A two-layered subgoal based mobile robot navigation algorithm with vision system and IR sensors

The present work describes the real-life implementation of a mobile robot navigation scheme where vision sensing is employed as primary sensor for path planning and IR sensors are employed as secondary sensors for actual navigation of the mobile robot with obstacle avoidance capability in a static or dynamic indoor environment. This two-layer based, goal-driven architecture utilizes a wireless camera in the first layer to acquire image and perform image processing, online, to determine subgoal, employing a shortest path algorithm, online. The subgoal information is then utilized in the second layer to navigate the robot utilizing IR sensors. Once the subgoal is reached, vision based path planning and IR guided navigation is reactivated. This sequential process is continued in an iterative fashion until the robot reaches the goal. The algorithm has been effectively tested for several real-life environments created in our laboratory and the results are found to be satisfactory.     

基于ATmega128的模糊控制系统

以ATmage128单片机为核心,提出了一种移动机器人路径规划模糊控制系统的设计思路,并给出了控制系统硬件和软件设计方案.系统预先设定目标,移动机器人根据超声波传感器感知环境信息,采用模糊控制进行路径规划,机器人自主移动到达目标.硬件方面介绍了显示电路、超声波测距电路、电机驱动电路、无线通信电路等接口电路.软件方面给出了模糊控制、超声波测距和无线通信的软件设计方案.使用MATLAB软件进行了仿真,仿真结果表明模糊控制系统具有较理想的有效性和可行性.     

未知环境中基于强化学习的移动机器人路径规划

为解决未知环境中移动机器人的自适应路径规划问题,提出了一种基于Q学习算法的自主学习方法。首先设计了未知环境中基于传感器信息的移动机器人自主路径规划的学习框架,并建立了学习算法中各要素的数学模型;然后利用模糊逻辑方法解决了连续状态空间的泛化问题,有效地降低了Q值表的维数,加快了算法的学习速度;最后在不同障碍环境中对基于Q学习算法的自主学习方法进行了仿真实验,仿真实验中移动机器人通过自主学习较好地完成了自适应路径规划。研究结果证明了该自主学习方法的有效性。     

一种考虑安全的移动机器人矢量场路径规划算法

提出了一种考虑安全的移动机器人矢量场路径规划算法,环境的矢量场模型体现了路径对安全的不同要求。通过调节模型参数可以规划得到安全和长度两个标准下的满意路径,此路径既不会太接近障碍,也不会太长。所提出模型计算简单,计算量与运动空间障碍的个数成正比。算法鲁棒性强,除了固定环境,还适合用来进行动态环境下的路径规划。仿真结果验证了算法的有效性。     

基于时间栅格法和最优搜索的电网巡检机器人避障路径规划方法

针对电网巡检机器人存在避障能力低下和路径规划不合理的问题,研究基于时间栅格法和最优搜索的电网巡检机器人避障路径规划方法.利用时间栅格法标识工作空间内障碍物,构建机器人电网巡检环境信息,通过最优搜索避障路径算法,全局规划机器人到达目标点的路径,结合改进势场法,通过调整斥力和引力势函数,计算合力实现机器人的局部避障及避障路径规划,形成全局和局部相结合的避障方法.试验结果表明,躲避静态障碍物和动态障碍物的平均躲避成功率分别为 ９８．３７％ 和 ９６． １２％ ,避障路径规划平均耗时为 １.５６ s ,具备快速、高效、精准的避障及路径规划能力,可提升机器人的动静态障碍物避障能力和路径规划效率.     

Quadratic programming-based approach for autonomous vehicle path planning in space

Path planning for space vehicles is still a challenging problem although considerable progress has been made over the past decades.The major difficulties are that most of existing methods only adapt to static environment instead of dynamic one,and also can not solve the inherent constraints arising from the robot body and the exterior environment.To address these difficulties,this research aims to provide a feasible trajectory based on quadratic programming(QP) for path planning in three-dimensional space where an autonomous vehicle is requested to pursue a target while avoiding static or dynamic obstacles.First,the objective function is derived from the pursuit task which is defined in terms of the relative distance to the target,as well as the angle between the velocity and the position in the relative velocity coordinates(RVCs).The optimization is in quadratic polynomial form according to QP formulation.Then,the avoidance task is modeled with linear constraints in RVCs.Some other constraints,such as kinematics,dynamics,and sensor range,are included.Last,simulations with typical multiple obstacles are carried out,including in static and dynamic environments and one of human-in-the-loop.The results indicate that the optimal trajectories of the autonomous robot in three-dimensional space satisfy the required performances.Therefore,the QP model proposed in this paper not only adapts to dynamic environment with uncertainty,but also can satisfy all kinds of constraints,and it provides an efficient approach to solve the problems of path planning in three-dimensional space.     

