results indicate that the proposed method can also be suitable for precise marketing,indoor navigation,and emergency rescue.

LSDA-APF:A Local Obstacle Avoidance Algorithm for Unmanned Surface Vehicles Based on 5G Communication Environment

In view of the complex marine environment of navigation,especially in the case of multiple static and dynamic obstacles,the traditional obstacle avoidance algorithms applied to unmanned surface vehicles(USV)are prone to fall into the trap of local optimization.Therefore,this paper proposes an improved artificial potential field(APF)algorithm,which uses 5G communication technology to communicate between the USV and the control center.The algorithm introduces the USV discrimination mechanism to avoid the USV falling into local optimization when the USV encounter different obstacles in different scenarios.Considering the various scenarios between the USV and other dynamic obstacles such as vessels in the process of performing tasks,the algorithm introduces the concept of dynamic artificial potential field.For the multiple obstacles encountered in the process of USV sailing,based on the International Regulations for Preventing Collisions at Sea(COLREGS),the USV determines whether the next step will fall into local optimization through the discriminationmechanism.The local potential field of the USV will dynamically adjust,and the reverse virtual gravitational potential field will be added to prevent it from falling into the local optimization and avoid collisions.The objective function and cost function are designed at the same time,so that the USV can smoothly switch between the global path and the local obstacle avoidance.The simulation results show that the improved APF algorithm proposed in this paper can successfully avoid various obstacles in the complex marine environment,and take navigation time and economic cost into account.

Path Planning for Mobile Anchor Node in Localization Based on Ad-Hoc Localization Algorithm

For mobile anchor node static path planning cannot accord the actual distribution of node for dynamic adjustment. We take advantage of the high localization accuracy and low computational complexity of ad-hoc localization system( AHLos)algorithm. This article introduces mobile anchor nodes instead of the traditional fixed anchor nodes to improve the algorithm. The result shows that, through introduce the mobile anchor node, the information of initial anchor nodes can be configured more flexible.Meanwhile,with the use of the approximate location and the transition path,the distance and energy consumption of the mobile anchor node is greatly reduced.

A reliable acoustic path:Physical properties and a source localization method

The physical properties of a reliable acoustic path(RAP) are analysed and subsequently a weighted-subspacefitting matched field(WSF-MF) method for passive localization is presented by exploiting the properties of the RAP environment.The RAP is an important acoustic duct in the deep ocean,which occurs when the receiver is placed near the bottom where the sound velocity exceeds the maximum sound velocity in the vicinity of the surface.It is found that in the RAP environment the transmission loss is rather low and no blind zone of surveillance exists in a medium range. The ray theory is used to explain these phenomena.Furthermore,the analysis of the arrival structures shows that the source localization method based on arrival angle is feasible in this environment.However,the conventional methods suffer from the complicated and inaccurate estimation of the arrival angle.In this paper,a straightforward WSF-MF method is derived to exploit the information about the arrival angles indirectly.The method is to minimize the distance between the signal subspace and the spanned space by the array manifold in a finite range-depth space rather than the arrival-angle space.Simulations are performed to demonstrate the features of the method,and the results are explained by the arrival structures in the RAP environment.

Double BP Q-Learning Algorithm for Local Path Planning of Mobile Robot

Aiming at the dimension disaster problem, poor model generalization ability and deadlock problem in special obstacles environment caused by the increase of state information in the local path planning process of mobile robot, this paper proposed a Double BP Q-learning algorithm based on the fusion of Double Q-learning algorithm and BP neural network. In order to solve the dimensional disaster problem, two BP neural network fitting value functions with the same network structure were used to replace the two Q value tables in Double Q-Learning algorithm to solve the problem that the Q value table cannot store excessive state information. By adding the mechanism of priority experience replay and using the parameter transfer to initialize the model parameters in different environments, it could accelerate the convergence rate of the algorithm, improve the learning efficiency and the generalization ability of the model. By designing specific action selection strategy in special environment, the deadlock state could be avoided and the mobile robot could reach the target point. Finally, the designed Double BP Q-learning algorithm was simulated and verified, and the probability of mobile robot reaching the target point in the parameter update process was compared with the Double Q-learning algorithm under the same condition of the planned path length. The results showed that the model trained by the improved Double BP Q-learning algorithm had a higher success rate in finding the optimal or sub-optimal path in the dense discrete environment, besides, it had stronger model generalization ability, fewer redundant sections, and could reach the target point without entering the deadlock zone in the special obstacles environment.

