Ing. Viktor Walter

This paper presents a family of autonomous Unmanned Aerial Vehicles (UAVs) platforms designed for a diverse range of indoor and outdoor applications. The proposed UAV design is highly modular in terms of used actuators, sensor configurations, and even UAV frames. This allows to achieve, with minimal effort, a proper experimental setup for single, as well as, multi-robot scenarios. Presented platforms are intended to facilitate the transition from simulations, and simplified laboratory experiments, into the deployment of aerial robots into uncertain and hard-to-model real-world conditions. We present mechanical designs, electric configurations, and dynamic models of the UAVs, followed by numerous recommendations and technical details required for building such a fully autonomous UAV system for experimental verification of scientific achievements. To show strength and high variability of the proposed system, we present results of tens of completely different real-robot experiments in various environments using distinct actuator and sensory configurations.

An optical inter-agent communication integrated into a relative localization system designed for the stabilization of teams of Unmanned Aerial Vehicles (UAVs) is introduced in this paper. We propose an alternative optical communication channel using UV light as a physical transmission medium in free space. The proposed communication system is suitable for implicit short-range inter-agent communication. It is robust against channel saturation and radio jamming that are bottlenecks of radio communication commonly used within aerial swarms. The proposed localization-communication system UVDAR-COM was verified in simulations and real-world experiments. Additionally, we present a simulated experiment showing the performance of the UVDAR-COM system within a decentralized swarm application.

The compact flocking of relatively localized Un- manned Aerial Vehicles (UAVs) in high obstacle density areas is discussed in this paper. The presented work tackles realistic scenarios in which the environment map is not known apriori and the use of a global localization system and communication infrastructure is difficult due to the presence of obstacles. To achieve flocking in such a constrained environment, we propose a fully decentralized, bio-inspired control law that uses only onboard sensor data for safe flocking through the environment without any communication with other agents. In the proposed approach, each UAV agent uses onboard sensors to self-localize and estimate the relative position of other agents in its local reference frame. The usability and performance of the proposed approach were verified and evaluated using various experiments in a realistic robotic simulator and a natural forest. The pre- sented experiments also validate the utility of onboard relative localization for autonomous multi-UAV applications in the ab- sence of global localization information and communication.

This paper presents a novel approach to autonomous extinguishing of indoor fires inside a building by a Micro-scale Unmanned Aerial Vehicle (MAV). In particular, controlling and estimating the MAV state, detection of a building entrance, multi-modal MAV localization during the outdoor-indoor transition, interior motion planning and exploration, fire detection and position estimation, and fire extinguishing are discussed. The performance of these elements, as well as of the entire integrated system, are evaluated in simulations and field tests in various demanding real-world conditions. The system presented here is part of a complex multi-MAV solution that won the Mohamed Bin Zayed International Robotics Challenge 2020 (MBZIRC 2020) competition, and is being used as the core of a fire-fighting Unmanned Aerial System (UAS) industrial platform under development. A video attachment to this paper is available at the website http://mrs.felk.cvut.cz/2020firechallenge-insidefires.

This article presents a unique framework for deploying decentralized and infrastructure-independent swarms of homogeneous aerial vehicles in the real world without explicit communication. This is a requirement in swarm research, which anticipates that global knowledge and communication will not scale well with the number of robots. The system architecture proposed in this article employs the ultraviolet direction and ranging technique to directly perceive the local neighborhood for direct mutual localization of swarm members. The technique allows for decentralization and high scalability of swarm systems, such as can be observed in fish schools, bird flocks, or cattle herds. The bio-inspired swarming model that has been developed is suited for real-world deployment of large particle groups in outdoor and indoor environments with obstacles. The collective behavior of the model emerges from a set of local rules based on direct observation of the neighborhood using onboard sensors only. The model is scalable, srequires only local perception of agents and the environment, and requires no communication among the agents. Apart from simulated scenarios, the performance and usability of the entire framework is analyzed in several real-world experiments with a fully-decentralized swarm of unmanned aerial vehicles (UAVs) deployed in outdoor conditions. To the best of our knowledge, these experiments are the first deployment of decentralized bio-inspired compact swarms of UAVs without the use of a communication network or shared absolute localization. The entire system is available as open-source athttps://github.com/ctu-mrs.

