From Sensor-Space to Eigenspace – A Novel Real-Time Obstacle Avoidance Method for Mobile Robots
From Sensor-Space to Eigenspace – A Novel Real-Time Obstacle Avoidance Method for Mobile Robots
复制标题
从传感器空间到特征空间——一种新型的移动机器人实时避障方法
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
Imthias Ahamed Thythodath Paramabath
中科院分区：
文献类型：
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作者：
S. Zaheer;T. Gulrez;Imthias Ahamed Thythodath Paramabath
提出了一种新的实时避障导航技术--自由构型特征空间(FCE)。FCE使自主机器人能够检测未知障碍物并避免碰撞，同时引导机器人朝向目标。该方法使用二维(2D)笛卡尔特征空间作为世界模型。通过计算(通过FCE模型)离散时间扫描的激光数据的堆叠特征向量来提取(在世界模型中)2D路点，这些特征向量显示了期望的机器人轨迹。在获得由机载光探测和测距(LiDAR)传感器采样的离散时间激光扫描后，世界模型不断更新。FCE技术已在机器人操作系统和实时机器人模拟器Gazebo上实现。文中给出了一些令人鼓舞的结果。FCE还通过安装在定制自主机器人上的VL6180X LiDAR传感器获得的激光扫描进行了实时测试。并与目前最先进的矢量场直方图(VFH)避障路径规划方法进行了比较。FCE的结果(如本文所示)在路径长度和安全距离维护方面具有可比性、鼓舞人心，并且优于VFH。
This paper presents a novel real-time obstacle avoidance and navigation technique called as “Free-configuration Eigenspace” (FCE). The FCE enables an autonomous robot to detect unknown obstacles and avoid collisions while simultaneously steering the robot towards the target. The methodology utilizes a two-dimensional (2D) Cartesian Eigenspace as a world model. 2D way points (in world model) are extracted by computing (through FCE model) stacked Eigenvectors of laser data at discrete time scans which manifest desired robotic trajectory. The world model is updated continuously upon obtaining discrete time laser scans sampled by on-board Light Detection and Ranging (LiDAR) sensors. The FCE technique has been implemented on Robotic Operating System and real-time robotics simulator Gazebo . Encouraging results are obtained and shown in this paper. The FCE has also been tested in real-time with laser scans obtained from VL6180X LiDAR sensor mounted on a custom autonomous robot. The results obtained are further compared with the state-of-the-art obstacle avoidance and path planning method called as vector field histogram (VFH). The FCE results (shown in the paper) are comparable, encouraging, and outperform VFH in path length and safe distance maintenance.