31 research outputs found
An execution engine for aerial robot mission plans
The goal of the work presented in this paper is to develop a practical solution for mission plan execution to simplify the way in which operators configure the missions of robots. This work has been done to promote a more extensive use of the software framework for aerial robotics Aerostack. We have designed a computer system called execution engine that includes technical solutions from general robotics and artificial intelligence. The system follows a behavior-based approach and a symbolic representation of beliefs. The execution engine has been designed to be part of Aerostack but it can also work independently, so that it can be reused for building other type of robot architectures. This paper has been written as a specification and software design to be used as a guide for software implementation of the execution engine
Autonomous aerial robot for high-speed search and intercept applications
In recent years, high-speed navigation and environment interaction in the context of
aerial robotics has become a field of interest for several academic and industrial research studies. In
particular, Search and Intercept (SaI) applications for aerial robots pose a compelling research
area due to their potential usability in several environments. Nevertheless, SaI tasks involve a
challenging development regarding sensory weight, onboard computation resources, actuation design,
and algorithms for perception and control, among others. In this work, a fully autonomous aerial
robot for high-speed object grasping has been proposed. As an additional subtask, our system is able
to autonomously pierce balloons located in poles close to the surface. Our first contribution is the
design of the aerial robot at an actuation and sensory level consisting of a novel gripper design with
additional sensors enabling the robot to grasp objects at high speeds. The second contribution is
a complete software framework consisting of perception, state estimation, motion planning, motion
control, and mission control in order to rapidly and robustly perform the autonomous grasping
mission. Our approach has been validated in a challenging international competition and has shown
outstanding results, being able to autonomously search, follow, and grasp a moving object at 6 m/s
in an outdoor environment.Agencia Estatal de InvestigaciónKhalifa Universit
Aerostack2: A Software Framework for Developing Multi-robot Aerial Systems
In recent years, the robotics community has witnessed the development of
several software stacks for ground and articulated robots, such as Navigation2
and MoveIt. However, the same level of collaboration and standardization is yet
to be achieved in the field of aerial robotics, where each research group has
developed their own frameworks. This work presents Aerostack2, a framework for
the development of autonomous aerial robotics systems that aims to address the
lack of standardization and fragmentation of efforts in the field. Built on ROS
2 middleware and featuring an efficient modular software architecture and
multi-robot orientation, Aerostack2 is a versatile and platform-independent
environment that covers a wide range of robot capabilities for autonomous
operation. Its major contributions include providing a logical level for
specifying missions, reusing components and sub-systems for aerial robotics,
and enabling the development of complete control architectures. All major
contributions have been tested in simulation and real flights with multiple
heterogeneous swarms. Aerostack2 is open source and community oriented,
democratizing the access to its technology by autonomous drone systems
developers
Human-Robot Cooperation in Surface Inspection Aerial Missions
The goal of the work presented in this paper is to
facilitate the cooperation between human opera-
tors and aerial robots to perform surface inspec-
tion missions. Our approach is based on a model
of human collaborative control with a mixed ini-
tiative interaction. In the paper, we present our
human-robot cooperation model based on the
combination of a supervisory mode and an as-
sistance mode with a set of interaction patterns.
We developed a software system implementing
this interaction model and carried out several
real flight experiments that proved that this ap-
proach can be used in aerial robotics for sur-
face inspection missions (e.g., in vision based
indoor missions). Compared to a conventional
tele-operated inspection system, the solution pre-
sented in this paper gives more autonomy to the
aerial systems, reducing the cognitive load of the
operator during the mission development
The MRS UAV System: Pushing the Frontiers of Reproducible Research, Real-world Deployment, and Education with Autonomous Unmanned Aerial Vehicles
We present a multirotor Unmanned Aerial Vehicle control (UAV) and estimation
system for supporting replicable research through realistic simulations and
real-world experiments. We propose a unique multi-frame localization paradigm
for estimating the states of a UAV in various frames of reference using
multiple sensors simultaneously. The system enables complex missions in GNSS
and GNSS-denied environments, including outdoor-indoor transitions and the
execution of redundant estimators for backing up unreliable localization
sources. Two feedback control designs are presented: one for precise and
aggressive maneuvers, and the other for stable and smooth flight with a noisy
state estimate. The proposed control and estimation pipeline are constructed
without using the Euler/Tait-Bryan angle representation of orientation in 3D.
Instead, we rely on rotation matrices and a novel heading-based convention to
represent the one free rotational degree-of-freedom in 3D of a standard
multirotor helicopter. We provide an actively maintained and well-documented
open-source implementation, including realistic simulation of UAV, sensors, and
localization systems. The proposed system is the product of years of applied
research on multi-robot systems, aerial swarms, aerial manipulation, motion
planning, and remote sensing. All our results have been supported by real-world
system deployment that shaped the system into the form presented here. In
addition, the system was utilized during the participation of our team from the
CTU in Prague in the prestigious MBZIRC 2017 and 2020 robotics competitions,
and also in the DARPA SubT challenge. Each time, our team was able to secure
top places among the best competitors from all over the world. On each
occasion, the challenges has motivated the team to improve the system and to
gain a great amount of high-quality experience within tight deadlines.Comment: 28 pages, 20 figures, submitted to Journal of Intelligent & Robotic
Systems (JINT), for the provided open-source software see
http://github.com/ctu-mr
A Review of Deep Learning Methods and Applications for Unmanned Aerial Vehicles
Deep learning is recently showing outstanding results for solving a wide variety of robotic tasks in the areas of perception, planning, localization, and control. Its excellent capabilities for learning representations from the complex data acquired in real environments make it extremely suitable for many kinds of autonomous robotic applications. In parallel, Unmanned Aerial Vehicles (UAVs) are currently being extensively applied for several types of civilian tasks in applications going from security, surveillance, and disaster rescue to parcel delivery or warehouse management. In this paper, a thorough review has been performed on recent reported uses and applications of deep learning for UAVs, including the most relevant developments as well as their performances and limitations. In addition, a detailed explanation of the main deep learning techniques is provided. We conclude with a description of the main challenges for the application of deep learning for UAV-based solutions
BEHAVIORAL COMPOSITION FOR HETEROGENEOUS SWARMS
Research into swarm robotics has produced a robust library of swarm behaviors that excel at defined tasks such as flocking and area search, many of which have potential for application to a wide range of military problems. However, to be successfully applied to an operational environment, swarms must be flexible enough to achieve a wide array of specific objectives and usable enough to be configured and employed by lay operators. This research explored the use of the Mission-based Architecture for Swarm Composability (MASC) to develop mission-specific tactics as compositions of more general, reusable plays for use with the Advanced Robotic Systems Engineering Laboratory (ARSENL) swarm system. Three tactics were developed to conduct autonomous search of a geographic area and investigation of generated contacts of interest. The tactics were tested in live-flight and virtual environment experiments and compared to a preexisting monolithic behavior implementation completing the same task. Measures of performance were defined and observed that verified the effectiveness of solutions and confirmed the advantages that composition provides with respect to reusability and rapid development of increasingly complex behaviors.Lieutenant Commander, United States NavyApproved for public release. Distribution is unlimited