Signal Path Reckoning Localization Method in Multipath Environment

In the wireless localization application, multipath propagation seriously affects the localization accuracy. This paper presents two algorithms to solve the multipath problem. Firstly, we improve the Line of Possible Mobile Device(LPMD) algorithm by optimizing the utilization of the direct paths for single-bound scattering scenario. Secondly, the signal path reckoning method with the assistance of geographic information system is proposed to solve the problem of localization with multi-bound scattering paths. With the building model's idealization, the proposed method refers to the idea of ray tracing and dead reckoning. According to the rule of wireless signal reflection, the signal propagation path is reckoned using the measurements of emission angle and propagation distance, and then the estimated location can be obtained. Simulation shows that the proposed method obtains better results than the existing geometric localization methods in multipath environment when the angle error is controlled.

3DT-PP:localization and path planning of mobile anchors over complex 3D terrains

Mobile anchors are widely used for localization in WSNs.However,special properties over 3D terrains limit the implementation of them.In this paper,a novel 3D localization algorithm is proposed,called 3 DT-PP,which utilizes path planning of mobile anchors over complex 3 D terrains,and simulations based upon the model of mountain surface network are conducted.The simulation results show that the algorithm decreases the position error by about 91%,8.7%and lowers calculation overhead by about 75%,1.3%,than the typical state-of-the-art localization algorithm(i.e.,'MDS-MAP','Landscape-3D').Thus,our algorithm is more potential in practical WSNs which are the characteristic of limited energy and 3D deployment.

Mode Analysis and Deepening Path of the Collaborative Education between Local Engineering Universities and Enterprises

At present, China's local engineering universities have basically formed five modes of collaborative education between Engineering Universities and Enterprises, namely the mode relied on university-enterprise alliance, the mode centered on the "Excellent Engineer Education and Training Program", the mode rooted in key disciplines, the mode based on the innovation and entrepreneurship education activities and the mode of carrying out international joint training, etc. However, in the process of university -enterprise collaborative education, there are still some shortcomings such as long-term deficiency, low fit and insufficient system. Therefore, three main ways to deepen the university-enterprise collaborative education are proposed: The first is to improve and implement the local government's policy guarantee system and incentive measures, fully mobilize and stimulate the enthusiasm and initiative of enterprises to participate in university -enterprise collaborative education;The second is to innovate the long-tenn operation mechanism of university-enterprise collaborative education, and to open up the last mile of educating and employing people;The third is to innovate the university-enterprise joint university-running mode and build a system of engineering talents that integrates innovation and practical capabilities.

AUV Local Path Planning Based on Acoustic Image Processing

The forward-looking image sonar is a necessary vision device for Autonomous Underwater Vehicles (AUV). Based on the acoustic image received from forward-looking image sonar, AUV local path is planned. When the environment model is made to adapt to local path planning, an iterative algorithm of binary conversion is used for image segmentation. Raw data of the acoustic image, which were received from serial port, are processed. By the use of “Mathematic Morphology" to filter noise, a mathematic model of environment for local path planning is established after coordinate transformation. The optimal path is searched by the distant transmission (Dt) algorithm. Simulation is conducted for the analysis of the algorithm. Experiment on the sea proves it reliable.

