Estimation of the Grasping Pose of Unknown Objects Based onMultiple Geometric Constraints

Aiming at the problem of grasping unknown objects quickly and stably by robots in unstructured environment, a method based on multiple geometric constraints is proposed to estimate the pose of unknown objects. Firstly, the geometric point cloud information of the scene is acquired by depth camera. Target object can be obtained after point cloud preprocessing, and the sample of the grasping pose is generated by simplified geometric constraint of the grasper. With the constraint of simplified force-closure, a quick and coarse screening of samples is performed. After the force balance constraint on the geometric profile of the grasping pose is analyzed, a stable pose is transmitted to the robot to perform grasping. Using a 6 degree of freedom (6-DoF) robotic manipulator with a depth camera, experiments are conducted to grasp objects with different postures and shapes. Results show that the proposed method can effectively deal with the situation that various objects exist and their three-dimensional models are unknown. It also has good applicability in both single-target and multi-target scenarios.