摘要
针对基于视觉的机械臂抓取精确抓取的需求,考虑传统的视觉识别算法受环境、对象变化的制约且在识别正确率及快速性上存在的问题,在现有研究的基础上,提出了一种基于深度学习的目标精确检测与识别方法。首先基于深度学习改进了YOLO算法,通过对数据集的训练,基于英伟达Jetson TX1高性能处理单元实现了复杂环境下多目标的识别与定位,给出了目标的类别与位置等信息;以此为基础,结合利用Move It!功能包完成的机械臂运动轨迹的求解与规划,以及基于李群李代数建立的递推正逆动力学模型,设计了机械臂抓取控制的滑模控制律。仿真及实物验证表明,基于深度神经网络的方法学习到的特征对复杂背景具有较强的鲁棒性和稳定性;所设计的滑模控制算法在0.21 s时跟踪误差在2%,取得了较高的控制精度。可为后续视觉抓取任务提供参考。
The precise capture of the robotic arm plays a very important role in actual military or industrial production. Vision is widely used because of its high efficiency,low price,etc. Therefore,robotic arm grabbing based on vision has gradually become a research hotspot in this field. However,the traditional visual recognition algorithms are restricted by the changes of the environment and the objects. At the same time,there exist recognition accuracy and rapidity problems. Based on the existing researches,an algorithm based on deep learning was proposed in this paper to accurately detect and identify the target so as to improve the recognition ability in a complex environment. First,the YOLO algorithm was improved by deep learning. Based on the high-performance processing unit of Nvidia Jetson TX1,the identification and location of multi-targets in a complex environment were achieved through data sets training,and the information such as the category and location of the target were given. Then,based on the solution and planning of the trajectory of the robot arm solved by the Move It! Function Package,and the efficient recursive and inverse dynamic model established according to Lie groups and Lie algebra theory,the sliding mode control algorithm for robotic arm grab control was designed. The simulation and physical verification showed that compared with the traditional image recognition algorithm,the features learned from the deep neural network method have stronger robustness and stability for the complex background. The tracking error of the designed sliding mode control algorithm was 2% at 0. 21 s and with high control accuracy which may provide relevant references for subsequent visual grabbing tasks.
引文
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