This research was supported by European Commission Horizon 2020 Programme through project under G. A. number 810321, named Twinning coordination action for spreading excellence in Aerial Robotics-AeRoTwin [1] and through project under G. A. number 820434, named ENergy aware BIM Cloud Platform in a COst-effective Building REnovation Context-ENCORE [2] ABSTRACT This paper presents a LiDAR-equipped unmanned aerial vehicle (UAV) performing semiautonomous wind-turbine blade inspection which includes traversing to the blade tip and back, while keeping constant relative distance and heading to the blade plane. Plane detection is performed applying the RANSAC method on a subset of the gathered pointcloud. Utilizing the relative distance to the inferred plane as well as its normal vector, the UAV is able to maintain a constant distance and heading towards the plane while moving in parallel with it. The proposed procedure performs successful wind-turbine blade inspections with minimal operator involvement. Inspection results include high-resolution blade images as well as a 3D model of the wind-turbine structure. Finally, to show the feasibility of this approach, simulations are performed on a wind-turbine model and experimental results are presented for an outdoor single-blade inspection scenario both on a mock-up setup and a full-scale wind-turbine blade. It is worth noting that the system's adequacy has been fully validated in real conditions on an operational wind farm.
In this paper we present a study of a robotic system that consists of an unmanned aerial vehicle equipped with a pair of manipulator arms (MMUAV), and unmanned ground vehicles (UGVs). The envisioned application scenario includes autonomous packet transportation, where MMUAV is used for picking/placing packets, while both MMUAV and UGV can be used for packet transportation, with different energy consumption profiles. We propose a reactive method for decentralized task planning and coordination of robots using hierarchical task decomposition based on TAEMS framework. Our approach takes into account low-level motion-planning aspects of the system as well as high-level mission specification, making this a multi-layered system. For low-level planning we use sampling-based planner combined with obstacle-free trajectory generation. Methods are verified in simulations and on an experimental testbed, using 3D Robotics quadcopter and Pioneer 3DX mobile robots with the results showing stability and robustness of the presented methods.
Microwave Resonance Therapy (MRT) is a novel medical treatment, which represents a synthesis of the ancient Chinese traditional knowledge in medicine (acupuncture) and recent breakthroughs in biophysics. Affecting the appropriate acupuncture points by the generation of high frequency microwaves (52-78 GHz), remarkable clinical results of the treatment are being achieved. In this paper the quantum-like macroscopic biophysical bases of the MRT are elaborated too, offering a new insight in the mechanisms of the assembling gap junction hemichannels upon the internal microwave (MW) electromagnetic field spatio-temporal maximums at the temporary position of the acupuncture system, and hence the very biophysical nature of the temporary psychosomatic health or disease. Such a picture also supports the EM/ionic "optical" ultralowfrequency modulated MW quantum holographic neural network-like function of the acupuncture system (similar to complex-valued oscillatory holographic Hopfield-like neural networks), and its essential relation to consciousness, as strongly suggested from biophysical modeling of altered states of consciousness. Finally, our electroencephalographic investigations showed more or less characteristic EEG features in many subjects of relaxation induced by microwave resonance therapy applied to corresponding acupuncture points, as well as by some other activities which may alter consciousness (transcendental meditation, musicogenic states, healer/healee noncontact interaction, drowsiness), demonstrating that EEG may permit rapid and reproducible identification of different states of consciousness, useful in evaluating the effectiveness of consciousness altering techniques and related stress reduction.
Periodic bridge inspections are required every several years to determine the state of a bridge. Most commonly, the inspection is performed using specialized trucks allowing human inspectors to review the conditions underneath the bridge, which requires a road closure. The aim of this paper was to use aerial manipulators to mount sensors on the bridge to collect the necessary data, thus eliminating the need for the road closure. To do so, a two-step approach is proposed: an unmanned aerial vehicle (UAV) equipped with a pressurized canister sprays the first glue component onto the target area; afterward, the aerial manipulator detects the precise location of the sprayed area, and mounts the required sensor coated with the second glue component. The visual detection is based on an RGB-D sensor and provides the target position and orientation. A trajectory is then planned based on the detected contact point, and it is executed through the adaptive impedance control capable of achieving and maintaining a desired force reference. Such an approach allows for the two glue components to form a solid bond. The described pipeline is validated in a simulation environment while the visual detection is tested in an experimental environment.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.