Motion Planning and Safe Object Handling for a Low-Resource Mobile Manipulator as Human Assistant

Nowadays, mobile manipulators can support humans in daily life tasks while sharing the same workspace. These robots are usually requested to perform pick-and-place actions that involve objects that must be handled with care, since they may hurt the human operators. To optimize their utility as smart assistants, they require autonomous grasping pose generation, object recognition, and pose estimation capabilities. In addition, since they work in dynamic environments, adaptability is essential, hence predefined starting positions for grasping actions should be avoided. These demands are even more challenging for robots with limited computational capabilities. In this paper, we propose an approach that demonstrates how to improve the capabilities of a low-resource mobile manipulator. First, an easy way to model the robot as a unique system for holistic motion planning is developed. Then, we propose a lightweight approach to generate the grasping point to pick a requested item that relies only on the available CPU. Finally, a simple yet flexible solution that involves human feedback is adopted to let the robot handle potentially dangerous objects, while ensuring the operator's safety. The proposed solution has been developed in ROS1 and experimentally tested on the LoCoBot mobile manipulator in a laboratory environment.