Autonomous Indoor Flight Research Articles

Object Fetching UAV using Autonomous Flight and Object Detection Algorithms

Abstract: Autonomous UAVs (Unmanned Aerial Vehicles) are being used in various applications around the world like surveillance and aerial security, construction, agriculture, delivery, etc. These applications require heavy duty UAVs with high payload capacity and long battery life. The aim of this project is to use the principles of autonomous flight and object detection for indoor autonomous flight with the target to fetch and deliver lightweight objects with minimum energy and time consumption to create a viable prototype for indoor applications like delivering objects to bedridden patients, smart home applications, warehouse management and more.

System Engineering-Based Conceptual Design of Indoor Navigation System of Unmanned Aerial Vehicles

Unmanned aerial vehicles (UAVs) have been become important day by day because of advantages in our life use cases; specially, they can play important role in the industry 4.0 concept such as automation in production plants. System engineering approach is planning the design and production phases. Every system, subsystem, component, or mission of the systems are investigated before project starts and each requirement is set. The investigation of compatibility between systems and products minimizes the risks during the production phases. This paper proposes the usage of system engineering approach to the conceptual design of an UAV navigation system which aims to be used in indoor environments. In order to provide autonomous indoor flight, system requirements are set by using system engineering approach. Then step by step, the basic functions that UAV needs to complete operation are specified. The required products are listed as a tree and the relation between products and functions are investigated. Thus, the conceptual design of the UAV indoor navigation system is demonstrated by using of the functional tree, the functions and product relation matrix, the product tree, and the use cases. Moreover, the system operational requirements are investigated, and the requirement operation phases, modes and use cases are also determined. Thanks to system engineering, all these complex systems are put together systematically, and the conceptual design is achieved.

Model Predictive Control-Based Multirotor Three-Dimensional Motion Planning with Point Cloud Obstacle

This study proposes a collision-free motion planning framework for indoor autonomous flight of multirotor unmanned aerial vehicle (UAV) based on the convex model predictive control (MPC) approach under a three-dimensional point cloud environment. The suggested framework is divided into three steps: full reference path generation, piecewise flight corridor (PFC) creation, and MPC-based motion planning. The framework begins with reconstructing boundary surfaces that can encapsulate the given point cloud in order to generate a full reference path by applying Dijkstra and Voronoi diagram algorithms. Then PFC that represents locally convex and feasible flight corridor is generated using the current vehicle state, triangulized obstacle, and full reference path. In such a way, the entire problem breaks down into a series of discretized convex motion planning problems whose solution can be found by applying MPC iteratively until the UAV reaches its final destination. The constraints of the MPC are set up with the dynamics of the UAV, PFC, and the performance limitation of the platform. The framework is verified with simulation under a MATLAB environment. As a result, the UAV can find the control variable needed to reach the final destination with the suggested framework. Also, the computational time of the suggested framework is shorter than those of full reference path optimization methods.

Use of a MEMS Differential Pressure Sensor to Detect Ground, Ceiling, and Walls on Small Quadrotors

Effective autonomous indoor flight requires an ability to sense and navigate ground, ceiling, and walls. While this can be accomplished in many ways including vision, one bio-inspired approach that is not well studied uses the detection of pressure changes when flying close to surfaces. As an example, an increase in pressure can be detected when a quadrotor flies close to the ground, a phenomenon known as ‘ground effect’. This work evaluates the viability of differential pressure measurements as a means of sensing ground, ceilings, and walls for small quadrotors. Extending existing models for thrust in ground effect, a model is derived to predict pressure changes due to ground effect in hover and tilt. The pressure response with ground effect is characterized across a range of distances, thrust levels, sensor locations, and quadrotor tilts using a Crazyflie 2.0 quadrotor and a small MEMS differential pressure sensor. Characterization experiments are also used to evaluate pressure measurements at varying distances from ceilings and walls, as well as the pressure behavior due to step changes in ground height. Manually controlled free flight tests are used to validate ground sensing in both nominal and visually challenging environments, as well as wall sensing in free flight.

A novel distributed architecture for UAV indoor navigation

Abstract In the last decade, different indoor flight navigation systems for small Unmanned Aerial Vehicles (UAVs) have been investigated, with a special focus on different configurations and on sensor technologies. The main idea of this paper is to propose a distributed Guidance Navigation and Control (GNC) system architecture, based on Robotic Operation System (ROS) for light weight UAV autonomous indoor flight. The proposed framework is shown to be more robust and flexible than common configurations. A flight controller and companion computer running ROS for control and navigation are also included in the section. Both hardware and software diagrams are given to show the complete architecture. Further works will be based on the experimental validation of the proposed configuration by indoor flight tests.

