Building the executive system of autonomous aerial robots using the Aerostack open-source framework

in International Journal of Advanced Robotic Systems (2020), 17(3), 1--20

A variety of open-source software tools are currently available to help building autonomous mobile robots. These tools have proven their effectiveness in developing different types of robotic systems, but ... [more ▼]

A variety of open-source software tools are currently available to help building autonomous mobile robots. These tools have proven their effectiveness in developing different types of robotic systems, but there are still needs related to safety and efficiency that are not sufficiently covered. This article describes recent advances in the Aerostack software framework to address part of these needs, which may become critical in the case of aerial robots. The article describes a software tool that helps to develop the executive system, an important component of the control architecture whose characteristics significantly affect the quality of the final autonomous robotic system. The presented tool uses an original solution for execution control that aims at simplifying mission specification and protecting against errors, considering also the efficiency needs of aerial robots. The effectiveness of the tool was evaluated by building an experimental autonomous robot. The results of the evaluation show that it provides significant benefits about usability and reliability with acceptable development effort and computational cost. The tool is based on Robot Operating System and it is publicly available as part of the last release of the Aerostack software framework (version 3.0). [less ▲]

Vision-based multirotor following using synthetic learning techniques

in Sensors (2019), 19(21), 1--20

Deep-and reinforcement-learning techniques have increasingly required large sets of real data to achieve stable convergence and generalization, in the context of image-recognition, object-detection or ... [more ▼]

Deep-and reinforcement-learning techniques have increasingly required large sets of real data to achieve stable convergence and generalization, in the context of image-recognition, object-detection or motion-control strategies. On this subject, the research community lacks robust approaches to overcome unavailable real-world extensive data by means of realistic synthetic-information and domain-adaptation techniques. In this work, synthetic-learning strategies have been used for the vision-based autonomous following of a noncooperative multirotor. The complete maneuver was learned with synthetic images and high-dimensional low-level continuous robot states, with deep-and reinforcement-learning techniques for object detection and motion control, respectively. A novel motion-control strategy for object following is introduced where the camera gimbal movement is coupled with the multirotor motion during the multirotor following. Results confirm that our present framework can be used to deploy a vision-based task in real flight using synthetic data. It was extensively validated in both simulated and real-flight scenarios, providing proper results (following a multirotor up to 1.3 m/s in simulation and 0.3 m/s in real flights). [less ▲]

Fast and Robust Flight Altitude Estimation of Multirotor UAVs in Dynamic Unstructured Environments Using 3D Point Cloud Sensors

in Aerospace (2018), 5(3),

This paper presents a fast and robust approach for estimating the flight altitude of multirotor Unmanned Aerial Vehicles (UAVs) using 3D point cloud sensors in cluttered, unstructured, and dynamic indoor ... [more ▼]

This paper presents a fast and robust approach for estimating the flight altitude of multirotor Unmanned Aerial Vehicles (UAVs) using 3D point cloud sensors in cluttered, unstructured, and dynamic indoor environments. The objective is to present a flight altitude estimation algorithm, replacing the conventional sensors such as laser altimeters, barometers, or accelerometers, which have several limitations when used individually. Our proposed algorithm includes two stages: in the first stage, a fast clustering of the measured 3D point cloud data is performed, along with the segmentation of the clustered data into horizontal planes. In the second stage, these segmented horizontal planes are mapped based on the vertical distance with respect to the point cloud sensor frame of reference, in order to provide a robust flight altitude estimation even in presence of several static as well as dynamic ground obstacles. We validate our approach using the IROS 2011 Kinect dataset available in the literature, estimating the altitude of the RGB-D camera using the provided 3D point clouds. We further validate our approach using a point cloud sensor on board a UAV, by means of several autonomous real flights, closing its altitude control loop using the flight altitude estimated by our proposed method, in presence of several different static as well as dynamic ground obstacles. In addition, the implementation of our approach has been integrated in our open-source software framework for aerial robotics called Aerostack. [less ▲]

Laser-Based Reactive Navigation for Multirotor Aerial Robots using Deep Reinforcement Learning

Scientific Conference (2018)

Navigation in unknown indoor environments with fast collision avoidance capabilities is an ongoing research topic. Traditional motion planning algorithms rely on precise maps of the environment, where re ... [more ▼]

Navigation in unknown indoor environments with fast collision avoidance capabilities is an ongoing research topic. Traditional motion planning algorithms rely on precise maps of the environment, where re-adapting a generated path can be highly demanding in terms of computational cost. In this paper, we present a fast reactive navigation algorithm using Deep Reinforcement Learning applied to multi rotor aerial robots. Taking as input the 2D-laser range measurements and the relative position of the aerial robot with respect to the desired goal, the proposed algorithm is successfully trained in a Gazebo-based simulation scenario by adopting an artificial potential field formulation. A thorough evaluation of the trained agent has been carried out both in simulated and real indoor scenarios, showing the appropriate reactive navigation behavior of the agent in the presence of static and dynamic obstacles. [less ▲]

Stereo Visual Odometry and Semantics based Localization of Aerial Robots in Indoor Environments

Scientific Conference (2018)

In this paper we propose a particle filter localization approach, based on stereo visual odometry (VO) and semantic information from indoor environments, for mini-aerial robots. The prediction stage of ... [more ▼]

