Force-Based Simultaneous Mapping and Object Reconstruction for Robotic Manipulation
Force-Based Simultaneous Mapping and Object Reconstruction for Robotic Manipulation
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DOI：
10.1109/lra.2022.3152244
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发表时间：
2022-04
影响因子：
5.2
通讯作者：
João Bimbo;A. S. Morgan;A. Dollar
中科院分区：
文献类型：
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作者：
João Bimbo;A. S. Morgan;A. Dollar
这封信提出了一种通过物理交互同时重建未知物体及其环境的几何形状的算法。涉及高度混乱或封闭的工作空间的应用程序会妨碍视觉的有效使用。为了解决出现的一些挑战，我们提出了一种方法，利用机器人末端执行器的力和扭矩测量来解决可能的接触位置，并从概率上确定 3D 地图上的占用可能性。我们的程序构建了两个占用图：一个固定的代表环境，另一个地图与机器人末端执行器一起移动并重建抓取的物体形状，其中每个地图都通知另一个地图的概率更新。该算法在两种场景下进行应用和测试：从场景中检索缠结的对象并重建对象的几何形状。我们将结果与配置空间规划器和强化学习算法进行比较，我们的方法需要更少的与环境的碰撞来提取对象。
This letter presents an algorithm to simultaneously reconstruct the geometry of an unknown object and its environment via physical interactions. Applications involving highly cluttered or occluded workspaces prevent the effective use of vision. To address some of the challenges that arise, we propose an approach that instead utilizes force and torque measurements at the robot end-effector to solve for possible contact locations and probabilistically determine the occupancy likelihood on a 3D map. Our procedure constructs two occupancy maps: one fixed that represents the environment and another map that moves with the robot end-effector and reconstructs the grasped object shape, where each map informs the probability updates on the other. The algorithm is applied and tested on two scenarios: retrieving a tangled object from a scene and reconstructing the geometry of an object. We compare the results against a configuration space planner and a reinforcement learning algorithm, with our method requiring fewer collisions with the environment to extract the object.