移动机器人的智能路径规划算法综述

针对目前移动机器人路径规划所处的的环境复杂度高、随机性强等情况,难以有效地实现最优路径规划的问题,从移动机器人的实际应用出发,对点对点路径规划和遍历路径规划、全局路径规划和局部路径规划进行综述,对各类规划方法进行分析与归纳;重点分析强化学习算法的路径规划技术;针对目前路径规划算法存在的问题,提出类脑智能算法应用于路径规划的探索,同时给出路径规划在农业装备应用的新思路.     

智能系统局部路径规划算法研究

对于非常规环境下的局部路径规划,建立了相应的数据量测模型,提出了分块和滤波算法体系.通过实车试验验证算法及系统的可靠性,并时结果进行分析、规律进行总结,研究降噪系统在复杂环境下的实用效果.完成智能系统在实际环境中局部路径规划的最优评价方法.     

移动机器人全局路径规划算法综述

全局路径规划是移动机器人室外工作的关键技术,全局路径规划相关算法主要应用于地理场景预知的室外环境中,机器人面对复杂多变的室外环境,通过对算法的优化改进来提高机器人路径规划的实时避障性、路径平滑性、规划有效性就成为了全局路径规划算法的核心研究内容.首先根据算法的智能程度,将移动机器人的全局路径规划算法分为传统全局路径规划算法和仿生智能全局路径规划算法,并深入阐述了实际应用更为广泛的多目标路径规划算法,然后介绍了当前每种算法的几种典型的优化改进方法,并对其优化改进后的算法的优缺点进行了分析总结,最后对全局路径算法的未来发展趋势进行了展望,指出全局路径规划算法将向优化已有常规算法路径规划的性能、多种算法优势融合、复杂环境中动态避障、适应多样化环境的地图表示方法这4方面发展.     

基于人工神经网络的智能化煤矿监测系统的路径规划

给出了一种煤矿监测、数据采集用智能化监测系统的设计方案,监测系统在运行的过程中可实现数据采集、循迹、避障、测距等功能.监测系统的路径规划采用了一种人工神经网络算法,该算法利用神经网络模型描述监测系统工作空间的动态环境信息,并建立起机器人动态避障与网络输出间的关系.仿真结果表明该算法具有较好的环境适应性和实时性,所用的动态路径规划方法是正确和有效的.     

无人驾驶车辆局部路径规划的时间一致性与鲁棒性研究

在无人驾驶系统中,局部规划在跟踪全局路径的同时完成避障,提高了规划系统在动态未知环境中的工作能力.避障分析的有效性是局部规划最重要的功能之一.然而在仿真和实车测试中发现,广泛使用的基于优化求解的局部规划算法无法在不依赖全局精确定位时保证规划结果满足时间一致性要求.时间不一致将导致车辆的实际行驶路线偏离初始规划结果,造成避障分析失效.本文设计了基于前向预测的局部路径规划算法,在不依赖全局精确定位的前提下保证规划结果的时间一致性.除了时间一致性问题外,跟踪控制误差也是导致规划结果避障分析失效的主要原因之一.现有研究大多通过膨胀障碍物体现误差的影响,然而这种方法无法避免车辆驶入膨胀危险区域而停车.本算法在路径生成过程中增加误差影响,用通行区域代替原有不具有宽度的规划路径进行避障分析,既可以解决误差导致的避障失效,又避免出现膨胀障碍物带来的问题.通过V-Rep软件与实车规划程序进行联合仿真,在能够体现时间一致性影响的典型场景中对本算法与基于最优化曲线生成的局部路径规划算法进行比较, 验证了该算法具有更好的安全分析有效性.应用本算法的北京理工大学无人驾驶平台参加了2013年智能车未来挑战赛,在无人干预的情况下顺利完成 18公里城郊赛段和5公里城市赛段行驶,展现了良好的避障能力.     