基于改进 RRT ∗ 算法的智能轮椅全局路径规划研究

现实环境中智能轮椅大多数处在复杂场景下工作,其自主导航时对路径安全性等要求较高。 渐进最优随机搜索树 RRT ∗ 算法 基本满足移动机器人最优路径规划,但由于智能轮椅本体较大,容易与环境较近接触,因此可对环境模型进行膨胀并定义不同搜索步 长,使其规划出的路径远离障碍物。 其次为保证用户在使用智能轮椅导航时能够获得更高的舒适性,更高效的到达目的地,而借用启 发式约束采样思想和人工势场中引力场思想修剪此算法规划时的冗余节点,从而减小系统运行内存,随后结合轮椅的最小转弯半径, 提出最小段路径曲率约束策略和三次 B 样条曲线算法对路径进行平滑处理,使其更加适合轮椅行驶。 最终在 MATLAB 和 Gazebo 仿真 平台对改进前后算法对比实验,并将本文算法应用与智能轮椅实体上,试验结果表明,该算法能够有效解决智能轮椅全局路径规划问 题,能够明显提升全局路径规划效率,具有一定安全性,可为其移动机器人领域提供有效参考。     

移动机器人在线实时路径规划

研究了不确定环境下移动机器人的路径规划问题。采用全局规划和局部规划相结合的方法，提出了一种基于极坐标空间，以期望方向角为优化性能指标的在线实日寸路径规划方法。该法利用机器人的传感器系统，实时探测局部环境信息，在每次的局部规划窗口，确定机器人的期望方向角，以机器人的实际运动方向角与期望方向角之间的差异来驱动机器人避开障碍物和朝向目标点运动。该法不仅简单灵活，而且克服了全局规划和局部规划的缺陷。仿真实验表明其有效可行性、实时性、优化性、精度高、稳定性好。     

Special Wide Area Path Planning Method of an Autonomous Mobile Robot Using GPS

Using sensor and GPS to make a trajectory planning for the stationary obstacle,autonomous mobile robot can assume that it is placed at the center of the map,and from the distance information between autonomous mobile robot and obstacles.But in case of active moving obstacle,many components and information need to process since their moving trace should be considered in real time.This paper proposes mobile robot’s driving algorithm of unknown dynamic environment in order to drive intelligently to destination using ultrasonic and Global Positional System(GPS).Sensors adjusted the placement dependment on driving of robot,and the robot plans the evasion method according to obstacle which are detected by sensors.The robot saves GPS coordinate of complex obstacle.If there are many repeated driving,robot creates new obstacles to the location by itself.And then it drives to the destination resolving a large range of local minimum point.If it needs an intelligent circumstantial decision,a proposed algorithm is suited for effective obstacle avoidance and arrival at the destination by performing simulations.     

动态环境下移动机器人路径规划的改进蚁群算法

研究动态环境下移动机器人路径规划问题,采用栅格法对机器人工作空间进行建模,在使用蚁群算法进行全局路径搜索过程中引入人工势场的概念,使蚂蚁对最优路径更加敏感;机器人针对动态环境中可能出现的不同类型障碍物分别执行不同的避障策略;同时提出一种最优路径预测模型用于预测在避障过程中是否出现新的最优路径。算法结合人工势场法和蚁群算法的特点,将全局路径规划与局部路径规划相融合以提高路径搜索的效率。仿真结果验证了该算法的有效性。     

基于细菌觅食行为的移动机器人动态路径规划

将自然界中细菌的自适应觅食现象与移动机器人动态路径规划相类比,设计基于细菌最优觅食理论的新型生物启发计算方法(DBFO)。通过对无约束复杂动态多峰测试函数库测试,证实DBFO算法具有较高的准确性和稳定性,具备动态优化能力。并以Sphere函数作为机器人路径寻优的仿真测试环境,DBFO算法驱动的搜索主体可以顺利避开障碍并快速找到目标地点,有效节约了行走时间,验证了其是一种高效、稳定、有竞争力的仿生智能优化方法,在求解实际复杂工程优化问题中体现了极为优越的搜索效率和求解精度。     

基于粒子群优化算法的小型足球机器人路径规划

为了解决足球机器人无法躲避动态障碍物和容易陷入局部极值的问题,在深入研究粒子群优化算法的基础上,提出了采用栅格法与粒子群优化算法相结合的路径规划算法。首先采用栅格法对小型足球机器人工作环境构造模型,再利用改进的粒子群优化算法进行最优路径搜索。该算法实现简单,收敛速度快,不易陷入局部极值,不仅能够满足足球机器人实时动态的路径规划要求,而且能满足不同环境下的路径规划要求。仿真实验表明,该方法可以很好地应用于足球机器人的路径规划中。     

基于感知-行为的移动机器人全区域覆盖路径规划

针对未知环境信息下的移动机器人全局区域覆盖路径规划问题,提出了一种基于感知-行为的路径规划方法。根据移动机器人实时感知的传感器信息,建立机器人全区域覆盖的行为执行表,在机器人全区域覆盖过程中,通过这种感知与行为的关联能够实时有效地驱动机器人实现路径规划。模拟与仿真的结果证明了算法的简单性和实用性。     