Experimental Evaluation of Trilateration-Based Outdoor Localizationwith LoRaWAN

Long Range Wide Area Network (LoRaWAN) in the Internet ofThings (IoT) domain has been the subject of interest for researchers. Thereis an increasing demand to localize these IoT devices using LoRaWAN dueto the quickly growing number of IoT devices. LoRaWAN is well suited tosupport localization applications in IoTs due to its low power consumptionand long range. Multiple approaches have been proposed to solve the localizationproblem using LoRaWAN. The Expected Signal Power (ESP) basedtrilateration algorithm has the significant potential for localization becauseESP can identify the signal’s energy below the noise floor with no additionalhardware requirements and ease of implementation. This research articleoffers the technical evaluation of the trilateration technique, its efficiency,and its limitations for the localization using LoRa ESP in a large outdoorpopulated campus environment. Additionally, experimental evaluations areconducted to determine the effects of frequency hopping, outlier removal, andincreasing the number of gateways on localization accuracy. Results obtainedfrom the experiment show the importance of calculating the path loss exponentfor every frequency to circumvent the high localization error because ofthe frequency hopping, thus improving the localization performance withoutthe need of using only a single frequency.

Local Path Planning Method of the Self-propelled Model Based on Reinforcement Learning in Complex Conditions

在实际波浪条件下面用一个大规模自己推动的模型进行水动力学和物理运动模拟测试是为研究轮船的环境适应性的一个重要工具。在自己推动的模型的航行测试期间，包括附近的各种各样的港口设备，航行设备，和轮船的复杂环境必须小心地被考虑，因为在这稠密的环境模型上的海波浪和风的影响是特别地重要的。为了改进自己推动的模型，的安全，这份报纸介绍基于加强学习学习的 Q 为模型碰撞回避与混乱想法结合了，以便改进计划的本地路径的可靠性。模拟和海测试结果证明这个算法与好适应性是在海风和波浪的干扰下面的自我航行模型的碰撞回避的一个更好的解决方案。

机耕道自动驾驶农机局部路径规划

针对机耕道场景下自动驾驶农机行驶的安全性、平稳性与规划实时性的实际需求,该研究提出了一种基于二次规划的局部路径规划方法。首先基于有限状态机构建农机机耕道行驶模式,其次采用横纵向解耦的方法,通过改进状态栅格法分别对农机速度行为和轨迹行为进行决策,随后利用二次规划方法生成满足多目标、多约束条件的农机轨迹和速度,得到最优路径,最后在多种行驶环境中进行仿真和实车试验,行驶参考速度为2 m/s。实车试验结果表明,在绕行静态障碍物场景中,规划轨迹的平均绝对曲率为0.021 m^(-1),最大绝对曲率为0.056 m^(-1),平均绝对横向误差为3.23 cm,最大绝对横向误差为8.69 cm,农机与障碍物外轮廓的距离大于0.76 m;在规避相向行驶、同向行驶和横穿机耕道的动态障碍物场景中,规划速度的平均绝对速度误差为0.08~0.12 m/s,绝对速度误差小于0.38 m/s,加速度变化范围为-0.38~0.44 m/s^(2)。在规划周期为200 ms的仿真试验中,该文算法平均耗时48 ms,最大耗时75 ms,相比采用静态状态栅格法平均耗时减少38 ms,算法效率提升44%。研究结果可为机耕道场景下的农机局部路径规划提供技术支持。

近海复杂环境下UUV动态路径规划方法研究

为解决近海环境下水下无人航行器(unmanned underwater vehicle,UUV)的动态路径规划问题,本文提出一种结合全局和局部动态路径规划的算法。首先,本文提出一种基于自适应目标引导的快速拓展随机树算法,以增加随机树生长的方向性,并通过转向和重选策略减少无效拓展加快算法的收敛速度。接着,获得全局路径之后使用自适应子节点选取策略获取动态窗口法的子目标点,将复杂的全局动态任务规划分解为多个简单的动态路劲规划,从而防止动态窗口法陷入局部极小值。最后,通过UUV出港任务仿真实验验证了算法的有效性和实用性。

