Yurii Stasinchuk

This paper presents a novel approach for autonomous cooperating UAVs in search and rescue operations in subterranean domains with complex topology. The proposed system was ranked second in the Virtual Track of the DARPA SubT Finals as part of the team CTU-CRAS-NORLAB. In contrast to the winning solution that was developed specifically for the Virtual Track, the proposed solution also proved to be a robust system for deployment onboard physical UAVs flying in the extremely harsh and confined environment of the real-world competition. The proposed approach enables fully autonomous and decentralized deployment of a UAV team with seamless simulation-to-world transfer, and proves its advantage over less mobile UGV teams in the flyable space of diverse environments. The main contributions of the paper are present in the mapping and navigation pipelines. The mapping approach employs novel map representations — SphereMap for efficient risk-aware long-distance planning, FacetMap for surface coverage, and the compressed topological-volumetric LTVMap for allowing multi-robot cooperation under low-bandwidth communication. These representations are used in navigation together with novel methods for visibility-constrained informed search in a general 3D environment with no assumptions about the environment structure, while balancing deep exploration with sensor-coverage exploitation. The proposed solution also includes a visual-perception pipeline for on-board detection and localization of objects of interest in four RGB stream at 5 Hz each without a dedicated GPU. Apart from participation in the DARPA SubT, the performance of the UAV system is supported by extensive experimental verification in diverse environments with both qualitative and quantitative evaluation.

The Mohamed Bin Zayed International Robotics Challenge (MBZIRC) is a prestigious, biennial competition aimed at furthering the state of the art in the field of autonomous robotics. In this paper, we present our solution to one of the tasks in the MBZIRC 2020 competition, which design won second place in Challenge 1 and first place in the Grand Challenge of the competition. This paper focuses specifically on the popping task of multiple balloons by multiple Micro Aerial Vehicles (MAVs). This task required a rapid and robust performance to compete with systems from other expert robotic teams from around the world. In this task, a team of autonomous MAV’s had to seek and attack several balloons positioned throughout the competition arena. The novel fast autonomous searching for multiple targets in 3D, their reliable detection, precise relative state estimation, and agile motion planning algorithms are presented in this paper, together with an application for general tasks of 3D target capturing. With a primary focus on reliability, the methods reported in this paper and the entire, complex multi-agent system were successfully verified in both extreme conditions of the desert and the MBZIRC competition. An evaluation of the proposed methods using data from the competition and additional separate datasets is presented. The relevant code of our implementation has been made publicly available for the robotics community.

In this paper, a novel approach for autonomously catching fast flying objects is presented, as inspired by the Mohamed Bin Zayed International Robotics Challenge (MBZIRC) 2020. In this competition, an autonomous Unmanned Aerial Vehicle (UAV) was used to intercept a ball carried by a fast flying drone. The presented solution utilizes a 3D LiDAR sensor for quick and robust target detection. The trajectory of the target is estimated and predicted to select a suitable interception position. The interceptor UAV is navigated into the interception position to safely approach the target. The interception position is frequently being adjusted based on the updated estimation and prediction of the target’s motion to ensure that the ball is caught in the dedicated onboard net. After a successful interception is detected, the UAV lands in a designated landing area. The proposed concept was intensively tested and refined in demanding outdoor conditions with strong winds and varying perception conditions to achieve the robustness required by both the demanding application and the competition. In the MBZIRC 2020 competition, our solution scored second place in Challenge 1 and first place in a combined Grand Challenge. This manuscript will provide a detailed description of the applied methods and an evaluation of our approach with data collected from real-world experiments. In addition, we present achievements of our R&D towards the transition from the MBZIRC competition to an autonomous drone interceptor, which was the main motivation of this challenge.

The problem of safe interception of multiple intruder UAVs by a team of cooperating autonomous aerial vehicles is addressed in this paper. The presented work is motivated by the Mohamed Bin Zayed International Robotics Challenge (MBZIRC) 2020 where this task was simplified to an interaction with a set of static and dynamic objects (balloons and a UAV), and by a real autonomous aerial interception system of Eagle.One that our team has been working on. We propose a general control, perception, and coordination system for the fast and reliable interception of targets in a 3D environment relying only on onboard sensors and processing. The proposed methods and the entire complex multi-robot sys- tem were successfully verified in demanding desert conditions, with the main focus on reliability and fast deployment. In the MBZIRC competition, the proposed approach exhibited the greatest reliability and fastest solution. It was crucial to our team in winning the entire competition and achieving the second place in the intruder UAV interception scenario.