未知环境下基于椭圆约束的机器人路径规划

针对未知环境下机器人路径规划问题,提出一种基于椭圆约束的路径规划方法。借助椭圆约束规划路径,将路径规划问题转化为椭圆参数优化问题。通过建立椭圆约束优化模型,引入障碍物和目标位置的约束,考虑机器人运动步长及运动方向的影响,实现复杂未知环境下机器人路径规划。基于不同算法的仿真实验结果表明,该方法有效解决了未知环境下机器人路径规划问题,在大量障碍物存在的未知环境,也能快速有效地进行无碰撞路径规划。     

一种面向加密流量的网络应用识别方法

为实现网络流量的有效管控,提出一种基于安全套接层(SSL)协议交互字段与多输入最大化单输出隐马尔可夫模型(HMM)的加密应用并行识别方法.将来自客户端或者服务器的单向数据流SSL协议交互阶段的字段作为HMM模型的观测序列,并对所有待识别的加密应用建立HMM模型形成指纹库.在此基础上,利用前向算法计算未知观测序列被识别为HMM模型的概率,选取概率最大HMM模型所对应的加密应用作为识别结果.实验结果表明,与传统应用识别方法相比,该方法对典型加密应用具有更好的识别效果及鲁棒性.     

基于障碍预估与概率方向权值的移动机器人动态路径规划

主要研究了移动机器人在未知动态环境中的路径规划问题.提出一种将障碍预估与概率方向权值相结合的动态路径规划新方法.该方法将卡尔曼滤波引入到规划算法中,使得对障碍物运动状态的实时有效预估成为可能.同时,为实现移动机器人的实时路径规划,提出一种新的概率方向权值方法,基于周期规划将障碍物与目标信息进行融合,能够有效处理室内环境下对于障碍物的速度和运动轨迹均未知的动态路径规划问题.仿真结果以及基于SmartROB2移动机器人平台所进行的实验结果验证了该方法的有效性和实用性.     

未知观测噪声时机器人同步定位与地图构建

对未知观测噪声的机器人同步定位与地图构建问题,提出基于神经网络PID自适应学习观测噪声的机器人同步定位与地图构建算法.已知系统噪声为高斯分布,噪声的方差未知,但其真值是在某个有限集合内.设计一个由神经网络PID控制器、观测噪声调整以及中值滤波构成的噪声在线辨识单元.通过自适应在线辨识观测噪声,并进行新息协方差平均值滤波,迭代修正观测噪声协方差,实现机器人同步定位精度的在线提高.实验表明,该算法可降低观测噪声先验信息不足的影响,减小定位误差.     

未知环境下机器人障碍物检测技术

未知环境下地面的不平坦性和机器人相对障碍物位姿的不确定性会造成障碍物特征提取困难,为了准确检测障碍物特征,采用3维相机获取机器人周围环境的灰度图像和距离信息。在此基础上,提出基于灰度信息和3维信息的"阈值法"进行障碍物区域的提取,并针对3维信息"阈值法"剔除的地面与障碍物过渡区域过多,以及机器人相对斜坡方位的不确定性引起的障碍物特征检测不准确,提出区域的恢复算法和表面法线坡度计算。实验结果表明,所提出的算法具有简单、有效、准确和鲁棒性强的优点。     

基于激光雷达的无人驾驶汽车动态障碍物检测、跟踪与识别方法

现有的基于几何特征的动态障碍物检测跟踪方法误检率较高,基于动态障碍物几何特征和运动状态的识别方法受距离和扫描角度影响较大,无法满足真实交通场景应用的要求.针对这些不足,本文提出了一种基于多特征融合的动态障碍物检测与跟踪方法和一种基于时空特征向量的动态障碍物识别方法.首先在动态障碍物几何特征的基础上,考虑障碍物回波脉冲宽度特征,以提高障碍物检测跟踪的正确率;其次,基于障碍物时间维度与空间维度信息来构建时空特征向量,并进而采用支持向量机方法实现动态障碍物的识别,以提升障碍物识别的准确率.最后,通过实车试验对所提出方法的准确性和有效性进行了验证.     

