Autonomous, Mobile Manipulation in a Wall-building Scenario: Team LARICS at MBZIRC 2020

TL;DR

This paper details the design and control strategies of an autonomous mobile manipulation system for wall-building, combining UAVs and a UGV in outdoor construction tasks, emphasizing perception, planning, and manipulation in unstructured environments.

Contribution

It introduces a novel integrated control framework and hardware design for autonomous brick manipulation in outdoor construction scenarios, including a passively-compliant electromagnetic gripper with tactile feedback.

Findings

Successful autonomous brick pick-and-place in competition

Effective perception and localization using RGB-D, LIDAR, IMU, GPS

Robust control approach with state machine and trajectory optimization

Abstract

In this paper we present our hardware design and control approaches for a mobile manipulation platform used in Challenge 2 of the MBZIRC 2020 competition. In this challenge, a team of UAVs and a single UGV collaborate in an autonomous, wall-building scenario, motivated by construction automation and large-scale robotic 3D printing. The robots must be able, autonomously, to detect, manipulate, and transport bricks in an unstructured, outdoor environment. Our control approach is based on a state machine that dictates which controllers are active at each stage of the Challenge. In the first stage our UGV uses visual servoing and local controllers to approach the target object without considering its orientation. The second stage consists of detecting the object's global pose using OpenCV-based processing of RGB-D image and point-cloud data, and calculating an alignment goal within a global map. The map is built with Google Cartographer and is based on onboard LIDAR, IMU, and GPS data. Motion control in the second stage is realized using the ROS Move Base package with Time-Elastic Band trajectory optimization. Visual servo algorithms guide the vehicle in local object-approach movement and the arm in manipulating bricks. To ensure a stable grasp of the brick's magnetic patch, we developed a passively-compliant, electromagnetic gripper with tactile feedback. Our fully-autonomous UGV performed well in Challenge 2 and in post-competition evaluations of its brick pick-and-place algorithms.