Frame-Based Slip Detection for an Underactuated Robotic Gripper for Assistance of Users with Disabilities

Stable grasping is essential for assistive robots aiding individuals with severe motor–sensory disabilities in their everyday lives. Slip detection can prevent unstably grasped objects from falling out of the gripper and causing accidents. Recent research on slip detection focuses on tactile sensing; however, not every robot arm can be equipped with such sensors. In this paper, we propose a slip detection method solely based on data collected by a RealSense D435 Red Green Blue-Depth (RGBd) camera. By utilizing Farneback optical flow (OF) to estimate the motion field of the grasped object relative to the gripper, while also removing potential background noise, the algorithm can perform in a multitude of environments. The algorithm was evaluated on a dataset of 28 daily objects that were lifted 30 times each, resulting in a total of 840 frame sequences. Our proposed slip detection method achieves an accuracy of up to 82.38% and a recall of up to 87.14%, which is comparable to state-of-the-art approaches when only using camera data. When excluding objects for which movements are challenging for vision-based methods to detect, such as untextured or transparent objects, the proposed method performs even better, with an accuracy of up to 87.19% and a recall of up to 95.09%.</p

Visual SLAM and Moving-object Detection for a Small-size Humanoid Robot

100學年度研究獎補助論文[[abstract]]In the paper, a novel moving object detection (MOD) algorithm is developed and integrated with robot visual Simultaneous Localization and Mapping (vSLAM). The moving object is assumed to be a rigid body and its coordinate system in space is represented by a position vector and a rotation matrix. The MOD algorithm is composed of detection of image features, initialization of image features, and calculation of object coordinates. Experimentation is implemented on a small-size humanoid robot and the results show that the performance of the proposed algorithm is efficient for robot visual SLAM and moving object detection.[[incitationindex]]SCI[[incitationindex]]EI[[booktype]]電子版[[booktype]]紙