Planning Robot Placement for Object Grasping

TL;DR

This paper introduces a novel robot placement planning method for object grasping that prioritizes collision-free and feasible positions before evaluating their effectiveness, improving grasp success in complex environments.

Contribution

The paper presents a new approach that finds feasible robot placements based on environmental data and reachability, reducing reliance on costly grasp planners and enhancing grasp success.

Findings

The approach enables grasping from challenging locations.

Experimental results outperform a fixed navigation baseline.

The method effectively integrates environmental perception and reachability.

Abstract

When performing manipulation-based activities such as picking objects, a mobile robot needs to position its base at a location that supports successful execution. To address this problem, prominent approaches typically rely on costly grasp planners to provide grasp poses for a target object, which are then are then analysed to identify the best robot placements for achieving each grasp pose. In this paper, we propose instead to first find robot placements that would not result in collision with the environment and from where picking up the object is feasible, then evaluate them to find the best placement candidate. Our approach takes into account the robot's reachability, as well as RGB-D images and occupancy grid maps of the environment for identifying suitable robot poses. The proposed algorithm is embedded in a service robotic workflow, in which a person points to select the target object for grasping. We evaluate our approach with a series of grasping experiments, against an existing baseline implementation that sends the robot to a fixed navigation goal. The experimental results show how the approach allows the robot to grasp the target object from locations that are very challenging to the baseline implementation.