基于双目视觉移动机器人的路径规划和避障研究

提出了一种移动机器人路径规划和避障的系统设计方案,实现了移动机器人自主行进的路径规划和自动避障功能.详细说明了如何采用立体视觉实现对环境的探测,利用图像处理算法的组合分离出地面、背景、障碍物和目标物,采用边界不变矩实现障碍物和目标物的区分,改进了经典的人工势场法进行路径的规划,根据模糊控制原理设计了避障控制器和避障规则.实际的运行结果表明了该系统的可行性和有效性,该系统实现了移动机器人利用自身传感器感知环境信息,动态规划行进路径,成功躲避障碍物等功能.     

采用改进D*Lite算法的自主移动机器人路径规划

针对动态环境下自主移动机器人的路径规划问题提出了改进D*Lite算法;该算法在D*Lite算法的基础上,引入Bresenham画线算法对扩展节点进行可视检测,得到方向任意且避免不必要转折的预规划路径,并建立分辨率高于全局障碍图的局部障碍图,动态存储传感器实时获取的局部环境信息,充分利用局部环境信息实时重规划机器人当前位置到目标点的最优路径,提高算法的规划精度及对动态环境的适应性;仿真实验结果证明,该算法大大缩短了路径长度,并且具有可行性和实时性.     

能力风暴机器人的全局路径规划

基于Messy遗传算法(Messy GA),设计了移动机器人的通用路径规划算法,其中的优化目标包括最短路径、一定的平滑度和最优安全距离.在算法中加入了优化算子及交叉率和变异率的自适应调整,加快了收敛速度.仿真结果验证了所提方法的有效性.根据能力风暴机器人(AS-R)的实际运行要求,修改算法以扩大路径与障碍物之间的间隔度,并提出采用平滑的方法来优化路径.以AS-R为平台进行了轨迹跟踪实验.实验结果表明算法在随机摆放障碍物和实验室环境下可以实现路径规划,并能够最终实现AS-R机器人的全局路径规划.     

精梳棉车间棉卷自动运输系统开发

连接生产工序参与生产流程是移动机器人真正应用于车间提高生产效率的关键。根据精梳棉车间的工作环境,研制了基于开源机器人操作系统（ROS）的棉卷自动运输机器人系统。移动机器人应用激光传感器采集车间环境数据,采用Gmapping算法构建精梳棉车间地图,应用经典A^*和人工势场法融合的混合路径规划算法自主规划行驶路径,并采用视觉二维码技术实现生产设备前的精确定位。测试结果显示,在1 m/s安全速度下,棉卷运输机器人定位误差与航向偏差的平均绝对误差（MAE）分别小于5 cm和07°,完全满足在精梳棉车间内自主避障运动以及在生产设备前精确定位的实际作业需求。     

机器人在已知三维自然环境中的路径规划算法

研究了移动机器人在三维环境的路径规划问题,针对该问题中存在环境适应性和全局性差的不足,对人工势场法进行改进,提出了一种新的路径规划的算法.该算法首先对已知的三维自然环境进行栅格化,建立栅格运行费用的评估模型,计算每个栅格的运行费用;然后依据栅格的运行费用建立斥力场,以目标点为中心的建立引力场,同时提出解决局部最小值的问题的方法;最后将两者合力的方向作为移动机器人在该点的路径走向,规划出一条从起始点到目标点的运行费用较低的路径.仿真实验结果表明,该算法能有效降低运行费用,适应性和全局性好,适合应用于移动机器人在三维自然环境中的路径规划.     

欠驱动自主水面船的非线性路径跟踪控制

基于级联方法提出一种欠驱动自主水面船的全局K指数稳定路径跟踪控制算法.采用以自由路径参考点为原点的Serret-Frenet坐标系建立路径跟踪误差的动态模型,以路径参数的变化率为附加控制输入,克服了以正交投影点为坐标原点时的奇异值问题.设计路径跟踪航向角指令,将路径跟踪模型分解为位置跟踪子系统和航向角、前向速度跟踪子系统两个子系统级联的形式,设计航向角和前向速度的全局指数稳定跟踪控制器,应用级联系统理论证明了路径跟踪误差的全局K指数稳定性.数学仿真和自主水面船湖上实验结果验证了该路径跟踪控制算法的有效性.     

