Distributed stereo vision‐based 6D localization and mapping for multi‐robot teams

Joint simultaneous localization and mapping (SLAM) constitutes the basis for cooperative action in multi‐robot teams. We designed a stereo vision‐based 6D SLAM system combining local and global methods to benefit from their particular advantages: (1) Decoupled local reference filters on each robot for real‐time, long‐term stable state estimation required for stabilization, control and fast obstacle avoidance; (2) Online graph optimization with a novel graph topology and intra‐ as well as inter‐robot loop closures through an improved submap matching method to provide global multi‐robot pose and map estimates; (3) Distribution of the processing of high‐frequency and high‐bandwidth measurements enabling the exchange of aggregated and thus compacted map data. As a result, we gain robustness with respect to communication losses between robots. We evaluated our improved map matcher on simulated and real‐world datasets and present our full system in five real‐world multi‐robot experiments in areas of up 3,000 m² (bounding box), including visual robot detections and submap matches as loop‐closure constraints. Further, we demonstrate its application to autonomous multi‐robot exploration in a challenging rough‐terrain environment at a Moon‐analogue site located on a volcano.     

一种室内环境下移动机器人的路径规划新算法

文章首先建立移动机器人对室内环境的全局模型,通过任务规划将全局目标分解为易于实现的多个子目标。然后针对每一个子目标,根据机器人边界圆直径构建一种改进的可视图,利用可视图和Dijkstra算法得到对子目标的全局路径,从而实现全局目标的路径规划。该算法简单,适于室内环境下移动机器人的实时导航系统。     

The Approximate Cell Decomposition with Local Node Refinement Global Path Planning Method: Path Nodes Refinement and Curve Parametric Interpolation

The paper presents a novel global path planning approach for mobile robot navigation in two dimensional workspace cluttered by polygonal obstacles. The core of the planning method introduced is based on the approximate cell decomposition method. The advantage of the new method is the employment of novel path refinement procedures of the paths produced by approximate cell decomposition that are based on local characteriscics of the workspace. Furthermore, the refined path is parametrically interpolated by cubic splines via a physical centripetal model, introducing the dynamic constraints of mobile robots' motion to the path construction. The method has been implemented both in a computer graphics simulation and on a real mobile robot cruising at indoor environments. Planned paths on several configurations are presented.     

Correlation-Based Scan Matching Using Ultrasonic Sensors for EKF Localization

This paper presents a localization method for a mobile robot equipped with only low-cost ultrasonic sensors. Correlation-based Hough scan matching was used to obtain the robot’s pose without any predefined geometric features. A local grid map and a sound pressure model of ultrasonic sensors were used to acquire reliable scan results from uncertain and noisy ultrasonic sensor data. The robot’s pose was measured using correlation-based Hough scan matching, and the covariance was calculated. Localization was achieved by fusing the measurements from scan matching with the robot’s motion model through the extended Kalman filter. Experimental results verified the performance of the proposed localization method in a real home environment.     

Linear-time robot localization and pose tracking using matching signatures

A feature-based method for global localization of mobile robot using a concept of matching signatures is presented. A group of geometric features, their geometric constraints invariant to frame transform, and location dependent constraints, together are utilized in defining signature of a feature. Plausible global poses are found out by matching signatures of observed features with signatures of global map features. The concept of matching signatures is so developed that the proposed method provides a very efficient solution for global localization. Worst-case complexity of the method for estimating and verifying global poses is linear with the size of global reference map. It will also be shown that with the approach of random sampling the proposed algorithm becomes linear with both the size of global map and number of observed features. In order to avoid pose ambiguity, simultaneous tracking of multiple pose hypotheses staying within the same framework of the proposed method is also addressed. Results obtained from simulation as well as from real world experiment demonstrate the performance and effectiveness of the method.     

一种动态环境下移动机器人的路径规划方法

本文提出了在动态环境中，移动机器人的一种路径规划方法，适用于环境中存 在已知和未知、静止和运动障碍物的复杂情况．采用链接图法建立了机器人工作空间模型， 整个系统由全局路径规划器和局部路径规划器两部分组成．在全局路径规划器中，应用遗传 算法规划出初步全局优化路径．在局部路径规划器中，设计了三种基本行为：跟踪全局路径 的行为、避碰的行为和目标制导的行为，采用基于行为的方法进一步优化路径．其中，避碰 的行为是通过强化学习得到的．仿真和实验结果表明所提方法简便可行，能够满足移动 机器人导航的高实时性要求．     

一种基于连接机制和时空经验的认知地图学习与导航方法

提出了一种连接主义方法, 利用移动机器人自身的时空经验, 在缺乏全局坐标信息和环境先验模型的情况下, 建立面向目标的认知地图. 在线形成的时序处理网络 (TSPN)可提供简洁的历史感知信息, 以神经元激活特性保存空间知识, 引导机器人运动. 结合TSPN和反应式行为模块的导航系统可实现动态的路标及方向检测、路径学习和实时导航功能. 仿真和实际实验验证了系统的有效性和适应性.     

