Agile Unmanned Aerial Vehicles Research Articles

How Much Energy Do We Need to Fly with Greater Agility? Energy Consumption and Performance of an Attitude Stabilization Controller in a Quadcopter Drone: A Modified MPC vs. PID

Increasing demand for faster and more agile Unmanned Aerial Vehicles (UAVs, drones) is observed in many scenarios, including but not limited to medical supply or Search-and-Rescue (SAR) missions. Exceptional maneuverability is critical for robust obstacle avoidance during autonomous flights. A novel modification to the Model Predictive Controller (MPC) is proposed, which drastically improves the speed of the attitude controller of our quadcopter drone. The modified MPC is suitable for the onboard microcontroller and the 400 Hz main control loop. The peak and total energy consumption and the performance of the attitude controllers are assessed: the modified MPC and the default Proportional-Integral-Derivative (PID). The tests were conducted in a custom-implemented Flight Mode in the ArduCopter software stack, securing the drone in a test harness, which guarantees the experiments are repetitive. The ultimate MPC greatly increases maneuverability of the drone and may inspire more research related to faster obstacle avoidance and new types of hybrid attitude controllers to balance the agility and the power consumption.

Realisation and testing of novel fully articulated bird-inspired flapping wings for efficient and agile UAVs

AbstractLarge birds have evolved an effective wing anatomy and mechanics, enabling airborne mastery of manoeuvres and endurance. For these very reasons, they are difficult to replicate and study. The aim of the present work is to achieve active wing articulations to mimic natural bird flapping towards efficient and agile Unmanned Aerial Vehicles (UAVs). The proposed design, bio-mimicking the black-headed gull, Larus ridibundus, has three active and independent servo-controlled wing joints at the shoulder, elbow and wrist to achieve complex controls. The construction of the wing is realised through a polymeric skin and carbon fibre–epoxy composite spars and ribs. The wing movements (flapping, span reduction and twisting) envelopes of the full-scale robotic gull (Robogull) are examined using the Digital Image Correlation (DIC) technique and laser displacement sensing. Its aerodynamic performance was evaluated in a wind tunnel at various flapping parameters, wind speeds and angles of attack. It is observed that a flapping amplitude of 45 $^\circ$ is more favourable than 90 $^\circ$ for generating higher lift and thrust, while also depending on the presence of span reduction, twist and wind speed. The model performs better at a flying velocity of 4m/s as compared with 8m/s. Both lift and thrust are high at a higher flapping frequency of 2.5Hz. Combined variation of the flapping frequency and stroke ratio should be considered for better aerodynamic performance. The combination of a lower stroke ratio of 0.5 with a flapping frequency of 2.5Hz generates higher lift and thrust than other combinations. Span reduction and wing twist notably and independently enhance lift and thrust, respectively. An increase in the angle-of-attack increases lift but decreases thrust. The model can also generate a significant rolling moment when set at a bank angle of 20 $^\circ$ and operated with independently controlled flapping amplitudes for the wings (45 $^\circ$ for the left wing and 90 $^\circ$ for the right wing). Based on the optimal values for the flapping amplitude (45 $^\circ$ ), flapping frequency (2.5Hz) and flying velocity (4m/s), the Strouhal number (St) of the Robogull model is 0.24, lying in the optimal range ( $0.2 < \mathrm{St} < 0.4$ ) for natural flyers and swimmers.

A Dynamically Feasible Fast Replanning Strategy with Deep Reinforcement Learning

In this work, we aim to develop a fast trajectory replanning methodology enabling highly agile aerial vehicles to navigate in cluttered environments. By focusing on reducing complexity and accelerating the replanning problem under strict dynamical constraints, we employ the b-spline theory with local support property for defining the high dimensional agile flight trajectories. We utilize the differential flatness model of an aerial vehicle, allowing us to directly map the desired output trajectory into input states to track a high dimensional trajectory. Dynamically feasible replanning problem is addressed through regenerating the local b-splines with control point reallocation. As the geometric form of the trajectory based on the location of the control points and the knot intervals, the control point reallocation for fast replanning with dynamical constraints is turned into a constrained optimization problem and solved through deep reinforcement learning. The proposed methodology enables generating dynamically feasible local trajectory segments, which are continuous to the existing, hence provides fast local replanning for collision avoidance. The DRL agent is trained with different environmental complexities, and through the batch simulations, it is shown that the proposed methodology allows to solve fast trajectory replanning problem under given or hard dynamical constraints and provide real-time applicability for such collision avoidance applications in agile unmanned aerial vehicles. Hardware implementation tests of the algorithm with the agile trajectory tracker to a small UAV can bee seen in the following video link: https://youtu.be/8IiLQFQ3V0E .