A lightweight autonomous MAV for indoor search and rescue

Micro Aerial Vehicles (MAVs) have great potentials to be applied for indoor search and rescue missions. In this paper, we propose a modular lightweight design of an autonomous MAV with integrated hardware and software. The MAV is equipped with the 2D laser scanner, camera, mission computer and flight controller, running all the computation onboard in real time. The onboard perception system includes a laser‐based SLAM module and a custom‐designed visual detection module. A dual Kalman filter design provides robust state estimation by multiple sensor fusion. Specifically, the fusion module provides robust altitude measurement in the circumstance of surface changing. In addition, indoor‐outdoor transition is explicitly handled by the fusion module. In order to efficiently navigate through obstacles and adapt to multiple tasks, a task tree‐based mission planning method is seamlessly integrated with path planning and control modules. The MAV is capable of searching and rescuing victims from unknown indoor environments effectively. It was validated by our award‐winning performance at the 2017 International Micro Air Vehicle Competition (IMAV 2017), held in Toulouse, France. The performance video is available on https://youtu.be/8H19ppS_VXM .     

融合粒子群与改进蚁群算法的AUV路径规划算法

针对传统蚁群算法在处理自主式水下机器人AUV（Autonomous Underwater Vehicle）三维路径规划问题时存在初期寻径能力弱、算法收敛速度慢等问题,提出一种融合粒子群与改进蚁群算法的AUV路径规划算法PSO-ACO（Particle Swarm Optimization-improved Ant Colony Optimization）。基于空间分层思想建立三维栅格模型实现水下环境建模;综合考虑路径长度、崎岖性、危险性等因素建立路径评价模型;先使用粒子群算法预搜索路径来优化蚁群算法的初始信息素;再对蚁群算法改进状态转移规则、信息素更新方式并加入奖惩机制实现全局路径规划。实验表明,算法能有效提高初期寻径能力和全局搜索能力,减少收敛迭代次数并缩短搜索使用时间。     

一种未知环境下的快速路径规划方法*

为提高机器人在未知环境中的快速路径规划能力,引入自由路径表征可以通过的自由空间,引入风险函数评价机器人切入自由路径过程中发生碰撞的风险。通过搜索最优自由路径、评价碰撞风险压缩表示环境信息,使得未知环境中利用模糊控制器进行局部路径规划的实时性大为提高。与虚拟势场法等传统方法相比,其无局部最小,且极大缓解了狭窄环境中的振荡现象。实验及仿真均表明该方法实时性好、规划所得路径优于已有方法。     

基于学习分类器的自主地面车在狭隘环境中的路径规划

提出了一种基于学习分类器(LCS)的避碰路径规划方法,设计了集成适应度函数,在确保安全避碰的前提下,解决自主地面车(ALV)在狭隘环境下的路径优化问题.不同环境的仿真实验结果表明,遗传算法和学习分类器结合用于自主地面车的路径规划是收敛的,提高了ALV在狭隘环境中快速发现安全路径的能力.     

PDE-based robust robotic navigation

In robotic navigation, path planning is aimed at getting the optimum collision-free path between a starting and target locations. The optimality criterion depends on the surrounding environment and the running conditions. In this paper, we propose a general, robust, and fast path planning framework for robotic navigation using level set methods. A level set speed function is proposed such that the minimum cost path between the starting and target locations in the environment, is the optimum planned path. The speed function is controlled by one parameter, which takes one of three possible values to generate either the safest, the shortest, or the hybrid planned path. The hybrid path is much safer than the shortest path, but less shorter than the safest one. The main idea of the proposed technique is to propagate a monotonic wave front with a particular speed function from a starting location until the target is reached and then extracts the optimum planned path between them by solving an ordinary differential equation (ODE) using an efficient numerical scheme. The framework supports both local and global planning for both 2D and 3D environments. The robustness of the proposed framework is demonstrated by correctly extracting planned paths of complex maps.     