Potential Applications of UAV along the Construction's Value Chain

Building Information Modelling (BIM) is foreseen as a key element in the improvements of future construction's productivity. Currently, very few solutions allow the integration of as-built information into the model. This research explores the potential of Unmanned Aerial Vehicle (UAV) in linking BIM to the real world to improve the productivity. This paper presents a technical review of the main challenges in addressing the need mentioned above. Also, it presents potential use cases. The study concludes that two main areas are to be analysed in greater depth (1) autonomous indoor flight for a UAV and (2) the smart integration of collected data into the existing BIM software.

High-performance software control platform designed for the autonomous navigation of quad-rotor helicopter

In recent years, unmanned aerial vehicles have gained increasing popularity. They are employed in many applications due to its features in terms of flexibility, low cost, and small size. Due to special structure of mechanical system for the quad-rotor helicopter (typical small-sized unmanned aerial vehicle), one of the main research fields for quad-rotor helicopter is to improve its stability flight control so as to well implement its autonomous navigation. However, for the quad-rotor helicopter whose power is supplied by battery, the embedded operating system directly affects its flight performance; thus, it is of very importance to implement this goal. This article aims to put forward a high-performance software platform consisted of embedded operating system and JAVA virtual machine in the quad-rotor helicopter. Then, this article also proposes an autonomous control algorithm written by real-time JAVA application, which uses image entropy to avoid obstacles and optical flow to discover the most appropriate area to fly in while avoiding obstacles; besides, image identification with image contour detection is used for the cases of junction turn and landing. The practical experimental results show that the implemented software control platform and proposed autonomous flight application are component for indoor autonomous flight of quad-rotor helicopter.

Control system design of a quad-rotor with collision detection

Purpose The purpose of this paper was to present the process of building hardware and software for a collision avoidance system of a quadrotor capable of an indoor autonomous flight.

2A2-D02 FPGA-based image analysis and control-signal shaping for autonomous indoor flight(Aerial Robot and Mechatronics (2))

While it is possible to use GPS and pressure-sensors in an outdoor environment, using these methods to hold a certain altitude and position in confined spaces or GPS denied environments is not possible. In this paper, a method to analyze a camera picture and use this information to achieve a hold position and altitude task via FPGA is proposed. The camera signal is processed and converted into control signals by the FPGA and then overlaid with the operator signals. This enables our prototype a semi- or fully autonomous indoor flight.

Indoor autonomous flight of ornithopter using motion capture system

PurposeThe purpose of this paper is to perform an indoor autonomous flight of ornithopter using a novel motion capture system.Design/methodology/approachThe ornithopter platform has no on‐board sensors and processors for state estimation or feedback control. Instead, passive markers on the ornithopter body reflect IR light of the multiple strobing CCD cameras, and the position data of the markers are streamed to the ground station in almost real time. Control inputs such as wingbeat frequency and rudder angle are generated by the proportional feedback controllers which are implemented in the ground station and transmitted to the ornithopter. Due to the complexity and nonlinearity of aerodynamics and flexible multibody dynamics, the flight dynamics of ornithopter are difficult to realize in the closed form of state‐space system equations. A controller for stabilizing the flight state variables of ornithopter is not necessary to be implemented by means of flapping counter‐forces and torques which make ornithopter have inherent flight stability. The gains of controllers for following circular trajectories are obtained by a trial‐and‐error approach rather than a model‐based design.FindingsThe autonomous ornithopter successfully circulates the pre‐described radius with the constant altitude and the result shows that control strategies proposed in this study are sufficient to implement the autonomy of ornithopter flight.Originality/valueThe autonomous flight of ornithopter is firstly conducted in a confined indoor environment by using the motion capture system and the control performance is evaluated in terms of position errors.

Indoor Localization of Flying Robot by Means of Infrared Sensors

Autonomous navigation of flying robots in GPSdenied environments such as indoors requires that the flying robot be able to recognize the environment using external sensors. Laser scanners and computer vision are mainly used for indoor mapping and localization in studies on indoor flight. However, such systems require higher payload capacity and processing power for the sensors. In this study, we develop a lightweight flying robot for achieving indoor autonomous flight using four infrared (IR) sensors. As the first stage of this study, we present a localization technique that involves the use of a particle filter. Two problems exist in our system. First, it is difficult to use IR sensors close to a wall, because doing so would yield faulty results when calculating distance using the sensor output voltage. To resolve this problem, we developed a probabilistic output voltage observation model. The particle filter estimates position from voltage information using this model without the use of calculated distance. The second problem is that the spatial resolution is low because only four IR sensors are used. This problem was solved by rotating the robot horizontally at all times to acquire information from various directions. The localization performance was verified experimentally using an electric turntable and a cart. In the first and second experiments, we confirmed that localization is successful even when the robot is in motion and even when the robot is flying near a wall.

