Wind and route choice affect performance of bees flying above versus within a cluttered obstacle field

Bees flying through natural landscapes encounter physical challenges, such as wind and cluttered vegetation. The influence of these factors on the flight performance of bees remains unknown. We analyzed 548 videos of wild-caught honeybees (Apis mellifera) flying through an enclosure containing a field of vertical obstacles that bees could fly within (through open corridors, without maneuvering) or above. We examined how obstacle field height, wind presence and direction (headwinds or tailwinds) affected altitude, ground speed, and side-to-side casting (lateral excursions) of bees. When obstacle fields were short, bees flew at altitudes near the midpoint between the tunnel floor and ceiling. When obstacle fields approached or exceeded this midpoint, bees typically, but not always, increased their altitudes to fly over the obstacles. Bees that flew above the obstacle fields exhibited 40% faster ground speeds and 36% larger lateral excursions than bees that flew within the obstacle fields, likely due to the visual feedback from obstacles and narrow space available within the obstacle field. Wind had a strong effect on ground speed and lateral excursions, but not altitude. Bees flew 12-19% faster in tailwinds than in the other wind conditions, but their lateral excursions were 19% larger in any wind, regardless of its direction, than in still air. Our results show that bees flying through complex environments display flexible flight behaviors (e.g., flying above versus within obstacles), which affect flight performance. Similar choices in natural landscapes could have broad implications for foraging efficiency, pollination, and mortality in wild bees.

Studies on Operational Frequencies of Controls on Self-propelled Combine Harvesters in India

A study was conducted to measure the operational frequencies of various controls on self-propelled combine harvesters and to categorize them into frequently and infrequently operated controls. The operational frequency of controls on 10 combine harvesters of different makes and models were measured during harvesting of wheat crop. The frequency of use of frequently operated controls viz. header assembly control lever, ground speed control lever, gear shift lever, brake pedal, and clutch pedal ranged 232-484, 43-170, 41- 135, 42-140, and 66-162 action.h-1, respectively. The percent time distributions of operation of controls were 44.84, 13.40, 12.21, 13.10, and 16.42%, respectively. The controls on the combine harvesters used repetitively that require high level of human effort. Therefore, to accommodate 90% of user population, the most frequently operated controls should preferably be placed in the optimum reach zone, and infrequently used controls can be conveniently placed within maximum reach zone of operators’ reach envelope.

Tracking the invasive hornet Vespa velutina in complex environments by means of a harmonic radar

An innovative scanning harmonic radar has been recently developed for tracking insects in complex landscapes. This movable technology has been tested on an invasive hornet species (Vespa velutina) for detecting the position of their nests in the environment, in the framework of an early detection strategy. The new model of harmonic radar proved to be effective in tracking hornets either in open landscapes, hilly environments and areas characterised by the presence of more obstacles, such as woodlands and urban areas. Hornets were effectively tracked in complex landscapes for a mean tracking length of 96 ± 62 m with maximum values of ~ 300 m. The effectiveness of locating nests was 75% in new invasive outbreaks and 60% in highly density colonised areas. Furthermore, this technology could provide information on several aspects of insect’s ecology and biology. In this case, new insights were obtained about the mean foraging range of V. velutina (395 ± 208 m with a maximum value of 786 m) and flying features (ground speed), which was 6.66 ± 2.31 m s−1 for foraging individuals (hornets that are not carrying prey’s pellet) and 4.06 ± 1.34 m s−1 for homing individuals.

Signal-Based Self-Organization of a Chain of UAVs for Subterranean Exploration

Miniature multi-rotors are promising robots for navigating subterranean networks, but maintaining a radio connection underground is challenging. In this paper, we introduce a distributed algorithm, called U-Chain (for Underground-chain), that coordinates a chain of flying robots between an exploration drone and an operator. Our algorithm only uses the measurement of the signal quality between two successive robots and an estimate of the ground speed based on an optic flow sensor. It leverages a distributed policy for each UAV and a Kalman filter to get reliable estimates of the signal quality. We evaluate our approach formally and in simulation, and we describe experimental results with a chain of 3 real miniature quadrotors (12 by 12 cm) and a base station.

