Los investigadores del Instituto de Optimización de Sistemas (ITE) del Instituto de Tecnología de Karlsruhe (KIT) en Alemania han estado trabajando en un enfoque prometedor que no utiliza GNSS. La premisa inicial del enfoque ITE es que para el futuro vuelo autónomo, especialmente en el entorno potencialmente difícil de búsqueda y salvamento (SAR), como en un incendio en un edificio, la recepción de la señal GNSS puede ser poca o nula . La mayoría de los UAV están equipados con GNSS e inercial, por lo que se prefiere la solución inercial con un sistema de respaldo. ITE optó por utilizar una cámara monocular y un telémetro láser 2D combinado en un sensor híbrido de cámara láser para ayudar a la navegación.
Since we’re running essentially a navigation magazine, someone had the bright idea that maybe we could bring together the monthly review of UAS/UAV activities combined with some hint of navigation content. Seems reasonable. So delving into the academic world once more, we’ve been searching for prior papers that address novel ways for divining where a UAV might be and how it might find its way about.
Promising non-GNSS approach
Turns out investigators at the Institute of Systems Optimization (ITE) at the Karlsruhe Institute of Technology (KIT) in Germany have been working on a promising approach that does not use GNSS.
The initial premise of the ITE approach is that for future autonomous flight, especially in the potentially difficult indoor environment of search and rescue (SAR) such as in a building fire, GNSS signal reception may be little to none. But most UAVs are equipped with GNSS and inertial, so aiding the inertial solution with a back-up system is preferred. ITE chose to use a monocular camera and a 2D laser rangefinder combined into a hybrid laser-camera sensor for navigation aiding.
The camera and laser-range finder were initially calibrated by focusing from multiple different adjacent locations on one object, and so determining the attitude and translation between the two sensors. Basic navigation sans GNSS is established using the acceleration and angular rate information provided by the IMU, but inertial drift rapidly decreases accuracy, so aiding is essential.
The aiding solution has several components which are first integrated together. The camera sensor provides an initial “keyframe” from which relative motion can be derived.
The next phase was to verify the initial performance of the inertial/hybrid solution, by flying the UAV down a corridor towards a wall. Horizontal position began to degrade around 67 seconds.
The next more challenging demonstration involved transit down the corridor then into an adjacent room and leaving via a different exit. In addition, solutions using hybrid aiding and laser scanning aiding were evaluated.
The hybrid approach appeared to satisfy the anticipated test constraints very accurately with a deviation of about 0.8 during the 274 second flight, while the laser scanning approach had a horizontal error between start and end point of about 3.7. It was felt that the structured environment in the test rooms presented challenges for laser scanning and resulted in vertical variations coming from the dependence on the UAV’s attitude, while the hybrid solution overcame these problems.
The conclusion from the testing was that the hybrid sensor performance was not limited by the structured test environment. So missions in more challenging environments could be better navigated in future with the hybrid system, compared to those where existing laser-scan-matching approaches would be used. The researchers intend to now focus on better perception of the test environment. For exploration missions, not only is accurate positioning crucial but also an accurate representation of the environment is necessary, for which the hybrid sensor is a promising tool.
Acknowledgments
Both research projects covered here were presented at ION ITM 2017 in Monterey, California.
Jamal Atman and Manuel Popp, Institute of Systems Optimization (ITE), Karlsruhe Institute of Technology (KIT), Germany. Gert F. Trommer, Institute of Systems Optimization (ITE), Karlsruhe Institute of Technology (KIT), Germany & ITMO University, St. Petersburg, Russia
Improved maneuverability
Another project ITE has undertaken has been to increase the level of control of quadrotor drones by adding tiltable rotors and associated control systems. The object is to maintain a certain orientation of the UAV and its payload without altering platform attitude, to manage maneuvering more effectively and to compensate for disturbances faster and possibly enlarge the area of operation for rescue forces.
For fire disaster recovery, hovering multi-rotor UAVs can provide invaluable information within buildings, rather than risking the lives of first responders. Locating survivors or difficult to find fire sources using video transmitted by drones may save time and reduce exposure for critical personnel.
A two-part nonlinear control system has been implemented by ITE — the first part takes the measurements of the vehicle dynamics and connects these measurements to a back-stepping controller to generate the desired forces and torque to change vehicle motion.
At first the commanded signals have to be fed through a filter in order to provide smooth and continuous command signals and to produce the derivatives required by the control algorithm. The smoothed command signal is then used by an arbitrary controller to create vectors of required forces and torque to control the attitude and velocity of the vehicle.
Desired force and torque is fed into an adaptive and dynamic control allocation algorithm to generate the values for the actuators – there are four propulsion motor commands and four servo motor commands. The control allocation algorithm is an adaptive algorithm – used in order to adjust for changing situations and environments. For example, when flying in a hallway and near walls, ceiling or floor, flight characteristics change significantly due to different aerodynamic effects. On the other hand, outdoors flight behavior is usually much easier to manage as the only nonlinear behavior occurs relatively close to the ground.
- 3D modeled performance versus flight data.
- 3D modeled performance versus flight data (both diagrams show the same flight).
In order to verify the performance of the system it was modeled — flight dynamics and operator control inputs were simulated. Performance was found to closely match actual recorded flight data. This novel approach could have a number of possible applications — possibly to serve as an alternative to a gimbal mount for a camera?
Acknowledgments
Both research projects covered here were presented at ION ITM 2017 in Monterey, California.
Georg Scholz, Institute of Systems Optimization (ITE), Karlsruhe Institute of Technology (KIT), Germany. Gert F. Trommer, Institute of Systems Optimization (ITE), Karlsruhe Institute of Technology (KIT), Germany.
A key feature of the tilt rotor approach is insensitivity to wind gusts; enabling successful operation in situations where standard UAVs could fail. So we might anticipate applications such as all-weather reliable delivery of goods, surveillance tasks even in storms, inspection of operational wind-generation parks, and uninterrupted searches for avalanche victims regardless of continuing stormy weather.
It’s easy to see that other applications may well want production solutions for ways to navigate when GNSS signals are blocked. It’s possible SAR in rugged mountainous terrain could also suffer intermittent GNSS signal blockage, as could UAV flight in heavily wooded forests, or anywhere where a canopy blocks out the sky. So could survey be a potential commercial application for this type of augmentation? What about mining and subways as well as indoors and outdoors search and rescue?
Fuente: http://gpsworld.com
