Role Dynamic Allocation of Human-Robot Cooperation Based on Reinforcement Learning in an Installation of Curtain Wall

A real-time adaptive roles allocation method based on reinforcement learning is proposed to improve humanrobot cooperation performance for a curtain wall installation task.This method breaks the traditional idea that the robot is regarded as the follower or only adjusts the leader and the follower in cooperation.In this paper,a self-learning method is proposed which can dynamically adapt and continuously adjust the initiative weight of the robot according to the change of the task.Firstly,the physical human-robot cooperation model,including the role factor is built.Then,a reinforcement learningmodel that can adjust the role factor in real time is established,and a reward and actionmodel is designed.The role factor can be adjusted continuously according to the comprehensive performance of the human-robot interaction force and the robot’s Jerk during the repeated installation.Finally,the roles adjustment rule established above continuously improves the comprehensive performance.Experiments of the dynamic roles allocation and the effect of the performance weighting coefficient on the result have been verified.The results show that the proposed method can realize the role adaptation and achieve the dual optimization goal of reducing the sum of the cooperator force and the robot’s Jerk.

An Optimal Deep Learning for Cooperative Intelligent Transportation System

Cooperative Intelligent Transport System(C-ITS)plays a vital role in the future road traffic management system.A vital element of C-ITS comprises vehicles,road side units,and traffic command centers,which produce a massive quantity of data comprising both mobility and service-related data.For the extraction of meaningful and related details out of the generated data,data science acts as an essential part of the upcoming C-ITS applications.At the same time,prediction of short-term traffic flow is highly essential to manage the traffic accurately.Due to the rapid increase in the amount of traffic data,deep learning(DL)models are widely employed,which uses a non-parametric approach for dealing with traffic flow forecasting.This paper focuses on the design of intelligent deep learning based short-termtraffic flow prediction(IDL-STFLP)model for C-ITS that assists the people in various ways,namely optimization of signal timing by traffic signal controllers,travelers being able to adapt and alter their routes,and so on.The presented IDLSTFLP model operates on two main stages namely vehicle counting and traffic flow prediction.The IDL-STFLP model employs the Fully Convolutional Redundant Counting(FCRC)based vehicle count process.In addition,deep belief network(DBN)model is applied for the prediction of short-term traffic flow.To further improve the performance of the DBN in traffic flow prediction,it will be optimized by Quantum-behaved bat algorithm(QBA)which optimizes the tunable parameters of DBN.Experimental results based on benchmark dataset show that the presented method can count vehicles and predict traffic flowin real-time with amaximumperformance under dissimilar environmental situations.

Regional container transportation system for Northeast Asia economic cooperation in the 21^(st) century

In this paper, the roles of infrastructure development and transportation coordination for Northeast Asian economic cooperation are discussed. It would be necessary to establish an efficient transportation network as soon as possible. 'Hub-and-Spoke'transportation system and China-Korean peninsula railway container transportation system might be more significant for regional economic cooperation.

ComplexTrans-Global Rail-Road Transportation System Economical and Clustered Individual and Individualised Public Transport also Prevents the Spread of Covid 19

Land transport can no longer meet the requirements.European transport can be described by these words−crowded motorways and cities,dangerous emissions,ubiquitous traffic accidents,delays,expensive railways.Solutions are being sought to transfer a large part of passengers and especially freight transport to(high-speed)rail,and efforts are moving towards electromobility,car-sharing,5G-connectivity,autonomous driving,MaaS(Mobility as a Service)-coordinated transport or hyperloop-type solutions.However,all these solutions have additional challenges and limitations.Solutions are not being searched where they really exist-in the mutual adaptation of road and rail vehicles and their deep cooperation.The ComplexTrans project shows that simply adapting the dimensions and functions of road and rail vehicles can eliminate(or at least significantly reduce)all the problems of existing land transport.The main features of the ComplexTrans system are sufficient parking spaces,reduction of urban and non-urban congestion,electric vehicles with unlimited range and cheaper than standard cars,cheaper and more accessible battery charging,“autonomous ride”,solving the overlap between passenger and freight rail transport and making it self-financing,transferring intercity freight transport to rail,replacing part of continental air transport and many others.The cost-effective and clustered individual transport and individualised public transport of the ComplexTrans system also bring very significant reductions in the risk of transmission of covid-19 and other contagious diseases during transport.