In this paper, a system for autonomous extinguishing of ground fires using the placement of fire blankets by Multi-rotor Unmanned Aerial Vehicles (UAVs) is proposed. The proposed system, relying on the fusion of multiple onboard sensors using only onboard computers, is infrastructure independent with a focus on high reliability in safety-critical missions that require power-on-and-go full autonomy. This task was part of the third challenge of MBZIRC 2020 aimed at the development of autonomous robotic systems for extinguishing fires inside and outside of buildings. The MBZIRC competition promotes the development of such robotics applications that are highly demanded by society and, due to their complexity and required robot abilities, go beyond the current robotic state of the art. As far as we are aware, our team was one of only two teams to achieve successful system for placement of fire blankets fully autonomously with vision-based target localization without using Real-time kinematic (RTK)-global navigation satellite system (GNSS), as was required in the competition and also for the real missions of first responders.

A novel approach for achieving fast evasion in self-localized swarms of unmanned aerial vehicles (UAVs) threatened by an intruding moving object is presented in this paper. Motivated by natural self-organizing systems, the presented approach of fast and collective evasion enables the UAV swarm to avoid dynamic objects (interferers) that are actively approaching the group. The main objective of the proposed technique is the fast and safe escape of the swarm from an interferer discovered in proximity. This method is inspired by the collective behavior of groups of certain animals, such as schools of fish or flocks of birds. These animals use the limited information of their sensing organs and decentralized control to achieve reliable and effective group motion. The system presented in this paper is intended to execute the safe coordination of UAV swarms with a large number of agents. Similar to natural swarms, this system propagates a fast shock of information about detected interferers throughout the group to achieve dynamic and collective evasion. The proposed system is fully decentralized using only onboard sensors to mutually localize swarm agents and interferers, similar to how animals accomplish this behavior. As a result, the communication structure between swarm agents is not overwhelmed by information about the state (position and velocity) of each individual and it is reliable to communication dropouts. The proposed system and theory were numerically evaluated and verified in real-world experiments.

By leveraging our relative Micro-scale Unmanned Aerial Vehicle localization sensor UVDAR, we generated an automatically annotated dataset MIDGARD, which the community is invited to use for training and testing their machine learning systems for the detection and localization of Microscale Unmanned Aerial Vehicles (MAVs) by other MAVs. Furthermore, we provide our system as a mechanism for rapidly generating custom annotated datasets specifically tailored for the needs of a given application. The recent literature is rich in applications of machine learning methods in automation and robotics. One particular subset of these methods is visual object detection and localization, using means such as Convolutional Neural Networks, which nowadays enable objects to be detected and classified with previously inconceivable precision and reliability. Most of these applications, however, rely on a carefully crafted training dataset of annotated camera footage. These must contain the objects of interest in environments similar to those where the detector is expected to operate. Notably, the positions of the objects must be provided in annotations. For non-laboratory settings, the construction of such datasets requires many man-hours of manual annotation, which is especially the case for use onboard Micro-scale Unmanned Aerial Vehicles. In this paper, we are providing for the community a practical alternative to that kind of approach.

A novel onboard relative localization method, based on ultraviolet light, used for real-time control of a leader-follower formation of multirotor UAVs is presented in this paper. A new smart sensor, UVDAR, is employed in an innovative way, which does not require communication and is extremely reliable in real-world conditions. This innovative sensing system exploits UV spectrum and provides relative position and yaw measurements independently of environment conditions such as changing illumination and presence of undesirable light sources and their reflections. The proposed approach exploits this retrieved information to steer the follower to a given 3D position and orientation relative to the leader, which may be considered as the main building block of any multi-UAV system operating with small mutual distances among team-members. The proposed solution was verified in demanding outdoor conditions, validating usage of UVDAR in real flight scenario and paving the way for further usage of UVDAR for practical multi-UAV formation deployments.