UAV-based canopy textures assess changes in forest structure from long-term degradation

Degraded tropical forests dominate agricultural frontiers and their management is becoming an urgent priority. This calls for a better understanding of the different forest cover states and cost-efficient techniques to quantify the impact of degradation on forest structure. Canopy texture analyses based on Very High Spatial Resolution (VHSR) optical imagery provide proxies to assess forest structures but the mechanisms linking them with degradation have rarely been investigated. To address this gap, we used a lightweight Unmanned Aerial Vehicle (UAV) to map 739 ha of degraded forests and acquire both canopy VHSR images and height model. Thirty-three years of degradation history from Landsat archives allowed us to sample 40 plots in undisturbed, logged, over-logged and burned and regrowth forests in tropical forested landscapes (Paragominas, Pará, Brazil). Fourier (FOTO) and lacunarity textures were used to assess forest canopy structure and to build a typology linking degradation history and current states. Texture metrics capture canopy grain, heterogeneity and openness gradients and correlate with forest structure variability (R2 = 0.58). Similar structures share common degradation history and can be discriminated on the basis of canopy texture alone (accuracy = 55%). Over-logging causes a lowering in forest height, which brings homogeneous textures and of finer grain. We identified the major changes in structures due to fire following logging which changes heterogeneous and intermediate grain into coarse textures. Our findings highlight the potential of canopy texture metrics to characterize degraded forests and thus be used as indicators for forest management and degradation mitigation. Inexpensive and agile UAV open promising perspectives at the interface between field inventory and satellite characterization of forest structure using texture metrics.

Swarm coordination of mini-UAVs for target search using imperfect sensors

Unmanned Aerial Vehicles (UAVs) have a great potential to support search tasks in unstructured environments. Small, lightweight, low speed and agile UAVs, such as multi-rotors platforms can incorporate many kinds of sensors that are suitable for detecting object of interests in cluttered outdoor areas. However, due to their limited endurance, moderate computing power, and imperfect sensing, mini-UAVs should be into groups using swarm coordination algorithms to perform tasks in a scalable, reliable and robust manner. In this paper a biologically-inspired mechanisms is adopted to coordinate drones performing target search with imperfect sensors. In essence, coordination can be achieved by combining stigmergic and flocking behaviors. Stigmergy occurs when a drone releases digital pheromone upon sensing of a potential target. Such pheromones can be aggregated and diffused between flocking drones, creating a spatiotemporal attractive potential field. Flocking occurs, as an emergent effect of alignment, separation and cohesion, where drones self organise with similar heading and dynamic arrangement as a group. The emergent coordination of drones relies on the alignment of stigmergy and flocking strategies. This paper reports on the design of the novel swarming algorithm, reviewing different strategies and measuring their performance on a number of synthetic and real-world scenarios.

Investigation of Fish-Eye Lenses for Small-UAV Aerial Photography

Aerial photography obtained by unmanned aerial vehicles (UAVs) is a rising market for their civil application. Small UAVs are believed to close gaps in niche markets, such as acquiring airborne image data for remote sensing purposes. Small UAVs can fly at low altitudes, in dangerous environments, and over long periods of time. However, their small lightweight construction leads to new problems, such as higher agility and more susceptibility to turbulence, which has a big impact on the quality of the data and their suitability for aerial photography. This paper investigates the use of fish-eye lenses to overcome field-of-view (FOV) issues for highly agile UAV platforms susceptible to turbulence. The fish-eye lens has the benefit of a large observation area (large FOV) and does not add additional weight to the aircraft, such as traditional mechanical stabilizing systems. We present the implementation of a fish-eye lens for aerial photography and mapping purposes, with potential use in remote sensing applications. We describe a detailed investigation from the fish-eye lens distortion to the registering of the images. Results of the process are presented using low-quality sensors typically found on small UAVs. The system was flown on a midsize platform (a more stable Cessna aircraft) and also on ARCAA's small (<10 kg) UAV platform. The effectiveness of the approach is compared for the two sized platforms.

The automatic air force [unmanned aerial vehicles

This paper presents the application of tiny, high-performance computers to create a new generation of much more agile unmanned aerial vehicles. UAVs have come a long way since the tethered balloons of the second world war. Modern versions can have as much surveillance power as a Lockheed Martin U-2 spy plane, or be small enough to fly into a room.