A decentralized multi-agent unmanned aerial system to search, pick up, and relocate objects

Abstract

We present a fully integrated autonomous multirobot aerial system for finding and collecting moving and static objects with unknown locations. This task addresses multiple relevant problems in search and rescue (SAR) robotics such as multi-agent aerial exploration, object detection and tracking, and aerial gripping. Usually, the community tackles these problems individually but the integration into a working system generates extra complexity which is rarely addressed. We show that this task can be solved reliably using only simple components. Our decentralized system uses accurate global state estimation, reactive collision avoidance, and sweep planning for multi-agent exploration. Objects are detected, tracked, and picked up using blob detection, inverse 3D-projection, Kalman filtering, visual-servoing, and a magnetic gripper. We evaluate the individual components of our system on the real platform. The full system has been deployed successfully in various public demonstrations, field tests, and the Mohamed Bin Zayed International Robotics Challenge 2017 (MBZIRC). Among the contestants we showed reliable performances and reached second place out of 17 in the individual challenge.

Team NimbRo at MBZIRC 2017: Fast landing on a moving target and treasure hunting with a team of micro aerial vehicles

“…The ETH Zürich MBZIRC team "Electronic Treasure Hunters" also describe their approach to solving the challenge (Bähnemann, Schindler, Kamel, Siegwart, & Nieto, 2017). They employ an electropermanent magnetic gripper and color blob detection for visual servoing, placing second in both Challenge 3 and the Grand Challenge.…”

Robust 3D Distributed Formation Control With Collision Avoidance And Application To Multirotor Aerial Vehicles

“…Thanks to technological advances in recent years, it is now possible to deploy a team of unmanned aerial vehicles to collaboratively map and monitor an environment [1], inspect infrastructures [2], deliver goods [3], or manipulate objects [4]. In these applications, the ability to bring the vehicles to a desired geometric shape (i.e., formation) is a fundamental building block upon which more sophisticated maneuvering and navigation policies can be constructed.…”

Cooperative autonomous search, grasping, and delivering in a treasure hunt scenario by a team of unmanned aerial vehicles

“…Solutions for the Treasure Hunt scenario have already been presented by two teams participating in the MBZIRC competition which had worked on this scenario autonomously. The approach used by the team from ETH Zurich is described in Bähnemann, Schindler, Kamel, Siegwart, and Nieto (2017), and the approach used by the team from the University of Bonn is presented in Nieuwenhuisen et al (2017). Both teams relied on an electropermanent magnetic gripper for grasping ferrous objects, which are recognized using a color blob detection algorithm.…”

“…First, the arena is cooperatively searched by UAVs to create a map of the objects, and then an attempt is made to grasp and deliver each object in the map. However, the solution in Bähnemann et al () relies on a Wi‐Fi communication infrastructure, and the authors do not propose any alternative in the event of communication blackout. They also do not explain how they solve the problem of multiple UAVs coordination over the drop‐off zone.…”