基于多目标快速探索随机树的移动机器人巡检路径优化方法

针对移动机器人需要访问多目标的巡检路径规划问题,该文提出一种多目标快速探索随机树路径优化方法。首先,根据提供的环境地图与巡检目标点,该文采用一种 RRT-Connect-ACO 算法得到目标点的巡检顺序和可行路径;然后,通过引入信息子集,对路径进行优化,得到最终的最优路径。实验结果表明,与现有的多目标路径规划算法相比,该方法考虑了地形的影响,得到的最优路径更符合实际情况。     

VARIABLE PATROL PLANNING OF MULTI-ROBOT SYSTEMS BY A COOPERATIVE AUCTION SYSTEM

A cooperative auction system (CAS) is proposed to solve the large-scale multi-robot patrol planning problem. Each robot picks its own patrol points via the cooperative auction system and the system continuously re-auctions, based on the team work performance. The proposed method not only works in static environments but also considers variable path planning when the number of mobile robots increases or decreases during patrol. From the results of the simulation, the proposed approach demonstrates decreased time complexity, a lower routing path cost, improved balance of workload among robots, and the potential to scale to a large number of robots and is adaptive to environmental perturbations when the number of robots changes during patrol.     

空地异构机器人系统协作巡逻路径规划方法EI北大核心CSCD

空地异构机器人系统由空中无人机和地面无人车组成,当两者协作执行持续巡逻任务时,使用无人车充当无人机的地面移动补给站能够解决无人机续航能力不足的问题.运动受限于路网中的无人车必须在适当地点为无人机补充能量,这使得两者的路径高度耦合,给空地协作路径规划带来了挑战.针对此问题,本文通过分析无人机能量、路网、空地汇合时间、巡逻任务全覆盖等多种约束,以无人机完成全部巡逻任务的总距离为代价,建立了空地协作巡逻路径规划模型.该模型可推广至多架无人机与多辆无人车协作的情形.然后,采用遗传算法与蚁群算法相融合的方法,对无人机巡逻路径和无人车能量补给路径进行优化求解.仿真实验表明,本文的方法不仅可以得到很好的路径规划结果,而且较其他算法具有更优的收敛性和执行速度.     

Multi-robot area patrol under frequency constraints

Patrolling involves generating patrol paths for mobile robots such that every point on the paths is repeatedly covered. This paper focuses on patrolling in closed areas, where every point in the area is to be visited repeatedly by one or more robots. Previous work has often examined paths that allow for repeated coverage, but ignored the frequency in which points in the area are visited. In contrast, we first present formal frequency-based optimization criteria used for evaluation of patrol algorithms. Then, we present a patrol algorithm that guarantees maximal uniform frequency, i.e., each point in the target area is covered at the same optimal frequency. This solution is based on finding a circular path that visits all points in the area, while taking into account terrain directionality and velocity constraints. Robots are positioned uniformly along this path in minimal time, using a second algorithm. Moreover, the solution is guaranteed to be robust in the sense that uniform frequency of the patrol is achieved as long as at least one robot works properly. We then present a set of algorithms for handling events along the patrol path. The algorithms differ in the way they handle the event, as a function of the time constraints for handling them. However, all the algorithms handle events while maintaining the patrol path, and minimizing the disturbance to the system.     

考虑基站选址的UAV交通巡视路径超级时空网络模型

针对考虑基站选址的无人机交通巡视路径优化问题,提出一个超级网络与时空网络相结合的方法,并通过该方法建立模型。通过在时空网络添加虚拟起降点,与全部时刻的备选基站相连接构成超级时空网络,可将考虑基站选址的路径规划转换为一个单纯的多UAV路径规划问题。与不考虑基站选址的路径规划相比,考虑基站选址能够使最大单机飞行时间和总飞行时间分别减少5.71%和11.59%。数值分析表明,基站选址和交通巡视路径规划整合可有效减少UVA的巡视成本。     

基于最大空闲时间的分布式巡逻机器人数量优化

针对多机器人巡逻问题,提出基于全局平均最大空闲时间的分布式巡逻算法,确保每个巡逻顶点在一定时间内被机器人访问.在算法执行过程中,每个机器人利用共享信息估算全局平均最大空闲时间.机器人在当前顶点收集的信息用于决策、选择下一个被机器人访问的顶点.再根据全局平均最大空闲时间的大小估算在巡逻任务过程中多机器人团队的表现,确定完成此巡逻任务所需的最优机器人数量.仿真实验表明文中算法的收敛速度较快,全局平均最大空闲时间值较小,多机器人巡逻任务完成效果较佳.     

