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.

中文翻译：

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增强型AMCL高精度定位与动态跟踪抓取相结合的移动操控


移动操纵比固定底座操纵具有更大的灵活性，一直是机器人领域的重要课题。然而，对于复杂环境下的精细操作，高效定位和动态跟踪掌握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器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纵方法。首先，通过融合激光反射器地标的信息来调整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来有效跟踪和抓取动态目标。然后，在室内环境中实现了将上述两种算法集成到具有六自由度（DOF）操作臂的机器人中的移动操纵系统。定位、多目标检测、动态跟踪抓取和集成系统等技术组件均在现实场景中得到验证。实验结果证明了我们方法的有效性和优越性。