Aerial Field Robotics

Aerial field robotics research represents the domain of study that aims to equip unmanned aerial vehicles - and as it pertains to this chapter, specifically Micro Aerial Vehicles (MAVs)- with the ability to operate in real-life environments that present challenges to safe navigation. We present the key elements of autonomy for MAVs that are resilient to collisions and sensing degradation, while operating under constrained computational resources. We overview aspects of the state of the art, outline bottlenecks to resilient navigation autonomy, and overview the field-readiness of MAVs. We conclude with notable contributions and discuss considerations for future research that are essential for resilience in aerial robotics.Comment: Accepted in the Encyclopedia of Robotics, Springe

Autonomous 3D Exploration of Large Structures Using an UAV Equipped with a 2D LIDAR

This paper addressed the challenge of exploring large, unknown, and unstructured industrial environments with an unmanned aerial vehicle (UAV). The resulting system combined well-known components and techniques with a new manoeuvre to use a low-cost 2D laser to measure a 3D structure. Our approach combined frontier-based exploration, the Lazy Theta* path planner, and a flyby sampling manoeuvre to create a 3D map of large scenarios. One of the novelties of our system is that all the algorithms relied on the multi-resolution of the octomap for the world representation. We used a Hardware-in-the-Loop (HitL) simulation environment to collect accurate measurements of the capability of the open-source system to run online and on-board the UAV in real-time. Our approach is compared to different reference heuristics under this simulation environment showing better performance in regards to the amount of explored space. With the proposed approach, the UAV is able to explore 93% of the search space under 30 min, generating a path without repetition that adjusts to the occupied space covering indoor locations, irregular structures, and suspended obstaclesUnión Europea Marie Sklodowska-Curie 64215Unión Europea MULTIDRONE (H2020-ICT-731667)Uniión Europea HYFLIERS (H2020-ICT-779411

PRECISE LANDING OF VTOL UAVS USING A TETHER

Unmanned Aerial Vehicles (UAVs), also known as drones, are often considered the solution to complex robotics problems. The significant freedom to explore an environment is a major reason why UAVs are a popular choice for automated solutions. UAVs, however, have a very limited flight time due to the low capacity and weight ratio of current batteries. One way to extend the vehicles\u27 flight time is to use a tether to provide power from external batteries, generators on the ground, or another vehicle. Attaching a tether to a vehicle may constrain its navigation but it may also create some opportunities for improvement of some tasks, such as landing. A tethered UAV can still explore an environment, but with some additional limitations: the tether can become wrapped around or bent by an obstacle, stopping the drone from traveling further and requiring backtracking to undo; the tether can fall loose and get caught while dragging on the ground; or the base of the tether could be mobile and the UAV needs to have a way to return to it. Most issues, like those listed above, could be solved with a vision system and various kinds of markers, but this approach could not work in situations of low light, where cameras are no longer effective. In this project, a state machine was developed to land a tethered, vertical take-off and landing (VTOL) UAV using only angles taken from both ends of the tether, the tension in the tether, and the height of the UAV. The main scenarios focused on in this project were normal operation, obstacle interference, loose tether, and a moving base. Normal operation is essentially tether guidance using the tether as a direction back to the base. The obstacle case has to determine the best action for untangling the tether. The loose tether case has to handle the loss of information given by the angle sensors, as the tether direction is no longer available. This case is performed as a last-ditched effort to find the landing pad with only a moderate chance for success. Lastly, the moving base case uses the change in the angles over time to determine the speed needed to reach the base. The software was not the only focus of this project. Two hardware components of this project were a landing platform and a matching landing gear to support the landing process. These two components were designed to aid in the precision of the landed location and to ensure that the UAV was secured in position once landed. The landing platform was designed as a passive funnel-type positioning mechanism with a depression in the center that the landing gear was designed to match. The tension of the tether is used to further lock the UAV into place when in motion. While some of this project remained theoretical, particularly the moving base case, there was flight testing performed for validation of most states of the proposed state machine. The normal operation state was effective at guiding the UAV onto the landing pad. The loose tether case was also able to land within reasonable expectations. This case was not always successful at finding the landing pad. Particular methods of increasing the likelihood of success are discussed in Future Work. The Obstacle Case was also able to be detected, but the response action has yet to be tested in full. The prior testing of velocity following can be used as proof of concept due to its simplicity. In conclusion, this project successfully developed a state machine for precisely landing a tethered UAV with no environmental knowledge or localization. Further development is necessary to improve the likelihood of landing in problematic scenarios and more testing is necessary for the system as a whole. More landing scenarios could also be researched and added as cases to the state machine to increase the robustness of the landing process. However, each current subsystem achieved some level of validation and is to be improved with future developments

Efficient 3D Segmentation, Registration and Mapping for Mobile Robots

Sometimes simple is better! For certain situations and tasks, simple but robust methods can achieve the same or better results in the same or less time than related sophisticated approaches. In the context of robots operating in real-world environments, key challenges are perceiving objects of interest and obstacles as well as building maps of the environment and localizing therein. The goal of this thesis is to carefully analyze such problem formulations, to deduce valid assumptions and simplifications, and to develop simple solutions that are both robust and fast. All approaches make use of sensors capturing 3D information, such as consumer RGBD cameras. Comparative evaluations show the performance of the developed approaches. For identifying objects and regions of interest in manipulation tasks, a real-time object segmentation pipeline is proposed. It exploits several common assumptions of manipulation tasks such as objects being on horizontal support surfaces (and well separated). It achieves real-time performance by using particularly efficient approximations in the individual processing steps, subsampling the input data where possible, and processing only relevant subsets of the data. The resulting pipeline segments 3D input data with up to 30Hz. In order to obtain complete segmentations of the 3D input data, a second pipeline is proposed that approximates the sampled surface, smooths the underlying data, and segments the smoothed surface into coherent regions belonging to the same geometric primitive. It uses different primitive models and can reliably segment input data into planes, cylinders and spheres. A thorough comparative evaluation shows state-of-the-art performance while computing such segmentations in near real-time. The second part of the thesis addresses the registration of 3D input data, i.e., consistently aligning input captured from different view poses. Several methods are presented for different types of input data. For the particular application of mapping with micro aerial vehicles where the 3D input data is particularly sparse, a pipeline is proposed that uses the same approximate surface reconstruction to exploit the measurement topology and a surface-to-surface registration algorithm that robustly aligns the data. Optimization of the resulting graph of determined view poses then yields globally consistent 3D maps. For sequences of RGBD data this pipeline is extended to include additional subsampling steps and an initial alignment of the data in local windows in the pose graph. In both cases, comparative evaluations show a robust and fast alignment of the input data