Adaptive continuous‐space informative path planning for online environmental monitoring

Autonomous mobile robots are increasingly employed to take measurements for environmental monitoring, but planning informative, measurement‐rich paths through large three‐dimensional environments is still challenging. Designing such paths, known as the informative path planning (IPP) problem, has been shown to be NP‐hard. Existing algorithms focus on providing guarantees on suboptimal solutions, but do not scale well to large problems. In this paper, we introduce a novel IPP algorithm that uses an evolutionary strategy to optimize a parameterized path in continuous space, which is subject to various constraints regarding path budgets and motion capabilities of an autonomous mobile robot. Moreover, we introduce a replanning scheme to adapt the planned paths according to the measurements taken in situ during data collection. When compared to two state‐of‐the‐art solutions, our method provides competitive results at significantly lower computation times and memory requirements. The proposed replanning scheme enables to build models with up to 25% lower uncertainty within an initially unknown area of interest. Besides presenting theoretical results, we tailored the proposed algorithms for data collection using an autonomous surface vessel for an ecological study, during which the method was validated through three field deployments on Lake Zurich, Switzerland. Spatiotemporal variations are shown over a period of three months and in an area of 350 m × 350 m × 13 m. Whereas our theoretical solution can be applied to multiple applications, our field results specifically highlight the effectiveness of our planner for monitoring toxic microorganisms in a pre‐alpine lake, and for identifying hot‐spots within their distribution.     

Optimized design of patrol path for offshore wind farms based on genetic algorithm and particle swarm optimization with traveling salesman problem

With the rapid expansion of global offshore wind power market, the research on improving the full life cycle income and reducing the construction and operation and maintenance costs has attracted the attention of scholars in the industry. In view of the different aging degree and maintenance cycle of wind turbines, this paper studies the optimized design of patrol path for offshore wind farms based on genetic algorithm (GA) and particle swarm optimization (PSO) with traveling salesman problem (TSP). Firstly, the problem of patrol routing planning in offshore wind farms is described as the traveling salesman problem of shortest route optimization. Secondly, the GA and PSO algorithms are simulated and verified separately, and the patrol path distance is taken as the objective function. Finally, through simulation experiments, the optimized patrol path performances of PSO and GA are compared, which can help to find a shortest route and reduce the operation and maintenance costs.     

基于5G通信技术的巡逻机器人定位误差自动补偿方法

以提升巡逻机器人执行巡逻任务能力为目的,提出基于5G通信技术的巡逻机器人定位误差自动补偿方法。该方法在巡逻机器人工作区域架设5G无线通信网络,并将AD7380型号位置传感器安装在巡逻机器人上,利用位置传感器获取巡逻机器人工作时的位置信息后,利用5G无线通信网络将其传输到用户PC端,得到巡逻机器人位置采样点数据；以该数据为基础,使用拉丁超立方采样方法描述巡逻机器人在其巡逻空间内的位置,得到巡逻机器人空间位置数据；再依据巡逻机器人空间位置数据,计算该机器人预设目标点误差矢量,并构建巡逻机器人定位误差补偿模型,利用该模型补偿巡逻机器人定位误差。实验结果表明：该方法可全面获取巡逻机器人在其巡逻空间内的位置信息,可有效且精准的对其定位误差进行自动补偿,使巡逻机器人定位误差保持在可允许范围内,应用效果较为显著。     

Real-Time Path Planning Algorithm for Autonomous Border Patrol: Design,Simulation, and Experimentation

This paper presents an online path planning algorithm for unmanned vehicles in charge of autonomous border patrol. In this Pursuit-Evasion game, the unmanned vehicle, acting as the pursuer, is required to capture multiple trespassers on its own before any of them reach a target safe house where they are safe from capture. The problem formulation is based on Isaacs’ Target Guarding problem, but extended to the case of multiple evaders. The proposed path planning method is based on Rapidly-exploring random trees (RRT) and is capable of producing trajectories within seconds to capture 2 or 3 evaders. Simulations are carried out to demonstrate that the resulting trajectories approach the optimal solution produced by a nonlinear programming-based numerical optimal control solver. Experiments are also conducted on unmanned ground vehicles to show the feasibility of implementing the proposed online path planning algorithm on physical applications.     