This paper proposes a new methodology for outdoor mutual relative localization of UAVs equipped with active ultraviolet markers and a suitable camera with specialized bandpass filters. Mutual relative localization is a crucial tool for formation preservation, swarming and cooperative task completion in scenarios in which UAVs share working space in small relative distances. In most current systems of compact UAV swarms the localization of particular UAVs is based on the data obtained from motion capture systems for indoor experiments or on precise RTK-GNSS data outdoor. Such an external infrastructure is unavailable in most of real multi-UAV applications and often cannot be pre-installed. To account for such situations, as well as to make the system more autonomous, reliance on onboard sensors only is desirable. In the proposed approach, we rely on ultraviolet LED markers, that emit light in frequencies that are less common in nature than the visible light or infrared radiation, especially in high intensities. Additionally, common camera sensors are sensitive to ultraviolet light, making the addition of a filter the only necessary modification, keeping the platform low-cost, which is one of the key requirements on swarm systems. This also allows for a smaller size of the markers to be sufficient, without burdening the processing resources. Thus the proposed system aspires to be an enabling technology for deployment of large swarms of possibly micro-scale aerial vehicles in real-world conditions and without any dependency on an external infrastructure.

A novel vision-based approach for indoor/outdoor mutual localization on Unmanned Aerial Vehicles (UAVs) with low computational requirements and without external infrastructure is proposed in this paper. The proposed solution exploits the low natural emissions in the near-Ultra-Violet (UV) spectrum to avoid major drawbacks of the visible spectrum. Such approach provides much better reliability while being less computationally intensive. Working in near-UV requires active markers, which can be leveraged by enriching the information content through blinking patterns encoded marker-ID. In order to track the markers motion and identify their blinking frequency, we propose an innovative use of three dimensional Hough Transform, applied to stored position-time points. The proposed method was intensively tested onboard multi-UAV systems in real-world scenarios that are very challenging for visible-spectrum methods.The results of our methods in terms of robustness, reliability and precision, as well as the low requirement on the system deployment, predestine this method to be an enabling technology for using swarms of UAVs.

This paper proposes and evaluates the implementation of a self-localization system intended for use in Unmanned Aerial Vehicles. Accurate localization is necessary for UAVs for efficient stabilization, navigation and collision avoidance. Conventionally, this requirement is fulfilled using external hardware infrastructure, such as Global Navigation Satellite System or visual motion-capture system. These approaches are, however, not applicable in environments where deployment of cumbersome motion capture equipment is not feasible, as well as in GNSS-denied environments. Systems based on Simultaneous Localization and Mapping (SLAM) require heavy and expensive onboard equipment and high amounts of data transmissions for sharing maps between UAVs. Availability of a system without these drawbacks is crucial for deployment of tight formations of multiple fully autonomous micro UAVs for both outdoor and indoor missions. The project was inspired by the often used sensor PX4FLOW Smart Camera. The aim was to develop a similar sensor, without the drawbacks observed in its use, as well as to make the operation of it more transparent and to make it independent of a specific hardware. Our proposed solution requires only a lightweight camera and a single-point range sensor. It is based on optical flow estimation from consecutive images obtained from downward-facing camera, coupled with a specialized RANSAC-inspired post-processing method that takes into account flight dynamics. This filtering makes it more robust against imperfect lighting, homogenous ground patches, random close objects and spurious errors. These features make this approach suitable even for coordinated flights through demanding forest-like environment. The system is designed mainly for horizontal velocity estimation, but specialized modifications were also made for vertical speed and yaw rotation rate estimation. These methods were tested in a simulator and subsequently in real-world conditions.