地方本科高校葡萄与葡萄酒工程专业服务乡村振兴路径探析——以楚雄师范学院为例

实施乡村振兴战略是党的十九大作出的重大决策部署,是实现中华民族伟大复兴的必由之路。地方本科高校作为重要的人才、科技高地,在服务乡村振兴战略中具有重要的作用。为了解决地方本科高校服务乡村振兴战略实施路径问题,本文在分析当前地方本科高校服务乡村振兴战略中存在的问题基础上,以楚雄师范学院葡萄与葡萄酒工程专业为例,对相关实践经验和特点进行了归纳总结,以期为同类院校服务乡村振兴提供路径借鉴。

地方工科高校一流本科教育的价值蕴涵与实践路径

地方高校办学规模大,与经济社会发展融合较为紧密。进入新时代,地方工科高校应主动适应经济社会发展需要,办好一流本科教育,助力高等教育强国建设。基于此,文章剖析了地方工科高校一流本科教育建设中暴露出的若干问题,在此基础上,以南京工业大学为例,从落实立德树人根本任务、提高专业建设水平、提升教师教学能力、推进课堂教学改革、施行教育“质量革命”、推动“互联网+”教育等方面探析了推进地方工科高校一流本科教育改革的路径与举措。

基于人工磁场的防疫机器人路径规划

针对传统人工势场法在防疫机器人路径规划过程中容易陷入局部极小值和目标不可达的问题,提出一种基于人工磁场的路径规划算法。首先,对障碍物周围建立人工磁场,引入洛伦兹力,洛伦兹力与引力保持垂直,避免机器人陷入局部极小值点;其次,在洛伦兹力函数中引入机器人与目标点的距离影响因子,解决目标不可达问题;再次,通过建立目标点与障碍物之间的虚拟直线优化洛伦兹力的方向,避免迂回路径,减少路径规划的步数。最后,在MATLAB环境下对传统人工势场法和人工磁场法进行了仿真实验,实验结果表明,人工磁场法可以克服局部极小值和目标不可达问题,规划的路径对障碍物进行了避障,避免了徘徊振荡的情况出现,提高了路径规划的质量。

智能建造机器人局部路径规划研究综述

智能建造机器人作为落实建筑业智能化转型的关键设备,其自主施工能力代表着智能建造的水平,而局部路径规划是机器人高效施工的关键技术。针对建筑施工现场的环境特点,探究了智能建造机器人局部路径规划的难点。首先分析阐述了人工势场、时间弹性带、Bug、动态窗口等经典局部路径规划算法;其次对强化学习、深度强化学习、模糊控制、群智能等人工智能的局部路径规划算法进行了归纳总结;最后分析了各种方法在施工现场环境中智能建造机器人应用的局限性,讨论了智能建造机器人局部路径规划算法的发展趋势,旨在为复杂施工现场智能建造机器人局部路径规划研究提供一定的思路和建议,提高机器人的自主施工能力,促进智能建造技术的发展。

本土化:我国职业教育现代化的困境、特征与路径

近代以来,我国职业教育现代化的自然进程受到“西方中心主义”的阻滞,新中国成立之后职业教育现代化开始从“被动现代化”转向“自觉现代化”,但仍呈现出本土基因遮蔽、本土话语权减弱、创新乏力等困境。为此,职业教育应回归本土传统、唤醒本土基因,凸显民族特征、重振本土话语权,搭实职业教育的本土理论架构,创新服务现代产业发展实践,焕发本土创新力。

基于Frenet坐标系的路径规划仿真

在传统路径规划领域中,由于使用笛卡尔坐标系,存在着定义具体行驶多远,是否偏离中心等问题。基于此提出一种Frenet坐标系取代原有传统笛卡尔坐标系,对参考路径局部轨迹进行优化,在满足设置的如安全距离、速度、加速度等要求下,产生安全可靠的路径轨迹规避障碍物到达目标点,以提高规划的效率与准确性。最后通过pycharm软件仿真,调整合适的惩罚项和损失函数系数,使得路径规划耗时较少,横向加速度和横向偏移趋于稳定,产生更为合理的规划轨迹。通过采取Frenet坐标系,保证在车辆能够以安全,平稳,可靠,舒适的轨迹躲避障碍物,满足在路径规划中的实际需求。