Ground Speed Optical Estimator for Miniature UAV

In a conventional Unmanned aerial vehicles (UAV) navigational system Global Navigation Satellite System (GNSS) sensor is often a main source of data for trajectory generation. Even video tracking based systems need some GNSS data for proper work. The goal of this study is to develop an optics-based system to estimate the ground speed of the UAV in the case of the GNSS failure, jamming, or unavailability. The proposed approach uses a camera mounted on the fuselage belly of the UAV. We can obtain the ground speed of the airplane by using the digital cropping, the stabilization of the real time image, and template matching algorithms. By combining the ground speed vector components with measurements of airspeed and altitude, the wind velocity and drift are computed. The obtained data were used to improve efficiency of the video-tracking based on a navigational system. An algorithm allows this computation to be performed in real time on board of a UAV. The algorithm was tested in Software-in-the-loop and implemented on the UAV hardware. Its effectiveness has been demonstrated through the experimental test results. The presented work could be useful for upgrading the existing MUAV products (with embedded cameras) already delivered to the customers only by updating their software. It is especially significant in the case when any necessary hardware upgrades would be economically unjustified or even impossible to be carried out.

Deep Spatio-Temporal Neural Networks for Risk Prediction and Decision Support in Aviation Operations

The maintenance and improvement of safety are among the most critical concerns in civil aviation operations. Due to the increased availability of data and improvements in computing power, applying artificial intelligence technologies to reduce risk in aviation safety has gained momentum. In this paper, a framework is developed to build a predictive model of future aircraft trajectory that can be utilized online to assist air crews in their decision-making during approach. Flight data parameters from the approach phase between certain approach altitudes (also called gates) are utilized for training an offline model that predicts the aircraft’s ground speed at future points. This model is developed by combining convolutional neural networks (CNNs) and long short-term memory (LSTM) layers. Due to the myriad of model combinations possible, hyperband algorithm is used to automate the hyperparameter tuning process to choose the best possible model. The validated offline model can then be used to predict the aircraft’s future states and provide decision-support to air crews. The method is demonstrated using publicly available Flight Operations Quality Assurance (FOQA) data from the National Aeronautics and Space Administration (NASA). The developed model can predict the ground speed at an accuracy between 1.27% and 2.69% relative root-mean-square error. A safety score is also evaluated considering the upper and lower bounds of variation observed within the available data set. Thus, the developed model represents an improved performance over existing techniques in literature and shows significant promise for decision-support in aviation operations.

The effect of optic flow cues on honeybee flight control in wind

To minimize the risk of colliding with the ground or other obstacles, flying animals need to control both their ground speed and ground height. This task is particularly challenging in wind, where head winds require an animal to increase its airspeed to maintain a constant ground speed and tail winds may generate negative airspeeds, rendering flight more difficult to control. In this study, we investigate how head and tail winds affect flight control in the honeybee Apis mellifera , which is known to rely on the pattern of visual motion generated across the eye—known as optic flow—to maintain constant ground speeds and heights. We find that, when provided with both longitudinal and transverse optic flow cues (in or perpendicular to the direction of flight, respectively), honeybees maintain a constant ground speed but fly lower in head winds and higher in tail winds, a response that is also observed when longitudinal optic flow cues are minimized. When the transverse component of optic flow is minimized, or when all optic flow cues are minimized, the effect of wind on ground height is abolished. We propose that the regular sidewards oscillations that the bees make as they fly may be used to extract information about the distance to the ground, independently of the longitudinal optic flow that they use for ground speed control. This computationally simple strategy could have potential uses in the development of lightweight and robust systems for guiding autonomous flying vehicles in natural environments.