|
The emerging application of robots in household scenarios provides a promising solution for daily housekeeping, warehousing and healthcare. In such scenarios, the robot needs capabilities for environment perception, object searching, navigation, and object manipulation. However, existing solutions often focus on a single functionality and cannot handle complex and unseen environments, imposing a huge barrier to collaboratively processing information in complex environments. In this paper, we propose a holistic robot control framework that allows a robot to percept, search, navigate, and grasp target objects at specified locations, providing a complete solution with the major functionalities for smart home robots. To tackle the issue of complex background interference in the environment, we developed a perception module that combines instance segmentation with point cloud clustering to extract spatial information of the target object. Based on the spatial information obtained by the perception module, we constructed an object-searching algorithm that uses related objects as clues that accelerate the object-searching process in unseen environments. In addition, a new grasp detection model is presented that explicitly considers the information of both the objects and backgrounds to reduce the interference of background on grasp detection, improving the grasp success ratio. Results from simulation experiments demonstrate that the proposed approach significantly outperforms the baseline method in terms of both efficiency and success rate.
|
