多机器人覆盖技术研究进展

系统地总结了当前覆盖问题的定义、分类和应用前景.对多机器人覆盖中关于通信、环境地图、路径规划算法及效果评价等方面的研究进展情况进行了阐述.分析并指出若干多机器人覆盖研究中的重点和难点问题:体系结构、通信技术、协商协作、地图表示、路径规划及效果评价,并对未来的研究发展方向进行了探讨.     

多移动机器人区域分割优化覆盖算法研究

针对多机器人协作的全覆盖路径规划优化问题,提出了一种区域分割优化覆盖算法,完成多机器人对未知区域的快速、高效的覆盖任务.在移动机器人全覆盖路径规划研究中,将机器人的能量、速度、任务完成时间等作为限制条件,对所需机器人的数目、机器人分成的组数及机器人工作的起始位置进行规划,以实现利用最少数目的机器人完成给定区域的优化分割和覆盖.对比仿真实验,结果表明本方法在给定机器人数量的情况下,完成了最大区域的覆盖.该方法可以有效完成多机器人对未知区域的快速高效的搜索覆盖任务.     

吸尘机器人的现状及其智能系统的若干关键技术

吸尘机器人能实现室内环境全自动清洁,替代了传统的人工清洁,具有十分广阔的市场前景。介绍了国内外吸尘机器人产品现状;综合评述了其智能系统中的全区域覆盖、路径规划、环境建模及传感器等的研究现状,对关键技术的难点进行了说明,并对其发展前景做了展望。     

移动机器人全覆盖信度函数路径规划算法

针对移动机器人全覆盖路径规划问题,给出一种基于栅格信度函数的全覆盖路径规划算法。目的是为了控制移动机器人能够遍历工作区域中所有的可到达点,同时保证能够自动避开障碍物。首先,根据环境的信息对栅格地图进行赋值,使用不同的函数值表示障碍物、已覆盖栅格和未覆盖栅格;其次,判断机器人是否陷入死区引入不同方向信度函数,对栅格函数值进行调整;最后,机器人根据栅格信度函数值规划覆盖路径。本文所提及的算法不仅能够引导移动机器人实现工作区域的全覆盖而且能够快速逃离死区,实现覆盖路径的低重复率。仿真实验中,通过与生物启发神经网络算法的比较,证明本文提及算法有更高的覆盖效率。     

基于遗传算法的AS-R移动机器人路径规划研究

将遗传算法应用于机器人的全局路径规划,并在AS-R移动机器人上进行了实验研究.首先用栅格法对机器人的工作环境进行划分,得到机器人的环境模型;其次采用遗传算法进行路径搜索,并引入多种遗传算子,增强了算法的实用性.在AS-R机器人上采用VC开发,并在5×5的栅格环境中进行路径规划研究.实验结果表明,遗传算法结合栅格环境对移动机器人进行路径规划,具有简单且通用的效果,所得的折线路径也更适合于机器人进行轨迹跟踪.     

基于置信度上界的移动机器人信息路径规划方法

路径规划是移动机器人未知环境探索的关键问题,路径点的合理规划对提高环境探索的效率和环境场预测的准确性至关重要.基于强化学习范式,提出一种适用于静态环境场探索的移动机器人在线信息路径规划方法.针对基于模型训练算法计算成本高的问题,通过机器人与环境的交互作用,采用动作价值评估的方法来学习所获取的环境场历史信息,提高机器人实时规划能力.为了提高环境预测准确性,引入基于置信度上界的动作选择方法来平衡探索未知区域与利用已有信息,鼓励机器人向更多未知区域进行全场特征探索,同时避免因探索区域有限而陷入局部极值.仿真实验中,环境场分别采用高斯分布和Ackley函数模型.结果表明,所提算法能够实现机器人环境探索路径点的在线决策,准确有效地捕捉全场和局部环境特征.     

