UltraSwarm: A Further Step Towards a Flock of Miniature Helicopters

We describe further progress towards the development of a MAV (micro aerial vehicle) designed as an enabling tool to investigate aerial flocking. Our research focuses on the use of low cost off the shelf vehicles and sensors to enable fast prototyping and to reduce development costs. Details on the design of the embedded electronics and the modification of the chosen toy helicopter are presented, and the technique used for state estimation is described. The fusion of inertial data through an unscented Kalman filter is used to estimate the helicopter’s state, and this forms the main input to the control system. Since no detailed dynamic model of the helicopter in use is available, a method is proposed for automated system identification, and for subsequent controller design based on artificial evolution. Preliminary results obtained with a dynamic simulator of a helicopter are reported, along with some encouraging results for tackling the problem of flocking

Optimization of a Simultaneous Localization and Mapping (SLAM) System for an Autonomous Vehicle Using a 2-Dimensional Light Detection and Ranging Sensor (LiDAR) by Sensor Fusion

Fully autonomous vehicles must accurately estimate the extent of their environment as well as their relative location in their environment. A popular approach to organizing such information is creating a map of a given physical environment and defining a point in this map representing the vehicle’s location. Simultaneous Mapping and Localization (SLAM) is a computing algorithm that takes inputs from a Light Detection and Ranging (LiDAR) sensor to construct a map of the vehicle’s physical environment and determine its respective location in this map based on feature recognition simultaneously. Two fundamental requirements allow an accurate SLAM method: one being accurate distance measurements and the second being an accurate assessment of location. Researched are methods in which a 2D LiDAR sensor system with laser range finders, ultrasonic sensors and stereo camera vision is optimized for distance measurement accuracy, particularly a method using recurrent neural networks. Sensor fusion techniques with infrared, camera and ultrasonic sensors are implemented to investigate their effects on distance measurement accuracy. It was found that the use of a recurrent neural network for fusing data from a 2D LiDAR with laser range finders and ultrasonic sensors outperforms raw sensor data in accuracy (46.6% error reduced to 3.0% error) and precision (0.62m std. deviation reduced to 0.0015m std. deviation). These results demonstrate the effectiveness of machine learning based fusion algorithms for noise reduction, measurement accuracy improvement, and outlier measurement removal which would provide SLAM vehicles more robust performance

Kalman Filter for Noise Reduction in Aerial Vehicles using Echoic Flow

Echolocation is a natural phenomenon observed in bats that allows them to navigate complex, dim environments with enough precision to capture insects in midair. Echolocation is driven by the underlying process of echoic flow, which can be broken down into a ratio of the distance from a target to the velocity towards it. This ratio produces a parameter τ representing the time to collision, and controlling it allows for highly efficient and consistent movement. When a quadcopter uses echoic flow to descend to a target, measurements from the ultrasonic range sensor exhibit noise. Furthermore, the use of first order derivatives to calculate the echoic flow parameters results in an even greater magnitude of noise. The implementation of an optimal Kalman filter to smooth measurements allows for more accurate and precise tracking, ultimately recreating the high efficiency and consistency of echolocation tracking techniques found in nature. Kalman filter parameters were tested in realistic simulations of the quadcopter's descent. These tests determined an optimal Kalman filter for the system. The Kalman filter's effect on an accurate echoic flow descent was then tested against that of other filtering methods. Of the filtering methods tested, Kalman filtering best allowed the quadcopter to control its echoic flow descent in a precise and consistent manner. In this presentation, the test methodology and results of the various tests are presented.No embargoAcademic Major: Electrical and Computer Engineerin

3D Distance Filter for the Autonomous Navigation of UAVs in Agricultural Scenarios

In precision agriculture, remote sensing is an essential phase in assessing crop status and variability when considering both the spatial and the temporal dimensions. To this aim, the use of unmanned aerial vehicles (UAVs) is growing in popularity, allowing for the autonomous performance of a variety of in-field tasks which are not limited to scouting or monitoring. To enable autonomous navigation, however, a crucial capability lies in accurately locating the vehicle within the surrounding environment. This task becomes challenging in agricultural scenarios where the crops and/or the adopted trellis systems can negatively affect GPS signal reception and localisation reliability. A viable solution to this problem can be the exploitation of high-accuracy 3D maps, which provide important data regarding crop morphology, as an additional input of the UAVs’ localisation system. However, the management of such big data may be difficult in real-time applications. In this paper, an innovative 3D sensor fusion approach is proposed, which combines the data provided by onboard proprioceptive (i.e., GPS and IMU) and exteroceptive (i.e., ultrasound) sensors with the information provided by a georeferenced 3D low-complexity map. In particular, the parallel-cuts ellipsoid method is used to merge the data from the distance sensors and the 3D map. Then, the improved estimation of the UAV location is fused with the data provided by the GPS and IMU sensors, using a Kalman-based filtering scheme. The simulation results prove the efficacy of the proposed navigation approach when applied to a quadrotor that autonomously navigates between vine rows

Quadcopter altitude estimation using low-cost barometric, infrared, ultrasonic and LIDAR sensors

