Manipulation Planning and Control for Shelf Replenishment

TL;DR

This paper presents a novel manipulation planning method integrating reactive control for shelf replenishment tasks, enabling robots to handle complex object placement with limited dexterity and improved control modalities.

Contribution

The paper introduces a new planning approach combining reactive grasping control with motion planning for improved manipulation in logistics.

Findings

Successful manipulation of objects with limited dexterity

Enhanced control modalities like slipping avoidance and controlled sliding

Robust performance demonstrated with new force/tactile sensors

Abstract

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.