面向复合地图的移动机器人分层路径规划

针对传统路径规划方法在部分未知复杂大场景环境下搜索空间大、效率低、避障成功率不高等问题,提出一种基于拓扑-栅格-度量复合地图的移动机器人分层路径规划方法。首先将机器人作业环境描述为栅格地图并划分为多个栅格化的子区域,以子区域为关键节点进行位置关系抽象从而获得拓扑架构,并对局部栅格区域进行精细化描述,构建拓扑-栅格-度量的复合地图。其次,在不同地图层级上分区域搜索机器人路径,在拓扑地图上采用Floyd算法规划子区域之间的区间路径,面向栅格地图提出搜索子区域内部路径的改进A^*算法,通过引入扩展点筛选策略、双向搜索机制、路径冗余点剔除技术提高路径规划的效率与质量,并拼接各段区间路径和内部路径生成全局优化初始路径。最后,针对部分未知场景中的动态障碍物,在度量地图上提出基于深度强化学习架构的动态避障路径规划方法,利用价值分类经验回放机制提高样本的利用率和模型训练的效率。实验结果表明,所提方法有较高的搜索效率和避障成功率,生成的路径兼具安全性和平滑性。     

A new approach to map building by sensor data fusion: sequential principal component-SPC method

This study proposes a new map building method for a mobile robot operating in an environment with obstacles by fusing sensor data. Required information for a map designing is supplied by fusion of different sensor data using the sequential principal component (SPC) method. We discuss mathematical and experimental issues of the method by comparing a Bayesian method that works efficiently in map building using sensor data fusion. Application of the method for grid based map building is introduced and compatibility in mobile robot navigation is demonstrated. Experimental studies are implemented on Nomad200 mobile robot successfully.     

超声波网络导航中移动机器人的测位误差分析

通过分析揭示了超声波网络导航系统测位误差主要来源于超声波接收器高度误差、超声波传输距离测量误差、超声波接收器在水平面内的二维位置误差。它们的综合作用构成了移动机器人的位置公差带，但受到所选三个超声波接收器分布方式的影响。实验检测结果证实了该分析结果的有效性，为控制和提高超声波网络导航精度提供了依据。     

Optimum design method of multi-axis force sensor integrated in humanoid robot foot system

The multi-axis force sensor (MFS) has been extensively adopted in humanoid robot foot to obtain external forces/moments acted on the foot while in locomotion. The precision and comprehensive performances of MFS originally determined by its elastomer structure are significant essentially for humanoid robots to keep balance by detection of external forces/moments during walking motion. The reason for the transmission of coupling-error of structure is analyzed and then the optimum method for the function of objective-optimization of MFS compliance matrix, Finite Element Method (FEM), orthogonal design and range analysis method are all proposed synthetically. In addition, the comprehensive performances of MFS designed by the optimum method are analyzed and elaborated in a concrete design case. Finally, the main indexes of the MFS are verified experimentally through humanoid robot’s dynamic walking motion.     

机器人导航的自主选择搜索策略蜂群规划算法

为了减少机器人导航路径长度和路径规划时间,提出了基于自主选择搜索策略蜂群算法的规划方法.分析了人工蜂群算法原理,依据蜜蜂从自身认知、种群认知和其他个体认知等多种环境认知方式,对应给出了多种蜜源搜索方式;通过建立不同蜜源搜索方式的即时价值和后效价值模型,计算了蜜蜂选择不同蜜源搜索方式的概率,从而给出了蜜蜂对蜜源搜索方式的...     

Model algorithm control for path tracking of wheeled mobile robots

This paper proposes a model algorithm control (MAC) method for the path tracking control of differentially steered wheeled mobile robots (WMRs) subject to nonholonomic constraints. The continuous dynamic model of the wheeled mobile robot is presented and used as the model to be controlled. The MAC controller is designed based on the sampled-data representation of the system. In this paper the case that there exists time delay in the control input is also considered. A time discretization method using the Taylor series and the zero-order-hold (ZOH) assumption is proposed to discretize the continuous dynamic model of the WMR. This time discretization method is especially useful in the case of the time delayed system. It can provide finite dimensional and more accurate discretized form model of the mobile robot with input time delay and convert it into a general nonlinear time discretized form to which the MAC controller can be applied. Simulations are conducted to show the performance and feasibility of the proposed control strategy. In these simulations the WMR is controlled to track two difference reference paths such as the “8” shape path and the circular path. The bounded inertial parameters uncertainties and some disturbance are also considered in the model of the control system.     

路径规划技术的现状与发展综述

路径规划技术是虚拟装配技术、移动机器人技术研究中的一个重要领域。它分为基于模型的环境已知的全局路径规划和基于传感器的环境未知的局部路径规划。本文详细地叙述了路径规划技术的分类和发展现状,全局路径规划和局部路径规划中的各种方法,具体地分析了各种方法的算法过程,并指出了各种方法的优缺点,最后对路径规划技术的未来的发展趋势进行了展望。