Enhancing the terrain adaptability of a multirobot cooperative transportation system via novel connectors and optimized cooperative strategies

Given limited terrain adaptability,most existing multirobot cooperative transportation systems(MRCTSs)mainly work on flat pavements,restricting their outdoor applications.The connectors'finite deformation capability and the control strategies'limitations are primarily responsible for this phenomenon.This study proposes a novel MRCTS based on tracked mobile robots(TMRs)to improve terrain adaptability and expand the application scenarios of MRCTSs.In structure design,we develop a novel 6-degree-of-freedom passive adaptive connector to link multiple TMRs and the transported object(the communal payload).In addition,the connector is set with sensors to measure the position and orientation of the robot with respect to the object for feedback control.In the control strategy,we present a virtual leader-physical follower collaborative paradigm.The leader robot is imaginary to describe the movement of the entire system and manage the follower robots.All the TMRs in the system act as follower robots to transport the object cooperatively.Having divided the whole control structure into the leader robot level and the follower robot level,we convert the motion control of the two kinds of robots to trajectory tracking control problems and propose a novel double closed-loop kinematics control framework.Furthermore,a control law satisfying saturation constraints is derived to ensure transportation stability.An adaptive control algorithm processes the wheelbase uncertainty of the TMR.Finally,we develop a prototype of the TMR-based MRCTS for experiments.In the trajectory tracking experiment,the developed MRCTS with the proposed control scheme can converge to the reference trajectory in the presence of initial tracking errors in a finite time.In the outdoor experiment,the proposed MRCTS consisting of four TMRs can successfully transport a payload weighing 60 kg on an uneven road with the single TMR's maximum load limited to 15 kg.The experimental results demonstrate the effectiveness of the structural design and control strategies of the TMR-based MRCTS.

Cooperative Game Theory Based Coordinated Scheduling of Two-Machine Flow-Shop and Transportation

A cooperative game theoretical approach is taken to production and transportation coordinated scheduling problems of two-machine flow-shop(TFS-PTCS problems)with an interstage transporter.The authors assume that there is an initial scheduling order for processing jobs on the machines.The cooperative sequencing game models associated with TFS-PTCS problems are established with jobs as players and the maximal cost savings of a coalition as its value.The properties of cooperative games under two different types of admissible rearrangements are analysed.For TFS-PTCS problems with identical processing time,it is proved that,the corresponding games areσ_(0)-component additive and convex under one admissible rearrangement.The Shapley value gives a core allocation,and is provided in a computable form.Under the other admissible rearrangement,the games neither need to beσ_(0)-component additive nor convex,and an allocation rule of modified Shapley value is designed.The properties of the cooperative games are analysed by a counterexample for general problems.

基于装卸顺序的中型机多航段协同配载优化

多航段、多经停的航空货运航班,飞行过程中需要合理控制飞机的重心(CG)位置,保持平衡状态,中转站装卸操作过程中需要避免额外装卸操作,减少时间和劳力浪费,因此,研究多航段航班的协同配载问题具有重要现实意义。根据航空器自身特点与集装器(ULD)装卸顺序,结合各航段飞机装载平衡与过站机场ULD装卸操作要求,通过协调分配各航段ULD和散货位置,建立了中型机联程航班多目标整数线性规划模型。模型考虑了ULD和散货的质量、体积及与舱位的匹配限制,航空器限重、舱位限重、舱位及区域累积限重、上下舱联合限重和CG位置的平衡限制,以及前后航段ULD和散货的连接性约束。根据造成中间机场额外装卸操作的2种原因,基于装卸顺序,提出装卸优化模型,通过ULD的舱内平移,优化了CG,减少了装卸次数。以B757-200F机型为例,在两装一卸、一装两卸和两装两卸3种场景下,采用商用求解器Gurobi,分别针对3种不同目标函数组合进行求解和分析对比。实验表明:所提模型可以有效协调过站货物的额外装卸次数,优化前后两航段的CG位置。

澜湄合作国家跨境物流模式创新与发展趋势研究

全球经济一体化推动跨境物流高质量发展,澜湄合作国家间的贸易往来密切,跨境物流发展势头良好,跨境物流模式主要有邮政小包、国际快递、第三方物流、海外仓和保税区物流等,跨境物流存在物流成本高、基础设施不完善、通关效率低、互联互通不顺畅的问题。从澜湄合作国家间的跨境物流发展现状和存在的问题出发,提出在国际陆港—边境口岸联动模式的基础上丰富边境口岸的功能,增加运输方式转换的功能,使运输方式灵活转换;在运用跨境甩挂运输模式的基础上推动澜湄合作国家加入TIR公约,使国家间的公路运输更加便利。国际铁路多式联运是跨境物流未来的发展趋势,改革查验方式优化通关流程、建立云平台共享信息、建立标准化单证“一单制”可使多式联运更高效、更便捷。

