When performing manipulation-based activities such as picking objects, a mobile robot needs to position its base at a location that supports successful execution. To address this problem, prominent approaches typically rely on costly grasp planners to provide grasp poses for a target object, which are then are then analysed to identify the best robot placements for achieving each grasp pose. In this paper, we propose instead to first find robot placements that would not result in collision with the environment and from where picking up the object is feasible, then evaluate them to find the best placement candidate. Our approach takes into account the robot's reachability, as well as RGB-D images and occupancy grid maps of the environment for identifying suitable robot poses. The proposed algorithm is embedded in a service robotic workflow, in which a person points to select the target object for grasping. We evaluate our approach with a series of grasping experiments, against an existing baseline implementation that sends the robot to a fixed navigation goal. The experimental results show how the approach allows the robot to grasp the target object from locations that are very challenging to the baseline implementation.

翻译：在执行诸如抓取物体等基于操作的活动时，移动机器人需要将其底座定位在能够支持成功执行的位置。为解决此问题，现有主流方法通常依赖于昂贵的抓取规划器来为目标物体提供抓取姿态，然后分析这些姿态以确定实现每个抓取姿态的最佳机器人放置位置。在本文中，我们提出了一种替代方案：首先寻找不会与环境发生碰撞且能够实现物体拾取的机器人放置位置，然后对这些位置进行评估以确定最佳候选放置点。我们的方法综合考虑了机器人的可达性、环境的RGB-D图像以及占据栅格地图，以识别合适的机器人位姿。所提出的算法被嵌入到一个服务机器人工作流程中，在该流程中，操作人员通过指向来选择待抓取的目标物体。我们通过一系列抓取实验评估了我们的方法，并与将机器人发送至固定导航目标的现有基线实现进行了对比。实验结果表明，该方法使机器人能够从基线实现难以应对的位置成功抓取目标物体。