基于交互车辆轨迹预测的自动驾驶车辆轨迹规划

针对城市道路等复杂行车场景,提出了一种基于交互车辆轨迹预测的自动驾驶车辆轨迹规划方法,将高维度的轨迹规划解耦为低维度的路径规划和速度规划;首先,采用五次多项式曲线和碰撞剩余时间规划车辆行驶路径;其次,在社会生成对抗网络Social-GAN的基础上结合车辆空间影响和注意力机制对交互车辆进行轨迹预测;然后,结合主车规划路径、交互车辆预测轨迹及碰撞判定模型得到主车S-T图,采用动态规划和数值优化方法求解S-T图,从而得到满足车辆动力学约束的安全、舒适最优速度曲线;最后,搭建PreScan-CarSim-Matlab&Simulink-Python联合仿真模型进行实验验证。仿真结果表明,提出的轨迹规划方法能够在对交互车辆有效避撞的前提下,保证车辆行驶的舒适性和高效性。     

A Generalized Real-Time Obstacle Avoidance Method Without the Cspace Calculation

An important concept proposed in the early stage of robot path planning field is the shrinking of a robot to a point and meanwhile the expanding of obstacles in the workspace as a set of new obstacles. The resulting grown obstacles are called the Configuration Space (Cspace) obstacles. The find-path problem is then transformed into that of finding a collision-free path for a point robot among the Cspace obstacles. However, the research experiences have shown that the Cspace transform is very hard when the following situations occur: 1) both the robot and obstacles are not polygons, and 2) the robot is allowed to rotate. This situation gets even worse when the robot and obstacles are three dimensional (3D) objects with various shapes. For this reason, direct path planning approaches without the Cspace transformation is quite useful and expected.Motivated by the practical requirements of robot path planning, a generalized constrained optimization problem (GCOP) with not only logic AND but also logic OR relationships was proposed and a mathematical solution developed previously. This paper inherits the fundamental ideas of inequality and optimization techniques from the previous work, converts the obstacle avoidance problem into a semi-infinite constrained optimization problem with the help of the mathematical transformation, and proposes a direct path planning approach without Cspace calculation, which is quite different from traditional methods. To show its merits, simulation results in 3D space have been presented.     

Deliberative On-Line Local Path Planning for Autonomous Mobile Robots

This paper describes a method for local path planning for mobile robots that combines reactive obstacle avoidance with on-line local path planning. Our approach is different to other model-based navigation approaches since it integrates both global and local planning processes in the same architecture while other methods only combine global path planning with a reactive method to avoid non-modelled obstacles. Our local planning is only triggered when an unexpected obstacle is found and reactive navigation is not able to regain the initial path. A new trajectory is then calculated on-line using only proximity sensor information. This trajectory can be improved during the available time using an anytime algorithm. The proposed method complements the reactive behaviour and allows the robot to navigate safely in a partially known environment during a long time period without human intervention.     

Ultrasound navigation based on minimally designed vehicle inspired by the bio-sonar strategy of bats

ABSTRACT

In this study, we conducted vehicle experiments and a numerical simulation based on a simple algorithm inspired by the bio-sonar system of bats to investigate how the behavioral strategy employed by bats contributes to acoustic navigation for minimal-design sensing. In particular, a double-pulse scanning method inspired from the echolocation behavior of bats was proposed, in which (1) the direction of ultrasound emission by a vehicle equipped with 1 transmitter and 2 receivers was alternately shifted between the movement direction of the vehicle and the direction of the nearest obstacle, and (2) the movement direction of the vehicle was calculated for every double-pulse emission based on integrated information from all echoes detected. As a result of 100 repeated drives in a practical course, the success rate of an obstacle-avoidance drive improved from 13% with the conventional single-pulse scanning method to 73% with the proposed method. Furthermore, the numerical simulation demonstrated that the proposed method achieves robust path planning by suppressing the localization ambiguity due to the interference of multiple echoes. The practical experiments and numerical simulation suggest that bats employ a simple behavioral solution in the operation of acoustic sensing for various problems occurring in the real world.