Cilj ovog istraživanja je procena različitih low-cost senzora za merenje visine leta bespilotne letelice sa više rotora na malim visinama. Primenjene su metode filtriranja podataka i druge metode u cilju optimizacije performansi i tačnosti merenja senzora. Izvšrena su merenja visine leta, a podaci su uskladišteni za kasniju analizu u odnosu na stvarnu visinu leta. Izračunati su stepeni korelacije i srednja kvadratna greška u merenju senzora sa ciljem procene rada senzora. Na osnovu rezultata istraživanja moguće je odrediti izbor adekvatnog senzora za ovu specifičnu primenu. Ovo istraživanje je pokazalo da je u uslovima ovog eksperimenta najbolje rezultate imao lidar senzor Garmin LIDAR-Lite V3HP i senzor Bosch Sensortech BME280 sa mogućnošću istovremenog merenja vlažnosti vazduha, atmosferskog pritiska i temperature.The goal of this research is to assess the different low-cost sensors for flight altitude measuring of a multirotor UAV at low altitude flight. For optimizing the sensor performances and accuracy, data filtering and other methods were applied. The flight altitude data were collected and stored for later analysis with reference to the true altitude. The correlation coefficient and the mean squared error were calculated in order to assess the sensors' performance. On the basis of the results of the study, it was possible to determine the choice of the adequate sensor for this specific use. The study showed that the best characteristics for this experiment conditions had the Garmin LIDAR-Lite V3HP sensor and the Bosch Sensortech BME280 that combined air humidity, atmospheric pressure, and air temperature sensor

Quadcopter altitude estimation using low-cost barometric, infrared, ultrasonic and LIDAR sensors

Cilj ovog istraživanja je procena različitih low-cost senzora za merenje visine leta bespilotne letelice sa više rotora na malim visinama. Primenjene su metode filtriranja podataka i druge metode u cilju optimizacije performansi i tačnosti merenja senzora. Izvšrena su merenja visine leta, a podaci su uskladišteni za kasniju analizu u odnosu na stvarnu visinu leta. Izračunati su stepeni korelacije i srednja kvadratna greška u merenju senzora sa ciljem procene rada senzora. Na osnovu rezultata istraživanja moguće je odrediti izbor adekvatnog senzora za ovu specifičnu primenu. Ovo istraživanje je pokazalo da je u uslovima ovog eksperimenta najbolje rezultate imao lidar senzor Garmin LIDAR-Lite V3HP i senzor Bosch Sensortech BME280 sa mogućnošću istovremenog merenja vlažnosti vazduha, atmosferskog pritiska i temperature.The goal of this research is to assess the different low-cost sensors for flight altitude measuring of a multirotor UAV at low altitude flight. For optimizing the sensor performances and accuracy, data filtering and other methods were applied. The flight altitude data were collected and stored for later analysis with reference to the true altitude. The correlation coefficient and the mean squared error were calculated in order to assess the sensors' performance. On the basis of the results of the study, it was possible to determine the choice of the adequate sensor for this specific use. The study showed that the best characteristics for this experiment conditions had the Garmin LIDAR-Lite V3HP sensor and the Bosch Sensortech BME280 that combined air humidity, atmospheric pressure, and air temperature sensor

Battery state-of-charge estimation using machine learning analysis of ultrasonic signatures

The potential of acoustic signatures to be used for State-of-Charge (SoC) estimation is demonstrated using artificial neural network regression models. This approach represents a streamlined method of processing the entire acoustic waveform instead of performing manual, and often arbitrary, waveform peak selection. For applications where computational economy is prioritised, simple metrics of statistical significance are used to formally identify the most informative waveform features. These alone can be exploited for SoC inference. It is further shown that signal portions representing both early and late interfacial reflections can correlate highly with the SoC and be of predictive value, challenging the more common peak selection methods which focus on the latter. Although later echoes represent greater through-thickness coverage, and are intuitively more information-rich, their presence is not guaranteed. Holistic waveform treatment offers a more robust approach to correlating acoustic signatures to electrochemical states. It is further demonstrated that transformation into the frequency domain can reduce the dimensionality of the problem significantly, while also improving the estimation accuracy. Most importantly, it is shown that acoustic signatures can be used as sole model inputs to produce highly accurate SoC estimates, without any complementary voltage information. This makes the method suitable for applications where redundancy and diversification of SoC estimation approaches is needed. Data is obtained experimentally from a 210 mAh LiCoO2/graphite pouch cell. Mean estimation errors as low as 0.75% are achieved on a SoC scale of 0–100%

Autonomous Aerial Water Sampling

Obtaining spatially separated, high frequency water samples from rivers and lakes is critical to enhance our understanding and effective management of fresh water resources. In this thesis we present an aerial water sampler and verify the system in field experiments. The aerial water sampler has the potential to vastly increase the speed and range at which scientists obtain water samples while reducing cost and effort. The water sampling system includes: 1) a mechanism to capture three 20 ml samples per mission; 2) sensors and algorithms for safe navigation and altitude approximation over water; and 3) software components that integrate and analyze sensor data, control the vehicle, and drive the sampling mechanism. In this thesis we validate the system in the lab, characterize key sensors, and present results of outdoor experiments. We compare water samples from local lakes obtained by our system to samples obtained by traditional sampling techniques. We find that nearly all water properties are consistent between the two techniques. These experiments show that despite the challenges associated with flying precisely over water, it is possible to quickly obtain water samples with an Unmanned Aerial Vehicle (UAV). Advisers: Carrick Detweiler and Matthew B. Dwye