Vision-Based Robotic Pushing and Grasping for Stone Sample Collection under Computing Resource Constraints

Increasing the robustness of grasping actions and the recovery from failure is key to improving a robot's autonomy. Endowing robots with the ability to robustly grasp and manipulate unknown difficult objects such as stones is required for sample collection in unknown environments. In this paper, we present a complete system for robust grasping of stones, which integrates stone segmentation based on depth information, the generation of grasp hypotheses and pushing actions as well as their execution. In particular, our system has been designed to solve these tasks on robots with limited computing resources. We evaluate the performance in real robot experiments in the context of stone sample collection. The results show that such a challenging task is achievable under computing resource constraints.