摘要
为解决室内机器人正向准确避障问题,提出一种光流结合特征提取的视觉避障方法。将LK光流与多尺度思想结合,加入仿射变换,提高算法追踪角点的噪声鲁棒性,进而准确检测视场中的障碍物;通过评估障碍物风险程度,制定出碰撞时间(TTC)结合光流平衡策略的碰撞机制,引导机器人在无碰路径上移动。改进光流与特征提取结合能提高相机快速运动下的追踪成功率,制定的碰撞策略能够有效规避机器人前方的障碍物。实验表明,相机快速运动时,算法能提高追踪的准确性,有效检测出视场中的障碍物,引导机器人无碰撞行驶,具备较强的独立性和实时性。
In order to solve the forward obstacle avoidance problem of indoor robots,a visual obstacle avoidance method based on optical flow and feature extraction is proposed.LK optical flow is combined with multi-scale thought,and affine transformation is added to improve the noise robustness of tracking corner points,so as to accurately detect obstacles in the field of view.By assessing the risk degree of obstacles,a collision mechanism based on collision time(TTC)combined with an optical flow balancing strategy was developed to guide the robot to move on a collision-free path.The combination of improved optical flow and feature extraction can improve the tracking success rate of the camera under fast motion,and the collision strategy formulated can effectively avoid the obstacles in front of the robot.Experiments show that the algorithm can improve tracking accuracy,effectively detect obstacles in the field of view,and guide the robot to drive without collision when the camera moves quickly,with strong independence and real-time performance.
作者
曹梦龙
石梦鸽
CAO Menglong;SHI Mengge(College of Automation and Electronic Engineering,Qingdao University of Science and Technology,Qingdao 266042,China)
出处
《组合机床与自动化加工技术》
北大核心
2024年第3期31-35,共5页
Modular Machine Tool & Automatic Manufacturing Technique
基金
山东省自然科学基金项目(ZR2020MF087)。
关键词
角点
光流
碰撞时间
平衡策略
避障
corners
optical flow
time-to-collision
balancing strategy
obstacle avoidance