基于稀疏点云分割的适应视角变化的场景识别方法

在机器人自主导航中,同时定位与建图负责感知周围环境并定位自身位姿,为后续的高级任务提供感知支撑。场景识别作为其中的关键模块,可以帮助机器人更加准确地感知周围环境,它通过识别当前的观测和之前的观测是否属于同一个场景来校正传感器硬件固有误差导致的误差累积。现有的方法主要关注稳定视角下的场景识别,根据两个观测之间的视觉相似性来判断它们是否属于同一个场景。然而,当观测视角发生变化时,同一个场景的观测可能存在较大的视觉差异,使得观测之间可能只是局部相似,进而导致传统方法失效,因此,一种基于稀疏点云分割的场景识别方法被提出。它将场景进行分割,以解决局部相似的问题,并且结合视觉信息和几何信息实现准确的场景描述和匹配,使得机器人能识别出不同视角下的相同场景,支撑单机的回环检测模块或多机的地图融合模块。该方法基于稀疏点云分割将每个观测分割为若干部分,分割结果对视角具有不变性,并且从每个分割部分中提取出局部词袋向量和β角直方图来准确描述其场景内容,前者包含场景的视觉语义信息,后者包含场景的几何结构信息。之后,基于分割部分匹配观测之间的相同部分,丢弃不同部分,实现准确的场景内容匹配,提高场景识别的成功率。最...     

人体行为识别的Markov随机游走半监督学习方法

针对目前人体行为识别方法大都需要大量有标注样本的问题,提出一种基于Markov随机游走的半监督人体行为识别算法.首先提取序列图像各帧人体区域的网格统计特征,再采用基于对手惩罚策略的竞争神经网络对其进行聚类和编码,将图像序列表示的人体行为变换为符号序列;然后根据行为之间的归一化编辑距离建立已标注行为、未标注行为和类别之间的Markov链,并采用Markov随机游走过程来预测未标注行为的类别;最后采用最大后验概率准则对观测到的未知行为进行分类.对Weizmann数据集中人体行为的识别实验结果表明,该方法是一种有效的人体行为识别方法,在标注样本很少的情况下平均识别精度可以超过80％.     

基于视觉的机器人自主定位与障碍物检测方法

针对稀疏型同时定位与地图构建（SLAM）算法环境信息丢失导致无法检测障碍物问题，提出一种基于视觉的机器人自主定位与障碍物检测方法。首先，利用双目相机得到观测场景的视差图。然后，在机器人操作系统（ROS）架构下，同时运行定位与建图和障碍物检测两个节点。定位与建图节点基于ORB-SLAM2完成位姿估计与环境建图。障碍物检测节点引入深度阈值，将视差图二值化；运用轮廓提取算法得到障碍物轮廓信息并计算障碍物凸包面积；再引入面积阈值，剔除误检测区域，从而实时准确地解算出障碍物坐标。最后，将检测到的障碍物信息插入到环境的稀疏特征地图当中。实验结果表明，该方法能够在实现机器人自主定位的同时，快速检测出环境中的障碍物，检测精度能够保证机器人顺利避障。     

基于深度强化学习的无地图移动机器人导航

针对传统导航方法对地图精度依赖和动态复杂场景适应差问题,提出一种基于课程学习的深度强化学习无地图自主导航算法.为了克服智能体稀疏奖励情况下学习困难的问题,借鉴课程学习思想,提出一种基于能力圈课程引导的深度强化学习训练方法.此外,为了更好地利用机器人当前的碰撞信息辅助机器人做动作决策,引入碰撞概率的概念,将机器人当前感知到的障碍物信息以一种高层语义的形式进行表示,并将其作为导航策略输入的一部分编码至机器人当前观测中,以简化观测到动作的映射,进一步降低学习的难度.实验结果表明,所提出的课程引导训练和碰撞概率可令导航策略收敛速度明显加快,习得的导航策略在空间更大的场景成功率到达90%以上,行驶耗时减少53.5%sim73.1%,可为非结构化未知环境下的无人化作业提供可靠导航.     

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.     

A Novel Approach for Mobile Robot Navigation with Dynamic Obstacles Avoidance

This paper proposes a new approach for trajectory optimization of a mobile robot in a general dynamic environment. The new method combines the static and dynamic modes of trajectory planning to provide an algorithm that gives fast and optimal solutions for static environments, and generates a new path when an unexpected situation occurs. The particularity of the method is in the representation of the static environment in a judicious way facilitating the path planning and reducing the processing time. Moreover, when an unexpected obstacle blocks the robot trajectory, the method uses the robot sensors to detect the obstacle, finds a best way to circumvent it and then resumes its path toward the desired destination. Experimental results showed the effectiveness of the proposed approach.     