“数字城管”的在线式电子巡查管理系统

离线式巡查管理系统通过对历史记录数据的分析与处理实现了对巡查人员的有效监管,但由于记录数据的分析存在滞后性,从而缺乏应对突发事件的能力和灵活实时的人员调度能力。本文基于城市巡查管理中已经具备的无线对讲系统,结合GIS、GPS和RFID技术,设计了面向“数字城管”的在线式电子巡查管理系统,实现了巡查装备的集成化、巡查管理的实时化与可视化。     

基于最近邻法的电力电缆巡线路径的优化

电缆线路的分布主要在地下、管道甚至海底,在检修与维护方面会面临巡视周期较长的问题。为了缩短电力电缆巡线路径的距离,压缩无效时长,提高供电服务效率,本文先分析电力电缆巡线路径的当前现状,再针对电缆中间接头巡线车辆的路径问题,采取启发式算法中的最近邻法对其进行研究,该方法简单实用有效。与现行的逐条线路巡视路径相比,最近邻算法可以缩短车辆路径的距离,减少巡线路程间无效时间。本文以舟山市供电有限公司客服中心的电力电缆巡线路径为研究平台,验证了最近邻法在不给作业人员增加额外负担的前提下实现路径优化的有效性,提高了工作效率,进而提高了业务承载力、压缩了业扩时长。     

BP神经网络在巡逻机器人多传感器数据融合中的应用

针对传统危化品仓库固定式监控器中监控范围小、报警准确率低的特点,研究了一款危化品仓库巡逻机器人,采用以拉依达准则改善BP神经网络融合性能的多传感器数据融合方法,通过采集泄露危化品浓度、仓库内环境温度和湿度等数据,在对数据进行拉依达去噪、归一化后利用BP神经网络进行融合输出.样机试验结果表明,该方法可有效提升危化品仓库巡逻机器人对空间环境的把握度,大幅度提高报警的准确性和可靠性,同时具备良好的传感器扩展性.     

WSN巡逻式强栅栏调度算法研究

无线传感器网络栅栏覆盖研究已取得丰厚成果.提出一种巡逻式栅栏调度算法,该算法将栅栏均匀分为若干子栅栏,依次轮流激活子栅栏,激活的子栅栏工作一段时间后进入休眠状态,然后下一条子栅栏由休眠进入激活状态.因此子栅栏的轮流激活形成了栅栏的巡逻状态,当入侵者穿越栅栏时有很大概率会被巡逻子栅栏检测到.在确保栅栏检测率的情况下使得栅栏的生存时间得到大幅度延长.文中计算了在理论情况下的栅栏检测率,最后通过实验验证了该算法的准确性和可靠性.     

多自主船协同路径跟踪的自适应动态面控制

为实现多自主船含模型不确定与未知风浪流干扰下的协同路径跟踪控制,提出了一种基于神经网络自适应动态面控制的协同路径跟踪算法.该算法采用单隐层(SHL)神经网络逼近模型不确定性以及海洋环境干扰,所引入的动态面设计技术显著降低了控制算法的复杂性.同时将网络通信约束考虑在内,通过设计分散式协同控制律有效地降低了信息通讯量.Lyapunov稳定性分析证明了闭环系统所有的状态和信号是有界的,并且通过选择合适的设计参数可使跟踪误差为任意小.对比仿真结果验证了所提方法的有效性.     

