Tomas Baca

System for deployment of groups of unmanned micro aerial vehicles in GPS-denied environments using onboard visual relative localization

A complex system for control of swarms of micro aerial vehicles (MAV), in literature also called as unmanned aerial vehicles (UAV) or unmanned aerial systems (UAS), stabilized via an onboard visual relative localization is described in this paper. The main purpose of this work is to verify the possibility of self-stabilization of multi-MAV groups without an external global positioning system. This approach enables the deployment of MAV swarms outside laboratory conditions, and it may be considered an enabling technique for utilizing fleets of MAVs in real-world scenarios. The proposed visual-based stabilization approach has been designed for numerous different multi-UAV robotic applications (leader-follower UAV formation stabilization, UAV swarm stabilization and deployment in surveillance scenarios, cooperative UAV sensory measurement) in this paper. Deployment of the system in real-world scenarios truthfully verifies its operational constraints, given by limited onboard sensing suites and processing capabilities. The performance of the presented approach (MAV control, motion planning, MAV stabilization, and trajectory planning) in multi-MAV applications has been validated by experimental results in indoor as well as in challenging outdoor environments (e.g., in windy conditions and in a former pit mine).

Embedded model predictive control of unmanned micro aerial vehicles

We propose a lightweight embedded system for stabilization and control of Unmanned Aerial Vehicles (UAVs) and particularly Micro Aerial Vehicles (MAVs). The system relies solely on onboard sensors to localize the MAV, which makes it suitable for experiments in GPS-denied environments. The system utilizes predictive controllers to find optimal control actions for the aircraft using only onboard computational resources. To show the practicality of the proposed system, we present several indoor and outdoor experiments with multiple quadrotor helicopters which demonstrate its capability of trajectory tracking and disturbance rejection.

Miniaturized X-ray telescope for VZLUSAT-1 nanosatellite with Timepix detector

We present the application of a Timepix detector on the VZLUSAT-1 nanosatellite. Timepix is a compact pixel detector (256×256 square pixels, 55×55 μm each) sensitive to hard X-ray radiation. It is suitable for detecting extraterrestrial X-rays due to its low noise characteristics, which enables measuring without special cooling. This project aims to verify the practicality of the detector in conjunction with 1-D Lobster-Eye optics to observe celestial sources between 5 and 20 keV. A modified USB interface (developed by IEAP at CTU in Prague) is used for low-level control of the Timepix. An additional 8-bit Atmel microcontroller is dedicated for commanding the detector and to process the data onboard the satellite. We present software methods for onboard post-processing of captured images, which are suitable for implementation under the constraints of the low-powered embedded hardware. Several measuring modes are prepared for different scenarios including single picture exposure, solar UV-light triggered exposure, and long-term all-sky monitoring. The work has been done within Medipix2 collaboration. The satellite is planned for launch in April 2017 as a part of the QB50 project with an end of life expectancy in 2019.

Localization and stabilization of micro aerial vehicles based on visual features tracking

This article presents a method for long-term autonomous micro-aerial vehicle (MAV) localization and position stabilization. The proposed method extends MAV proprietary stabilization based on inertial sensor or optical flow processing, without use of an external positioning system. The method extracts visual features from the images captured by a down-looking camera mounted under the MAV and matching these to previously observed features. Due to its precision and reliability, the method is well suited for stabilization of MAVs acting in closely cooperating compact teams with small mutual distances between team members. Performance of the proposed method is demonstrated by experiments on a quad-copter equipped with all necessary sensors and computers for the autonomous operation.