基于改进粒子群算法的机器人路径规划方法

提出一种基于粒子群算法的机器人路径规划方法.将路径规划看作一个带约束的优化问题,约束条件为路径不能经过障碍物,优化目标为整个路径的长度最短.机器人工作空间中的障碍物描述为多边型,对障碍物的顶点进行编号.利用粒子群算法进行路径规划,每一个粒子定义为一个由零或障碍物顶点编号组成的集合,在粒子的迭代过程中考虑约束条件,惯性权重随迭代次数动态改变,使算法既有全局搜索能力也有较强的局部搜索能力.仿真结果表明该方法的正确性和有效性.     

基于改进人工势场模型的移动机器人路径规划

针对传统人工势场模型在移动机器人路径规划中存在局部极值的问题,提出了一种改进方法。该方法首先将传统势场模型转换成解空间中寻优的问题,再加入罚因子建立罚函数数学模型。新的势场模型能够有效的使得机器人成功逃逸局部极值点。最后通过MATLAB进行仿真实验,仿真实验结果证明该方法的有效性。     

基于粒子群算法的移动机器人路径规划

提出一种分步路径规划方法，首先采用链接图建立机器人工作空间模型,用Dijkstra算法求得链接图 最短路径；然后用粒子群算法对此路径进行优化，得到全局最优路径.仿真结果表明:所提方法简便可行,能够满足 移动机器人导航的高实时性要求,是机器人路径规划的一个较好方案.􀁱     

控制受限的一类非完整动力学系统的镇定问题

考虑了控制受限的一类非完整动力学系统的镇定问题.在一定条件下经典非完整约 束下的Lagrange方程可化为扩展的链式系统.利用滑动模态的思想和多步控制策略,设计了不 连续反馈镇定律,该镇定律一方面满足控制受限条件,另一方面使得闭环系统的状态收敛到预 先给定的原点的任意小的ε邻域中.最后对移动机器人进行了仿真,验证了该方法的有效性.     

A Shortest Path Based Path Planning Algorithm for Nonholonomic Mobile Robots

A path planning algorithm for a mobile robot subject to nonholonomic constraints is presented. The algorithmemploys a global- local strategy, and solves the problem in the 2D workspace of the robot, without generating the complexconfiguration space. Firstly, a visibility graph is constructed for finding a collision-free shortest path for a point. Secondly,the path for a point is evaluated to find whether it can be used as a reference to build up a feasible path for the mobile robot.If not, this path is discarded and the next shortest path is selected and evaluated until a right reference path is found. Thirdly,robot configurations are placed along the selected path in the way that the robot can move from one configuration to the nextavoiding obstacles. Lemmas are introduced to ensure that the robot travels using direct, indirect or reversal manoeuvres. Thealgorithm is computationally efficient and runs in time O(nk + n log n) for k obstacles andn vertices. The path found is near optimal in terms of distance travelled. The algorithm is tested in computersimulations and test results are presented to demonstrate its versatility in complex environments.     

一种新的移动机器人全局路径规划算法

提出了一种新的移动机器人全局路径规划算法．该算法不需要对环境中的障碍物特征做任何假设,也不需要建立障碍物的连通图模型,有效地克服了传统路径规划算法因为搜索而带来的计算复杂性问题,提高了算法的适应性和实时性．仿真结果证明了算法的有效性．     

一种基于图像的移动机器人泊位方法

提出一种新的移动机器人泊位方法. 该方法采用一幅预先采集的参考图像定义机器人的期望泊位状 态（期望的位置和方向）,利用尺度不变特征变换（SIFT）算法和基于双向BBF 的特征匹配算法实现泊位站当前图 像与参考图像之间的匹配以获取视觉反馈信息,基于极点伺服策略根据参考图像准直机器人,采用质心跟踪法防止 目标图像出视场,采用RANSAC 算法求解当前图像与参考图像间的仿射变换,利用一个末段控制策略实现精确泊 位．本文方法不需要环境模型或人工标记．室内环境下的实验结果证实了该方法的有效性．     