项目驱动与合作学习主导的交通类专业教学模式研究

如今,我国的教育事业正面临着全面的发展及改革,在教学过程中,必须强调培养学生的学科理论知识及专业应用能力,强调理论与实践相结合的教学目标,加大学生的学科思维培养力度,并让教学从“教师教,学生学”转变为“教学生如何学”。基于这一背景,项目驱动教学法与合作学习教学法取得了不错的成果。文章讨论了以项目驱动教学法和合作学习教学法主导的交通类专业教学模式,致力于创新教学方法,强调培养高素质、专业化的应用型人才,以便为国家的建设及发展贡献力量。

智能农机多机协同收获作业控制方法与试验

为提高多台无人化智能收获机和运粮车协同作业效率,该研究以2台不同型号水稻收获机和1台运粮车为研究对象,开展了智能农机多机协同收获作业控制方法研究。根据协同作业控制决策约束条件,建立协同收获作业中有限个状态过程的改进型连续时间马尔科夫链模型。以减少非作业时间为优化目标,通过模型预测未来一段时间内每台收获机的卸粮时间,动态更新每台收获机的卸粮顺序和时间。仿真试验结果表明:该研究控制方法相对于仓满后再召唤运粮车的卸粮方式有效减少了作业时间,协同收获任务的农机平均作业时间减少了13.58%。田间试验结果表明:智能农机多机协同作业控制方法实现了2台水稻收获机和1台运粮车协同自主作业,在场景1中,相对于仓满召唤卸粮模式,收获机1和收获机2非作业时间分别减少了71.25%和42%,收获效率提高了6.65%和5.22%;在场景2中,相对于仓满召唤卸粮模式,收获机1和收获机2非作业时间分别减少了77.64%和37.09%,收获效率提高了12.07%和5.78%。该文提出的控制方法可以实现收获-卸粮转运自主作业,减少了收获机的非作业时间,提高了作业效率,可为无人农场智能收获协同作业提供支撑。

乡村振兴背景下生鲜农产品上行集货路径优化研究

农产品上行“最先一公里”的集货问题是乡村经济发展中的重要一环,集货的效率直接影响农产品上行运输的经济成本。针对偏远农户集货成本高、集货不便问题,设计考虑农户协作的运输模式,建立协作距离最小化模型,实现偏远农户的协作匹配。在此基础上,以运输成本、货损成本、协作成本最小化为目标,建立农产品上行集货路径优化模型,并设计改进的节约里程-禁忌搜索算法进行求解。最后,案例分析的结果表明,本文所提出的模型和算法是有效的。

考虑海运竞争影响的中欧班列运营商竞合关系研究

中欧班列(简称班列)市场竞争分为内部竞争与外部竞争,内部竞争来自班列运营商间的同质化竞争,外部竞争来自海运的差异化竞争。目前班列正陆续开展区域化合作探索。为探究在海运竞争影响下,班列运营商采取何种竞合策略效益更优,分别构建合作与非合作模式下,两班列运营商与海运班轮公司竞争的博弈模型。同时考虑班列运营商间的地理位置差异,采用Hotelling模型分析合作模式下两班列运营商的收益分配。选取已初步开展合作的班列运营商相关数据及海运运营数据进行实证分析。研究结果表明:1)从市场化运营发展来看,班列运营商开展合作模式可同时提高自身均衡运价及总市场份额,更有利于班列市场的扩张;班列运营商可在开展合作模式的同时适当提高运价,进一步开放高附加值货物市场,促进班列的高质量发展。2)班列运营商可以统一定价的形式开展合作,以运输成本及空间地理位置差异产生的费用为依据明确各自运货量,实现合作模式下班列运营商效益分配的初步探索。3)若给定班列实际运营参数,可根据非合作模式班列运营商竞争强度对总收益的影响确定阈值点,当实际竞争强度小于该阈值时,班列运营商选择合作模式更优;班列运输时效性的增加对合作模式下班列运营商总效益的影响更为显著,可作为合作模式发展的优化方向。研究可为班列运营商竞争与合作决策及班列高质量发展提供参考。

多移动机器人协作转运系统控制与试验

多机器人系统可使用多个功能相对简单的机器人执行较为复杂的任务。相比单一机器人运输,多机器人系统在超大、超重部件的转运上展现出更高的灵活性、可扩展性、多产品适应性等优势。提出一种多移动机器人协作转运系统,可实现多台移动机器人负载同一工件在无轨迹约束、无负载框架约束下的稳定运行。基于协作定位及领航-跟随编队控制方法,将有限的轮系驱动能力与纠偏需求相结合输出跟随单元控制量,可充分发挥全舵轮驱动移动机器人全向移动优势,保证速度指令有效并使位姿偏差减小并趋于稳定。考虑实际应用场景,在各机器人顶部设置全向力浮动机构以减小车组间相对位姿偏差对工件产生的外力影响。搭建由4台移动机器人组成的协作转运平台并进行试验,试验结果表明:在该控制方法下整个编队系统可稳定运行,直线、弧线轨迹下各从车相对位置偏差不大于40 mm,姿态偏差不大于6°。