X-ray Lobster Eye all-sky monitor for rocket experiment

This paper presents a Lobster Eye (LE) X-ray telescope developed for the Water Recovery X-ray Rocket (WRX-R) experiment. The primary payload of the rocket experiment is a soft X-ray spectroscope developed by the Pennsylvania State University (PSU), USA. The Czech team participates by hard LE X-ray telescope as a secondary payload. The astrophysical objective of the rocket experiment is the Vela Supernova of size about 8deg x 8deg. In the center of the nebula is a neutron star with a strong magnetic field, roughly the mass of the Sun and a diameter of about 20 kilometers forming the Vela pulsar. The primary objective of WRX-R is the spectral measurement of the outer part of the nebula in soft X-ray and FOV of 3.25deg x 3.25deg. The secondary objective (hard LE X-ray telescope) is the Vela neutron star observation. The hard LE telescope consists of two X-ray telescopes with the Timepix detector. First telescope uses 2D LE Schmidt optics (2DLE- REX) with focal length over 1m and 4 Timepix detectors (2x2 matrix). The telescope FOV is 1.5deg x 1.5deg with spectral range from 3keV to 60keV. The second telescope uses 1D LE Schmidt optics (1D-LE-REX) with focal length of 25 cm and one Timepix detector. The telescope is made as a wide field with FOV 4.5deg x 3.5deg and spectral range from 3keV to 40keV. The rocket experiment serves as a technology demonstration mission for the payloads. The LE X-ray telescopes can be in the future used as all‐sky monitor/surveyor. The astrophysical observation can cover the hard X-ray observation of astrophysical sources in time-domain, the GRBs surveying or the exploration of the gravitational wave sources.

Complex manoeuvres of heterogeneous MAV-UGV formations using a model predictive control

A problem of motion planning and coordination of compact formations of ground and aerial robots will be tackled in this paper. The scenarios when the formation composed from Unmanned Ground Vehicles (UGVs) and Unmanned Aerial Vehicles (UAVs), in particular Micro Aerial Vehicles (MAVs), has to reverse the direction of movement to fulfil task of collisionfree motion to a target zone will be solved. The presented motion planning and stabilization approach provides an effective technique to enable deployment of closely cooperating teams of robots in outdoor as well as indoor environment. The formation to target region problem is solved using a Model Predictive Control (MPC) methodology and the formation driving concept is based on a virtual-leader-follower approach. The mentioned MPC based process is used for trajectory planning and control of a virtual leader and also for control and stabilization of followers (MAVs and UGVs). The proposed approach is verified with numerous simulations and hardware experiments.

Autonomous landing on a moving car with unmanned aerial vehicle

This paper presents an implementation of a system that is autonomously able to find, follow and land on a car moving at 15 km/h. Our solution consists of two parts, the image processing for fast onboard detection of landing platform and the Model Predictive Control tracker for trajectory planning and control. This approach is fully autonomous using only the onboard computer and onboard sensors with differential GPS. Besides the description of the solution, we also present experimental results obtained at MBZIRC 2017 international competition.

Formations of unmanned micro aerial vehicles led by migrating virtual leader

A novel approach for control and motion planning of formations of multiple unmanned micro aerial vehicles (MAVs), also referred to as unmanned aerial vehicles (UAVs) — multirotor helicopters, in cluttered GPS-denied environments is presented in this paper. The proposed method enables autonomously to design complex maneuvers of a compact MAV team in a virtual-leader-follower scheme. The feasibility of obtained results of the motion planning approach and the required stability of the formation is achieved by migrating the virtual leader along a hull surrounding the formation. This enables us to suddenly change formation motion in all directions, independently of actual orientation of the formation.

On solution of the Dubins touring problem

The Dubins traveling salesman problem (DTSP) combines the combinatorial optimization of the optimal sequence of waypoints to visit the required target locations with the continuous optimization to determine the optimal headings at the waypoints. Existing decoupled approaches to the DTSP are based on an independent solution of the sequencing part as the Euclidean TSP and finding the optimal headings of the waypoints in the sequence. In this work, we focus on the determination of the optimal headings in a given sequence of waypoints and formulate the problem as the Dubins touring problem (DTP). The DTP can be solved by a uniform sampling of possible headings; however, we propose a new informed sampling strategy to find approximate solution of the DTP. Based on the presented results, the proposed algorithm quickly converges to a high-quality solution, which is less than 0.1% from the optimum. Besides, the proposed approach also improves the solution of the DTSP, and its feasibility has been experimentally verified in a real practical deployment.