In this letter we propose a planner for 3D exploration that is suitable for applications using state-of-the-art 3D sensors, such as LiDARs, that produce large point clouds with each scan. The planner is based on the detection of a frontier - a boundary between the explored and the unknown part of the environment - and consists of the algorithm for detecting frontier points, followed by the clustering of frontier points and the selection of the best frontier point to be explored. Compared to existing frontier-based approaches, the planner is more scalable, i.e., it requires less time for the same environment size while ensuring similar exploration time. The performance is achieved by relying not on data obtained directly from the 3D sensor, but on data obtained by a mapping algorithm. In order to cluster the frontier points, we exploit the properties of the Octree environment representation, which allows easy analysis with different resolutions. The planner is tested in the simulation environment and in an outdoor test area with a UAV equipped with a LiDAR sensor. The results show the advantages of the approach compared to current state-of-the-art approaches.

This paper presents a LiDAR-equipped unmanned aerial vehicle (UAV) performing semi-autonomous 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 letter, we address the problem of autonomous exploration of unknown environments with an aerial robot equipped with a sensory set that produces large point clouds, such as LiDARs. The main goal is to gradually explore an area while planning paths and calculating information gain in short computation time, suitable for implementation on an on-board computer. To this end, we present a planner that randomly samples viewpoints in the environment map. It relies on a novel and efficient gain calculation based on the Recursive Shadowcasting algorithm. To determine the Next-Best-View (NBV), our planner uses a cuboid-based evaluation method that results in an enviably short computation time. To reduce the overall exploration time, we also use a dead end resolving strategy that allows us to quickly recover from dead ends in a challenging environment. Comparative experiments in simulation have shown that our approach outperforms the current state-of-the-art in terms of computational efficiency and total exploration time. The video of our approach can be found at https://www.youtube.com/playlist?list =PLC0C6uwoEQ8ZDhny1VdmFXLeTQOSBibQl .

This paper demonstrates the experimental validation of canonical peg-in-hole manipulation task using an aerial robot. The robot consists of a multirotor platform equipped with a dual arm multi degree of freedom manipulator. The paper discusses the introduced kinematic constraints which make sure the robot holds a bolt with both arms. We build our peg-in-hole approach using impedance control which is the foundation of compliant interaction with the environment. We utilize a finite state automaton to plan a multi stage strategy which relies on tactile perception in order to pin point the target. Finally, the whole body locomotion is considered, meaning both the degrees of freedom of multirotor base and the dual arm manipulator are considered.

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 Red Green Blue - Depth (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.

This paper presents an Unmanned Aerial Vehicle (UAV) platform used for the competition held at the International Conference for Unmanned Aircraft Systems 2022 (ICUAS’22). The envisioned scenario in the competition is delivering an extinguishing ball to a fire with a UAV. Driven on that thought, the qualifying round of the competition is organized in a realistic simulation environment. The competitors are required to navigate through a cluttered environment into a free space, where they perform a search for the target and deliver the ball as close as possible to that target. The competition finals were organized as a part of the conference, with the same goal as in the simulation qualifiers. The UAVs used for the competition are prepared and provided by the organizers. A detailed description of the UAV software and hardware is outlined in the paper as a step towards standardizing aerial research platform.