Sensor planning for mobile robot localization using Bayesian network inference

We propose a new method of sensor planning for mobile robot localization using Bayesian network inference. Since we can model causal relations between situations of the robot's behavior and sensing events as nodes of a Bayesian network, we can use the inference of the network for dealing with uncertainty in sensor planning and thus derive appropriate sensing actions. In this system we employ a multi-layered-behavior architecture for navigation and localization. This architecture effectively combines mapping of local sensor information and the inference via a Bayesian network for sensor planning. The mobile robot recognizes the local sensor patterns for localization and navigation using a learned regression function. Since the environment may change during the navigation and the sensor capability has limitations in the real world, the mobile robot actively gathers sensor information to construct and reconstruct a Bayesian network, and then derives an appropriate sensing action which maximizes a utility function based on inference of the reconstructed network. The utility function takes into account belief of the localization and the sensing cost. We have conducted some simulation and real robot experiments to validate the sensor planning system.     

基于地图的移动机器人自定位与导航系统

针对地图已知情况下的移动机器人大范围导航问题,研制了一个由地图编辑器模块、地图匹配与定位模块以及多层递阶规划模块三部分组成的移动机器人导航系统．地图编辑器负责导航地图的编辑;地图匹配与定位模块利用里程计和激光雷达数据实现基于地图匹配的自定位;多层递阶规划模块将基于拓扑地图的全局规划、基于栅格地图的局部规划和底层的行为控制功能有机结合．通过室内定位和大范围导航实验评估了本系统的有效性和准确性．     

面向集成变量化设计的三维几何约束求解方法

针对集成变量化设计中三维几何约束和装配几何约束的混合建模与求解问题,提出改进的有向图方法.该方法采用几何约束的基本约束表达和几何实体的抽象对偶实体表达,引入定向弧表达实体之间的内在依赖关系建立混合几何约束有向图模型;结合约束有向图的优化处理,实现了几何约束系统的细粒度分解和高效并行求解.最后用实例验证了文中方法的正确性和有效性.     

非时间参考的移动机器人路径跟踪控制

基于非时间参考的思想提出了一种移动机器人路径跟踪控制方法.首先选择移动机器人实际路径在某参考系下的x轴投影作为非时间参考量,并针对一类几何路径的跟踪建立移动机器人非时间参考的运动学模 型,据此设计以恒定速度跟踪期望路径的控制律,然后在此基础上给出跟踪任意几何路径的分段控制策略.此跟踪 控制系统所采用的参考量为非时间量􀁯,摆脱了时间因素的影响,因此能提高移动机器人在不确定环境中的跟踪能 力.仿真和物理实验表明了控制方法的有效性.􀁱     

车型移动机器人的SPRM路径规划方法

研究车型移动机器人的路径规划问题,提出一种用局部规则路图结合随机路图,辅助建立全局复合路图的环境建模方法. 在此基础上进行路径规划,提高了在障碍物附近产生的局部路径的质量,减少了由于频繁地执行避碰校验所造成的时间消耗,并解决了可行空间丢失的问题. 仿真实验验证了这种方法在车形移动机器人路径规划应用中的有效性.     

Cooperative adaptive consensus tracking for multiple nonholonomic mobile robots

This paper addresses the cooperative adaptive consensus tracking for a group of multiple nonholonomic mobile robots, where the nonholonomic robot model is assumed to be a canonical vehicle having two actuated wheels and one passive wheel. By integrating a kinematic controller and a torque controller for the nonholonomic robotic system, a cooperative adaptive consensus tracking strategy is developed for the uncertain dynamic models using Lyapunov-like analysis in combination with backstepping approach and sliding mode technique. A key feature of the developed adaptive consensus tracking algorithm is the introduction of a directed network topology into the control constraints based on algebraic graph theory to characterise the communication interaction among robots, which plays an important role in realising the cooperative consensus tracking with respect to a specific common reference trajectory. Furthermore, a novel framework is proposed for developing a unified methodology for the convergence analysis of the closed-loop control systems, which can fully ensure the desired adaptive consensus tracking for multiple nonholonomic mobile robots. Subsequently, illustrative examples and numerical simulations are provided to demonstrate and visualise the theoretical results.