Robotics Institute, School of Mechanical Engineering and Automation, Beihang University, Beijing 100191, China.
State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing 100191, China.
Sensors (Basel). 2020 Nov 23;20(22):6697. doi: 10.3390/s20226697.
Mobile manipulation, which has more flexibility than fixed-base manipulation, has always been an important topic in the field of robotics. However, for sophisticated operation in complex environments, efficient localization and dynamic tracking grasp still face enormous challenges. To address these challenges, this paper proposes a mobile manipulation method integrating laser-reflector-enhanced adaptive Monte Carlo localization (AMCL) algorithm and a dynamic tracking and grasping algorithm. First, by fusing the information of laser-reflector landmarks to adjust the weight of particles in AMCL, the localization accuracy of mobile platforms can be improved. Second, deep-learning-based multiple-object detection and visual servo are exploited to efficiently track and grasp dynamic objects. Then, a mobile manipulation system integrating the above two algorithms into a robotic with a 6-degrees-of-freedom (DOF) operation arm is implemented in an indoor environment. Technical components, including localization, multiple-object detection, dynamic tracking grasp, and the integrated system, are all verified in real-world scenarios. Experimental results demonstrate the efficacy and superiority of our method.
移动操作比固定基座操作更具灵活性，一直是机器人领域的一个重要课题。然而，对于复杂环境中的复杂操作，高效的定位和动态跟踪抓取仍然面临着巨大的挑战。为了解决这些挑战，本文提出了一种集成激光反射增强自适应蒙特卡罗定位（AMCL）算法和动态跟踪抓取算法的移动操作方法。首先，通过融合激光反射地标信息来调整 AMCL 中粒子的权重，可以提高移动平台的定位精度。其次，利用基于深度学习的多目标检测和视觉伺服技术，实现对动态目标的高效跟踪和抓取。然后，将上述两种算法集成到具有 6 自由度（DOF）操作臂的机器人中，构建了一个移动操作系统，并在室内环境中进行了验证。定位、多目标检测、动态跟踪抓取以及集成系统等技术组件都在实际场景中得到了验证。实验结果证明了我们方法的有效性和优越性。
