Abstract
The Mohamed Bin Zayed International Robotics Challenge (MBZIRC) held in spring 2017 was a very successful competition well attended by teams from all over the world. One of the challenges (Challenge 1) required an aerial robot to detect, follow, and land on a moving target in a fully autonomous fashion. In this paper, we present the hardware components of the micro air vehicle (MAV) we built with off the self components alongside the designed algorithms that were developed for the purposes of the competition. We tackle the challenge of landing on a moving target by adopting a generic approach, rather than following one that is tailored to the MBZIRC Challenge 1 setup, enabling easy adaptation to a wider range of applications and targets, even indoors, since we do not rely on availability of global positioning system. We evaluate our system in an uncontrolled outdoor environment where our MAV successfully and consistently lands on a target moving at a speed of up to 5.0 m/s.
Original language | English |
---|---|
Pages (from-to) | 49-77 |
Number of pages | 29 |
Journal | Journal of Field Robotics |
Volume | 36 |
Issue number | 1 |
Early online date | 27 Oct 2018 |
DOIs | |
Publication status | Published - 1 Jan 2019 |
Keywords
- autonomous landing
- micro aerial vehicles
- model based control
- visual-inertial estimation
ASJC Scopus subject areas
- Control and Systems Engineering
- Computer Science Applications
Fingerprint
Dive into the research topics of 'Fully autonomous micro air vehicle flight and landing on a moving target using visual–inertial estimation and model‐predictive control'. Together they form a unique fingerprint.Profiles
-
Wenbin Li
- Department of Computer Science - Senior Lecturer
- Artificial Intelligence and Machine Learning
- Visual Computing
- UKRI CDT in Accountable, Responsible and Transparent AI
- Centre for Digital, Manufacturing & Design (dMaDe)
- Centre for Regenerative Design & Engineering for a Net Positive World (RENEW)
- IAAPS: Propulsion and Mobility
- Bath Institute for the Augmented Human
Person: Research & Teaching, Core staff, Affiliate staff