This project studies the implementation of multi-robot systems consisting of flying robots to inspect infrastructural assets autonomously.
Team and Collaborators
Research Period and Sponsors
This research started in December 2021 and is ongoing. It has been partially funded by the ENAC Interdisciplinary Cluster Grant “ECOLE: IntElligent Systems for Automated InspeCtiOn of SteeL InfrastructurE” [link].
Publications
Additional material available in the next section.
Benchmarking and Experimental Validation of a Real-Time Multi-Robot Hybrid Coverage Algorithm for Known Environments
In the context of asset inspection, the size of the environment to be covered can be large. Mobile robotic systems are capable of acquiring more extensive data than static sensors, but the capacity of the robotic platform used can be limited by its autonomy and sensing capabilities. This is why multirobot systems are interesting in such applications. However, scaling up to larger robot team sizes requires coordination among robots to be carried out efficiently. In this work, we investigate a hybrid coordination strategy with a team of microaerial vehicles, where a ground station centrally assigns tasks in real time to the robots, and the robots distributively coordinate their trajectories to carry out the coverage of a known asset. In particular, we perform the benchmarking and experimental validation of such a strategy. Several variants of the strategy are implemented by adapting existing state-of-the-art solutions to this context. Extensive simulation experiments are carried out in various environments to benchmark each variant and evaluate how their performance scales with the robot team size. The results show that the strategy scales well for larger robot teams, thanks to its efficient task generation process. Notably, despite its relatively simple but efficient task generation technique, it outperforms or is comparable to other methods employing more complex schemes (such as information gain or frontiers). Finally, we validated the proposed strategy with teams of up to three robots in physical experiments.
2026
2026 IEEE International Conference on Robotics and Automation, Vienna, Austria, 2026-06-01 – 2026-06-05.Multi-Robot Online Coverage with a Team of Resource-Constrained Micro Aerial Vehicles
We present a novel coordination scheme for online multi-robot coverage with a team of resource-constrained aerial vehicles relying on the PH-tree data structure. We propose a hybrid system architecture where coverage tasks are assigned to robots in a centralized manner, and individual robots plan their paths cooperatively in a distributed fashion. In this way, robots can focus only on local path planning tasks, while a ground station ensures coordination among the team. We show the applicability of our method to various objects and quantitatively evaluate the performance of our solution for various robot team sizes.
Proc. of the 17th International Symposium on Distributed Autonomous Robotic Systems [Forthcoming publication]
2024
17th International Symposium on Distributed Autonomous Robotic Systems, New York, NY, USA, 2024-10-28 – 2024-10-30.Lumped Drag Model Identification and Real-Time External Force Detection for Rotary-Wing Micro Aerial Vehicles
This work focuses on understanding and identifying the drag forces applied to a rotary-wing Micro Aerial Vehicle (MAV). We propose a lumped drag model that concisely describes the aerodynamical forces the MAV is subject to, with a minimal set of parameters. We only rely on commonly available sensor information onboard a MAV, such as accelerometer data, pose estimate, and throttle commands, which makes our method generally applicable. The identification uses an offline gradient-based method on flight data collected over specially designed trajectories. The identified model allows us to predict the aerodynamical forces experienced by the aircraft due to its own motion in real-time and, therefore, will be useful to distinguish them from external perturbations, such as wind or physical contact with the environment. The results show that we are able to identify the drag coefficients of a rotary-wing MAV through onboard flight data and observe the close correlation between the motion of the MAV, the measured external forces, and the predicted drag forces.
2024 IEEE International Conference on Robotics and Automation (ICRA)
2024
2024 IEEE International Conference on Robotics and Automation, Yokohama, Japan, 2024-05-13 – 2024-05-17.p. 3853 – 3860
DOI : 10.1109/ICRA57147.2024.10610862
Publications – Additional Material
- Benchmarking and Experimental Validation of a Real-Time Multi-Robot Hybrid Coverage Algorithm for Known Environments (ICRA 26)
- Multi-Robot Online Coverage with a Team of Resource-Constrained Micro Aerial Vehicles (DARS 24)
- No additional material available.
- Lumped Drag Model Identification and Real-Time External Force Detection for Rotary-Wing Micro Aerial Vehicles (ICRA 24)
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