In this paper we propose a particle filter localization approach, based on stereo visual odometry (VO) and semantic information from indoor environments, for mini-aerial robots. The prediction stage of the particle filter is performed using the 3D pose of the aerial robot estimated by the stereo VO algorithm. This predicted 3D pose is updated using inertial as well as semantic measurements. The algorithm processes semantic measurements in two phases; firstly, a pre-trained deep learning (DL) based object detector is used for real time object detections in the RGB spectrum. Secondly, from the corresponding 3D point clouds of the detected objects, we segment their dominant horizontal plane and estimate their relative position, also augmenting a prior map with new detections. The augmented map is then used in order to obtain a drift free pose estimate of the aerial robot. We validate our approach in several real flight experiments where we compare it against ground truth and a state of the art visual SLAM approach. [less ▲]

A Multi-Layered Component-Based Approach for the Development of Aerial Robotic Systems: The Aerostack Framework

in Journal of Intelligent and Robotic Systems (2017), 88(2), 638-709

To achieve fully autonomous operation for Unmanned Aerial Systems (UAS) it is necessary to integrate multiple and heterogeneous technical solutions (e.g., control-based methods, computer vision methods ... [more ▼]

To achieve fully autonomous operation for Unmanned Aerial Systems (UAS) it is necessary to integrate multiple and heterogeneous technical solutions (e.g., control-based methods, computer vision methods, automated planning, coordination algorithms, etc.). The combination of such methods in an operational system is a technical challenge that requires efficient architectural solutions. In a robotic engineering context, where productivity is important, it is also important to minimize the effort for the development of new systems. As a response to these needs, this paper presents Aerostack, an open-source software framework for the development of aerial robotic systems. This framework facilitates the creation of UAS by providing a set of reusable components specialized in functional tasks of aerial robotics (trajectory planning, self localization, etc.) together with an integration method in a multi-layered cognitive architecture based on five layers: reactive, executive, deliberative, reflective and social. Compared to other software frameworks for UAS, Aerostack can provide higher degrees of autonomy and it is more versatile to be applied to different types of hardware (aerial platforms and sensors) and different types of missions (e.g. multi robot swarm systems). Aerostack has been validated during four years (since February 2013) by its successful use on many research projects, international competitions and public exhibitions. As a representative example of system development, this paper also presents how Aerostack was used to develop a system for a (fictional) fully autonomous indoors search and rescue mission. [less ▲]

Visual Marker based Multi-Sensor Fusion State Estimation

in IFAC-PapersOnLine (2017, July), 50(1), 16003-16008

A flight altitude estimator for multirotor UAVs in dynamic and unstructured indoor environments

in 2017 International Conference on Unmanned Aircraft Systems (ICUAS) (2017, June)

Towards fully autonomous landing on moving platforms for rotary Unmanned Aerial Vehicles

Scientific Conference (2017)

Fully autonomous landing on moving platforms poses a problem of importance for Unmanned Aerial Vehicles (UAVs). Current approaches are usually based on tracking and following the moving platform by means ... [more ▼]

Fully autonomous landing on moving platforms poses a problem of importance for Unmanned Aerial Vehicles (UAVs). Current approaches are usually based on tracking and following the moving platform by means of several techniques, which frequently lack performance in real applications. The aim of this paper is to prove a simple landing strategy is able to provide practical results. The presented approach is based on three stages: estimation, prediction and fast landing. As a preliminary phase, the problem is solved for a particular case of the IMAV 2016 competition. Subsequently, it is extended to a more generic and versatile approach. A thorough evaluation has been conducted with simulated and real flight experiments. Simulations have been performed utilizing Gazebo 6 and PX4 Software-In-The-Loop (SITL) and real flight experiments have been conducted with a custom quadrotor and a moving platform in an indoor environment. [less ▲]

A reliable open-source system architecture for the fast designing and prototyping of autonomous multi-uav systems: Simulation and experimentation

in Journal of Intelligent and Robotic Systems (2016), 84(1-4), 779-797

During the process of design and development of an autonomous Multi-UAV System, two main problems appear. The first one is the difficulty of designing all the modules and behaviors of the aerial multi ... [more ▼]

During the process of design and development of an autonomous Multi-UAV System, two main problems appear. The first one is the difficulty of designing all the modules and behaviors of the aerial multi-robot system. The second one is the difficulty of having an autonomous prototype of the system for the developers that allows to test the performance of each module even in an early stage of the project. These two problems motivate this paper. A multipurpose system architecture for autonomous multi-UAV platforms is presented. This versatile system architecture can be used by the system designers as a template when developing their own systems. The proposed system architecture is general enough to be used in a wide range of applications, as demonstrated in the paper. This system architecture aims to be a reference for all designers. Additionally, to allow for the fast prototyping of autonomous multi-aerial systems, an Open Source framework based on the previously defined system architecture is introduced. It allows developers to have a flight proven multi-aerial system ready to use, so that they can test their algorithms even in an early stage of the project. The implementation of this framework, introduced in the paper with the name of ``CVG Quadrotor Swarm'', which has also the advantages of being modular and compatible with different aerial platforms, can be found at \url{https://github.com/Vision4UAV/cvg_quadrotor_swarm} with a consistent catalog of available modules. The good performance of this framework is demonstrated in the paper by choosing a basic instance of it and carrying out simulation and experimental tests whose results are summarized and discussed in this paper. [less ▲]