Fuzzy integral-based gaze control architecture incorporated with modified-univector field-based navigation for humanoid robots

When a humanoid robot moves in a dynamic environment, a simple process of planning and following a path may not guarantee competent performance for dynamic obstacle avoidance because the robot acquires limited information from the environment using a local vision sensor. Thus, it is essential to update its local map as frequently as possible to obtain more information through gaze control while walking. This paper proposes a fuzzy integral-based gaze control architecture incorporated with the modified-univector field-based navigation for humanoid robots. To determine the gaze direction, four criteria based on local map confidence, waypoint, self-localization, and obstacles, are defined along with their corresponding partial evaluation functions. Using the partial evaluation values and the degree of consideration for criteria, fuzzy integral is applied to each candidate gaze direction for global evaluation. For the effective dynamic obstacle avoidance, partial evaluation functions about self-localization error and surrounding obstacles are also used for generating virtual dynamic obstacle for the modified-univector field method which generates the path and velocity of robot toward the next waypoint. The proposed architecture is verified through the comparison with the conventional weighted sum-based approach with the simulations using a developed simulator for HanSaRam-IX (HSR-IX).     

一种基于多组传感器信息移动机器人的避障方法

提出了一种基于多组传感器信息的移动机器人避障的新方法.该方法把多组传感 器信息作为ART-2神经网络的输入,实现移动机器人对当前感知环境的快速识别和分类.在 此基础上,设计了用于移动机器人在未知环境下避障的模糊控制器.试验证明了这一方法的 有效性和实时性.     

基于多传感器的家庭服务机器人局部导航方法研究

本文提出了一种基于多传感器的家庭服务机器人局部导航方法。首先,采用单个摄像头获取居室内障碍物的图像信息,利用超声波传感器和红外线传感器探测障碍物的距离信息。然后,据此计算在机器人运动方向上障碍物的遮挡空间或者多个障碍物之间的实际距离,再根据机器人自身的大小计算避开障碍物应该转动的方向及角度,从而实现居室内的自主导航。最后,仿真实验结果证明了该方法的有效性。     

A bayesian approach to real-time obstacle avoidance for a mobile robot

Real-time obstacle avoidance is essential for the safe operation of mobile robots in a dynamically changing environment. This paper investigates how an industrial mobile robot can respond to unexpected static obstacles while following a path planned by a global path planner. The obstacle avoidance problem is formulated using decision theory to determine an optimal response based on inaccurate sensor data. The optimal decision rule minimises the Bayes risk by trading between a sidestep maneuver and backtracking to follow an alternative path. Real-time implementation is emphasised here as part of a framework for real world applications. It has been successfully implemented both in simulation and in reality using a mobile robot.     

基于红外传感皮肤的多关节机器人自主运动方法研究

本文研究了障碍环境下多关节机器人自主实时避碰运动理论、技术与方法. 研制的新型红外传感皮肤, 可以为多关节机器人提供所需要的周围环境信息. 针对非结构化环境下的多关节机器人实时避障问题, 提出了一种未知环境下的机器人模糊路径实时规划新方法. 实验结果表明: 基于研制的红外传感皮肤和模糊运动规划算法, 多关节机器人可以在未知或时变环境下自主工作.     

Vision-Based Obstacle Detection and Avoidance: Application to Robust Indoor Navigation of Mobile Robots

We propose a more practical and efficient method for obstacle detection and avoidance. In this paper, a robot detects obstacles based on the projective invariants of stereo cameras, fuses this information with two-dimensional scanning sensor data, and finally builds up a more informative and conservative occupancy map. Although this approach is not supposed to recognize the exact shape of the obstacles, this shortcoming is overcome in the actual application by its fast calculation time and robustness against the illumination conditions. To avoid detected obstacles, a new reactive obstacle avoidance strategy is also presented. To evaluate the proposed method, we applied it to the mobile robot iMARO-III. In this test, iMARO-III has succeeded in long-term operation for 7 days continuously without any intervention of engineers and any collision in the real office environment.     

A method for combining odometry and distance sensor information for effective obstacle avoidance of autonomous mobile robots

In this paper, a concept for virtual sensors is proposed for efficient avoidance of obstacles during the motion of robots. The virtual sensor yields new data by combining encoder values and real distance data, and derives new sensor data that includes the mobility of the robot. Simulation on Windows XP is executed to illustrate the proposed approach with actually acquired distance from virtual and actual sensors. To facilitate comparison with the alternative results developed in this paper, we refer to the conventional artificial potential field method using actual distance. Data from virtual sensors show smoother and safer motion in obstacle avoidance traces in regards to obstacle and robot mobility.