煤矿掘进转载技术创新与实践

结合长距离带式输送机柔性运输技术与蛇形带式输送机,根据掘进工作面和短壁开采工作面的实际需求,设计了多胶轮移动式大曲率DZY100/160/135型柔性带式运输系统,包含了空间柔性运输整体结构、柔性单元耦合、带式运输自平衡、带型及成槽、短距离强压带多点驱动、协同控制等关键技术,实现采掘工作面柔性连续运输,有效解决高效运输难题。为短壁机械化开采探索了新的高效运输方案、提供了采掘运输智能化新的解决思路,经济和社会效益显著。

刮板输送机与采煤机协同调速技术研究

为实现采面煤炭高效开采及运输,确保刮板输送机与采煤机等协调、经济运行,综合采用S7-300PLC、传感器、Mesh网络以及WinCC构建刮板输送机和采煤机运行速度协同控制系统。对系统架构、传感器类型、Mesh网络布置以及WinCC平台控制方案等进行阐述并在12303综采工作面进行工程应用。结果表明,构建的运行速度协同控制系统可实现刮板输送机链速与采煤机牵引速度间协同,在满足煤炭高效生产的同时降低刮板输送机能耗;同时现场应用期间未出现刮板输送机堆煤情况,现场应用效果较为显著。

采面刮板输送机与采煤机协同控制技术研究

通过实时监测采煤机牵引速度、负载及刮板输送机负载等实现刮板输送机与采煤机协同控制,具体采煤机牵引速度快时且刮板输送机负载呈增加趋势时适当增大刮板输送机运行速度,反之则适当降低运行速度。对协同控制使用的硬件设备结构及控制方式等进行分析。现场应用后,刮板输送机运行速度可随采煤机牵引速度改变而改变,在一定程度提高了刮板输送机运行效率。

基于客流博弈均衡的旅客列车双层开行方案优化

为提高旅客列车运行效益及旅客出行满意度,以铁路运营部门净收益、列车空座位走行公里最小化为上层0-1规划模型,以旅客出行满意度最大化为下层客流均衡模型,构建了纯整数非线性高速铁路旅客列车开行方案双层规划模型。针对下层客流均衡模型,通过划分博弈环境和分析旅客乘车出行选择行为,选取旅客同质群体作为局中人,构造各局中人合理赢得函数,对每个博弈环境均运用非合作博弈理论进行客流均衡分配。基于构建的模型特征分析,采用NSGA-Ⅱ算法求解上层停站方案模型,运用QPSO算法求解下层客流博弈均衡模型,并以兰新高速铁路为例验证了模型及算法的可行性。结果表明:该算法可获得使铁路运营部门与旅客双方均满意的Nash均衡解,由此得到的开行方案铁路旅客损失率为0%。

跟车车辆交通信息提示装置设计与实现

跟车行驶是一种常见的驾驶状态,如何有效提升跟车车辆的行驶安全,减少交通拥堵,提高交通效率,已成为智能交通系统研究的重要课题之一。跟车事故是现代社会中严重威胁人们生命和财产安全的交通问题。为了解决这个问题,本论文设计了一种跟车车辆交通信息提示装置。该装置能够实时获取车辆的运行状态、行驶环境和交通信息,根据预设的算法对这些信息进行分析和处理,并向驾驶员提供及时、准确的提示,从而提高驾驶员对路况的感知能力和驾驶安全性。该装置采用传感器、通信技术等技术手段,并结合车联网,能够有效降低跟车事故的发生风险,提高道路安全性。实验结果表明,该装置能够有效地提高驾驶员对路况的感知能力和驾驶安全性。本论文的研究为智能交通系统中的车辆信息交流与处理提供了有益的参考与实践案例,对于提升交通运行效率、确保驾驶安全等方面具有重要意义。

Exploration and Cooperation Robotics on the Moon

Space robotics are the development of general purpose machines that is capable of surviving (for a time, at least) in the rigors of the space environment, and performing exploration, assembly, construction, maintenance, servicing. Space Robots can perform tasks less expensively or on an accelerated schedule, with less risk and occasionally with improved performance while humans doing the same tasks. The moon is the natural next step in the exploration of our own universe. Understanding moon better will help us understand our neighbors in the solar system. In this paper, a concept of exploration and cooperation robotics on the moon is discussed. The concept requires not only to extend the exploration mission on the moon surface but also to address a way to integrate the developed robotics with each other. Sharing the information between robots is one of a concept’s features to reduce lime and power consumption in the exploration process. Moreover, several challenges are discussed here, which prevent the concept from developing in outer space or on moon.