面向荒漠复杂地形的机器人在线全覆盖路径规划方法

对于地形复杂、范围广阔的荒漠环境,当前的地图模型存在占用存储空间过大的问题;同时在复杂地形下,当前的全覆盖路径规划算法能量消耗大,无法适用于在线条件.对此,提出一种在线的全覆盖路径规划算法及相应的地图模型.首先,介绍一种变分辨率的三维栅格地图模型.其次,分析机器人在非平面环境下进行全覆盖任务的能量消耗问题,得出最节约能量的覆盖方式.在对平坦地形的覆盖中,基于优先级覆盖的思想,对传统的牛耕法覆盖做出改进,拓展为8个方向.然后,针对非平坦地形提出一种在线的面向地形的区域分解方法,在全覆盖过程中根据高度将特殊地形区域分解出来单独覆盖.在子区域内部,对特殊地形抽象得到斜面模型,引入地形变化函数,形成新的优先级遍历方法,并设计一种针对性的避障策略以进一步减少能量消耗.最后,对所提出的算法进行仿真验证以及机器人实验.仿真验证和实验结果表明,相比于其他算法,所提出算法能显著减少全覆盖过程中的重复率以及机器人总能量消耗.     

基于栅格地图的机器人覆盖路径规划研究*

研究了基于接触传感器的机器人覆盖问题,提出了基于栅格地图的内螺旋覆盖(ISC)算法.ISC算法通过边界探索获得环境边界地图之后,在线规划覆盖路径,用距离转变的搜索方法保证了完全覆盖,通过设置gate栅格降低了重复覆盖率.通过对三个房间组成的室内环境的覆盖仿真试验验证了该方法的可行性.     

动态未知环境中移动机器人的滚动路径规划及安全性分析

借鉴预测控制滚动优化原理,研究了全局环境未知且存在动态障碍物情况下的机器人路径规划问题.提出的基于滚动窗口的移动机器人路径规划方法充分利用机器人实时测得的局部环境信息,以滚动方式进行在线规划,合理结合了优化与反馈,对动态环境具有良好的适应性.还对规划算法的安全性进行了分析.     

一类动态不确定环境下机器人的滚动路径规划

研究了一类全局环境未知且存在的动态障碍物情况下的机器人路径规划问题.借鉴预测 控制滚动优化原理,给出了基于滚动窗口的移动机器人路径规划方法.充分利用机器人实时测 得的局部环境信息,实施在线滚动规划,把优化与反馈机制合理结合起来,对动态不确定环境具 有良好的适应性.同时还对滚动规划算法的安全和可达性进行了分析.     

Dynamic Frontier-Led Swarming: Multi-Robot Repeated Coverage in Dynamic Environments

A common assumption of coverage path planning research is a static environment.Such environments require only a single visit to each area to achieve coverage.However,some real-world environments are characterised by the presence of unexpected,dynamic obstacles.They require areas to be revisited periodically to maintain an accurate coverage map,as well as reactive obstacle avoidance.This paper proposes a novel swarmbased control algorithm for multi-robot exploration and repeated coverage in envir...     

Multisensor online 3D view planning for autonomous underwater exploration

This study presents a novel octree‐based three‐dimensional (3D) exploration and coverage method for autonomous underwater vehicles (AUVs). Robotic exploration can be defined as the task of obtaining a full map of an unknown environment with a robotic system, achieving full coverage of the area of interest with data from a particular sensor or set of sensors. While most robotic exploration algorithms consider only occupancy data, typically acquired by a range sensor, our approach also takes into account optical coverage, so the environment is discovered with occupancy and optical data of all discovered surfaces in a single exploration mission. In the context of underwater robotics, this capability is of particular interest, since it allows one to obtain better data while reducing operational costs and time. This study expands our previous study in 3D underwater exploration, which was demonstrated through simulation, presenting improvements in the view planning (VP) algorithm and field validation. Our proposal combines VP with frontier‐based (FB) methods, and remains light on computations even for 3D environments thanks to the use of the octree data structure. Finally, this study also presents extensive field evaluation and validation using the Girona 500 AUV. In this regard, the algorithm has been tested in different scenarios, such as a harbor structure, a breakwater structure, and an underwater boulder.     

An exploration strategy using sonar sensors in corridor environments

We present a novel solution for topological exploration in corridor environments using cheap and error-prone sonar sensors. Topological exploration requires significant location detection and motion planning. To detect nodes (i.e., significant places) robustly, we propose a new measure, the eigenvalue ratio (EVR), which converts geometrical shapes in the environment into quantitative values using principal component analysis. For planning the safe motion of a robot, we propose the circle following (CF) method, which abstracts the geometry of the environment while taking the characteristics of the sonar sensors into consideration. Integrating the EVR with the CF method results in a topological exploration strategy using sonar sensors approach. The practicality of this approach is demonstrated by simulations and real experiments in corridor environments.     