基于知识的线路巡检计划层次推理技术研究

提出了一种基于知识规则的巡检计划生成方法。利用知识系统原理，对巡检专家知识进行分层描述，建立巡检知识库；基f巡检知识的层次描述，提出巡检计划的层次推理疗法；在层次推理机制的控制下，使用巡检知识对巡检历史数据进行动态解释，形成巡检决策，并自动生成巡检计划。该方法在实际中得到了应用，并取得了较好的效果。     

An exponentially convergent adaptive sliding mode control of robot manipulators

A new robust adaptive algorithm for control of robot manipulators is proposed to account for a desired transient response with global exponential convergence of tracking errors without any persistent excitating assumption on the regressor. Its novelty lies in a new dynamic sliding surface that allows a systematic combination of adaptive control and variable structure control to yield a sliding mode inside an adaptive control loop. During sliding mode, parameter uncertainty appears in terms of known variables in such a manner that a new robust parameter estimator with enhanced stability properties is established. On the other hand, if the regressor meets the persistent exciting condition, the global uniform exponential stability of the equilibrium concerning the adaptive closed-loop error equation is easily established. The proposed controller from the VSS viewpoint relaxes the longstanding condition on a priori knowledge of the size of the parametric uncertainty to induce a sliding mode. On the other hand, from the adaptive control viewpoint it relaxes the standard assumption of the persistent excitation on the regressor to obtain the exponential convergence of tracking errors. Also, the stability against time-varying parameters is briefly discussed. Concluding remarks concerning its structural behaviour are given, and computer simulation data show a robust performance.     

一种基于离散时间一致性理论的多机器人分布式巡逻算法

在多机器人巡逻任务中,由于通信距离的限制,单个机器人很难获得全局信息。然而,现有的大多数多机器人分布式巡逻算法都要求每个机器人获得其巡逻区域的全局信息进行决策。因此,考虑到通信半径约束和局部信息约束,为了通过相邻机器人之间的交互完成巡逻任务,基于离散时间一致性理论提出了两种巡逻算法。算法1使用全局信息进行决策,算法2基于离散时间一致性理论实现局部信息对全局信息的预测进行决策。通过模拟器Stage对所提算法与对比算法在不同机器人数量、通信半径、地图环境下进行了对比。实验验证了所提出的基于局部信息的分布式多机器人巡逻算法具有与原算法类似的特性和性能,能够使机器人在没有全局信息的情况下判断全局状态,并基于邻居之间的协商完成巡逻任务。     

Robust analysis and synthesis for a class of uncertain nonlinear systems with multiple equilibria

This paper focuses on a class of uncertain nonlinear systems which are subject to norm-bounded parameter uncertainty in the forward path and a vector-valued periodic nonlinearity in the feedback path, and addresses robust analysis and synthesis problems for such systems. Sufficient conditions for global asymptotic stability are derived in terms of linear matrix inequalities (LMIs) and a technique for the estimation of the uncertainty bound is proposed by solving a generalized eigenvalue minimization problem. The problem of robust synthesis is concerned with designing a feedback controller such that the resulting closed-loop system is globally asymptotically stable for all admissible uncertainties. It is shown that a solution to the robust synthesis problem for the uncertain system can be obtained by solving a synthesis problem for an uncertainty free system. A concrete example is presented to demonstrate the applicability and validity of the proposed approach.     

Optimal route selection based on Monte Carlo method and adaptive amoeba algorithm under uncertain environment

The fuzzy optimal path under uncertainty is one of the basic network optimization problems. Considering the uncertain environment, many fuzzy numbers are used to represent the edge weights, such as interval number and triangular fuzzy number. Then, these fuzzy numbers are converted to real numbers directly. This converting makes the optimal path the shortest path selection problem. However, much information of uncertainty get lost when converting fuzzy numbers to real numbers. In order to ensure all the origan data complete, in this paper, a fuzzy optimal path solving model based on the Monte Carlo method and adaptive amoeba algorithm is proposed. In Monte Carlo process, a random number which belongs to the fuzzy number is generated. Then, Physarum polycephalum algorithm is used to solve the shortest path every time and record the result. After many times calculation, many shortest paths have been found and recorded. At last, by analysing the characters of all the results, the optimal path can be selected. Several numerical examples are given to illustrate the effectiveness of the proposed method, the results show that the proposed method can deal with the fuzzy optimal path problems effectively.