Fully Autonomous Indoor Flight Relying on Only Five Very Low-Cost Range Sensors

Answering what is the bare minimum combination of avionics and vehicle platform design that enables useful autonomous indoor flight without a Global Positioning System (GPS) is required to understand the lower-cost bounds for indoor unmanned aerial system (UAS) technology. The GTLama indoor UAS, capable of autonomously exploring indoor areas in the GPS-denied environment discussed here, was designed in an attempt to answer this question. TheGTLamaweighs around 600 g (1.3 lb), has a diameter of approximately 70 cm (27.6 in.), and costs less than 900 U.S. dollars. Cost and weight savings are realized through development of an innovative indoor guidance, navigation, and control algorithm that uses only five small off-the-shelf low-cost range sensors rather than expensive and sophisticated sensors such as inertial measurement units, scanning laser range sensors, andGPS. The onboard software leverages the inherent stability of the coaxial rotorcraft platform and fuses information from the low-fidelity range sensors to detect and follow walls and other stationary obstacles. The algorithms are designed to encourage maximum exploration of the perimeter of an indoor environment in a reasonable amount of time. The avionics design and the guidance, navigation, and control algorithms used are discussed in detail and validated through flight tests.

1A1-G02 三次元測域センサ搭載MS-06の屋内自律飛行の研究(飛行ロボット・メカトロニクス(1))

1A1-G02 三次元測域センサ搭載MS-06の屋内自律飛行の研究(飛行ロボット・メカトロニクス(1))

B27 マルチロータヘリコプタによる三次元SLAMを用いた屋内自律飛行の研究(M-OS10-3 自律知能無人ビークルの運動と制御III,M-OS10 自律知能無人ビークルの運動と制御,「海を越え,国を越え,世代を超えて!」)

B27 マルチロータヘリコプタによる三次元SLAMを用いた屋内自律飛行の研究(M-OS10-3 自律知能無人ビークルの運動と制御III,M-OS10 自律知能無人ビークルの運動と制御,「海を越え,国を越え,世代を超えて!」)

Self-Contained Autonomous Indoor Flight with Ranging Sensor Navigation

This paper describes the design and flight test of a completely self-contained autonomous indoor miniature unmanned aerial system (M-UAS). Guidance, navigation, and control algorithms are presented, enabling the M-UAS to autonomously explore cluttered indoor areas without relying on any off-board computation or external navigation aids such as Global Positioning Satellite (GPS). The system uses a scanning laser rangefinder and a streamlined simultaneous localization and mapping (SLAM) algorithm to provide a position and heading estimate, which is combined with other sensor data to form a six-degree-of-freedom inertial navigation solution. This enables an accurate estimate of the vehicle attitude, relative position, and velocity. The state information, with a selfgeneratedmap, is used to implement a frontier-based exhaustive search of an indoor environment. Improvements to existing guidance algorithms balance exploration with the need to remain within sensor range of indoor structures such that the SLAM algorithm has sufficient information to form a reliable position estimate. A dilution of the precisionmetric is developed to quantify the effect of environment geometry on the SLAMpose covariance, which is then used to update the two-dimensional position and heading in the navigation filter. Simulation and flight-test results validate the presented algorithms.

2A2-J10 屋内飛行のための回転式揺動装置付測域センサ搭載飛行ロボット(飛行ロボット・メカトロニクス(2))

In case of disasters or accidents, such as earthquake, typhoon, and critical accidents, it may be occurred danger area that people can't enter. The areas may interfere with rescue work and wreak further damage. Flying robot which can move three-dimensionally and fly low altitude and narrow place is expected to play an important role in such disaster areas. The purpose of this study is to develop indoor autonomous flight control system using three-dimensional mapping by Laser Range Finder(LRF). If the LRF is fixed, in order to scan three-dimensionally, it is needed that flying robot to be able to move up and down. So in this study, we add roundly swing device that can scan three-dimensionally without moving up and down to flying robot.

A fly-locust based neuronal control system applied to an unmanned aerial vehicle: the invertebrate neuronal principles for course stabilization, altitude control and collision avoidance

The most versatile and robust flying machines are still those produced by nature through evolution. The solutions to the 6 DOF control problem faced by these machines are implemented in extremely small neuronal structures comprising thousands of neurons. Hence, the biological principles of flight control are not only very effective but also efficient in terms of their implementation. An important question is to what extent these principles can be generalized to man-made flying platforms. Here, this question is investigated in relation to the computational and behavioral principles of the opto-motor system of the fly and locust. The aim is to provide a control infrastructure based only on biologically plausible and realistic neuronal models of the insect opto-motor system. It is shown that relying solely on vision, biologically constrained neuronal models of the fly visual system suffice for course stabilization and altitude control of a blimp-based UAV. Moreover, the system is augmented with a collision avoidance model based on the Lobula Giant Movement Detector neuron of the Locust. It is shown that the biologically constrained course stabilization model is highly robust and that the combined model is able to perform autonomous indoor flight.