移动机器人全覆盖任务的研究进展

结合笔者的有关研究工作,对国内外在移动机器人的全覆盖寻优路径规划方面的研究现状加以分析、比较、归纳和介绍,对全覆盖路径规划方法的发展趋势和研究方向进行了探讨。     

A Bioinspired Neural Network for Real-Time Concurrent Map Building and Complete Coverage Robot Navigation in Unknown Environments

Complete coverage navigation (CCN) requires a special type of robot path planning, where the robots should pass every part of the workspace. CCN is an essential issue for cleaning robots and many other robotic applications. When robots work in unknown environments, map building is required for the robots to effectively cover the complete workspace. Real-time concurrent map building and complete coverage robot navigation are desirable for efficient performance in many applications. In this paper, a novel neural-dynamics-based approach is proposed for real-time map building and CCN of autoxnomous mobile robots in a completely unknown environment. The proposed model is compared with a triangular-cell-map-based complete coverage path planning method (Oh , 2004) that combines distance transform path planning, wall-following algorithm, and template-based technique. The proposed method does not need any templates, even in unknown environments. A local map composed of square or rectangular cells is created through the neural dynamics during the CCN with limited sensory information. From the measured sensory information, a map of the robot's immediate limited surroundings is dynamically built for the robot navigation. In addition, square and rectangular cell map representations are proposed for real-time map building and CCN. Comparison studies of the proposed approach with the triangular-cell-map-based complete coverage path planning approach show that the proposed method is capable of planning more reasonable and shorter collision-free complete coverage paths in unknown environments.      

多中心维修任务分配研究现状

目前战争形式发生变化，装备结构变得复杂，在作战环境多变、维修时间紧迫、维修资源有限等情况下，维修任务的合理分配显得越发重要。因此本文对多中心维修任务分配的研究现状进行了综述，总结现存的任务分配方法，以促进其进一步发展。本文首先对多中心维修任务分配问题进行了描述；然后，对基本概念进行了说明，对问题进行了分析；之后，对优先级排序、任务分配及路径规划常采用的模型与算法等进行了概述；最后，总结了当前研究的不足，并展望了未来发展的方向。     

未知环境中移动机器人故障诊断与容错控制技术综述

以我国月球探测为研究背景,以轮式移动机器人为研究对象,介绍了在外星球表面等未知环境中进行深空探测的移动机器人的故障模型和传感器误差模型,分析了未知环境中移动机器人故障诊断与容错控制的特点.在此基础上综述了国内外在该领域的研究进展和主要方法,包括基于多模型的方法、基于粒子滤波器的方法、基于传感器信息融合的方法以及层次容错结构等. 最后,总结了该领域待解决的几个难点问题,并对该研究领域的发展趋势进行了展望.     

Optimization of route planning and exploration using multi agent system

This research presents an optimization technique for route planning and exploration in unknown environments. It employs the hybrid architecture that implements detection, avoidance and planning using autonomous agents with coordination capabilities. When these agents work for a common objective, they require a robust information interchange module for coordination. They cannot achieve the goal when working independently. The coordination module enhances their performance and efficiency. The multi agent systems can be employed for searching items in unknown environments. The searching of unexploded ordinance such as the land mines is an important application where multi agent systems can be best employed. The hybrid architecture incorporates learning real time A* algorithm for route planning and compares it with A* searching algorithm. Learning real time A* shows better results for multi agent environment and proved to be efficient and robust algorithm. A simulated ant agent system is presented for route planning and optimization and proved to be efficient and robust for large and complex environments.     

移动机器人全局覆盖路径规划算法研究进展与展望

首先通过势场栅格法、单元分解法、全局与局部转换法等三大方法介绍了单移动机器人各种不同的全覆盖算法,分析了各种不同算法的性能,指出了它们的优缺点,并对每种方法的改进方法进行了探讨分析;另外,针对多机器人协作全覆盖路径规划的研究,探讨了基于单机器人全覆盖路径规划算法和任务分配算法等结合得到的多机器人协作路径规划算法;最后探讨移动机器人全覆盖路径规划算法的研究方向。分析结果表明,对于移动机器人全覆盖算法的研究,可充分利用现有算法的优势互补,或借助多学科交叉的优势,寻找更有效的算法。