Manipulation Planning and Control for Shelf Replenishment
This addresses the problem of automating complex in-store logistics tasks for retail robots, representing an incremental improvement in manipulation planning and control.
The paper tackles the challenge of robotic shelf replenishment in supermarkets by integrating reactive grasping control with motion planning, enabling robots to successfully perform manipulation tasks that were previously unfeasible with standard fixed grasps.
Manipulation planning and control are relevant building blocks of a robotic system and their tight integration is a key factor to improve robot autonomy and allows robots to perform manipulation tasks of increasing complexity, such as those needed in the in-store logistics domain. Supermarkets contain a large variety of objects to be placed on the shelf layers with specific constraints, doing this with a robot is a challenge and requires a high dexterity. However, an integration of reactive grasping control and motion planning can allow robots to perform such tasks even with grippers with limited dexterity. The main contribution of the paper is a novel method for planning manipulation tasks to be executed using a reactive control layer that provides more control modalities, i.e., slipping avoidance and controlled sliding. Experiments with a new force/tactile sensor equipping the gripper of a mobile manipulator show that the approach allows the robot to successfully perform manipulation tasks unfeasible with a standard fixed grasp.