Home helper robots have become more acceptable due to their excellent image recognition ability. However, some common household tools remain challenging to recognize, classify, and use by robots. We designed a detection method for the functional components of common household tools based on the mask regional convolutional neural network (Mask-R-CNN). This method is a multitask branching target detection algorithm that includes tool classification, target box regression, and semantic segmentation. It provides accurate recognition of the functional components of tools. The method is compared with existing algorithms on the dataset UMD Part Affordance dataset and exhibits effective instance segmentation and key point detection, with higher accuracy and robustness than two traditional algorithms. The proposed method helps the robot understand and use household tools better than traditional object detection algorithms.

The health condition of servo turret depends machining quality of CNC lathe. Performance tests of servo turret are taken to evaluate the health condition and overall status during its service life. This paper selects the core signals during one tool change-cutting cycle from multichannel physical signals and describes the performance of tool change-cutting cycle by defining a series of feature matrixes. In order to clearly and visually express the health condition of different working stages and tool position, a multidimensional features map is designed according to the combination of different time sequence feature matrixes. The above method is applied on a servo turret test platform which is equipped to acquire abundant signals. The result shows that the feature map clearly defines the health condition with one easily interpreted picture instead of unarranged signals. The proposed method provides a new idea of feature selection and visual expression, which can be an instructive tool for health evaluation and fault isolation.