Browsing by Author "Courtois, Hugo"

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    NDT RC: Normal Distribution Transform Occupancy 3D Mapping with recentering
    (IEEE, 2023-02-28) Courtois, Hugo; Aouf, Nabil; Ahiska, Kenan; Cecotti, Marco
    The Normal Distribution Transform Occupancy Map (NDT OM) is a mapping algorithm able to represent a dynamic 3D environment. The resulting map has fixed boundaries, thus a robot with unbounded displacement might fall outside of the map due to memory limitation. In this paper, a recentering algorithm called NDT RC is proposed to avoid this issue. NDT RC extends the use of NDT OM for vehicles with unbounded displacements. NDT RC provides a seamless translation of the map as the robot gets far from the center of the previous map. The influence of NDT RC on the precision of the estimated trajectory of the robot, or odometry, is examined on two publicly available datasets, the KITTI and Ford datasets. An analysis of the sensitivity of the NDT RC to its tuning parameters is carried out using the Ford dataset, while the KITTI dataset is used to measure the influence of the density of the input point cloud. The results show that the proposed recentering strategy improves the accuracy of the odometry calculated by registering the latest lidar scan on the generated map compared to other NDT based approaches (NDT OM, NDT OM Fusion, SE-NDT). In particular, the proposed method, which does not perform loop closure, reduces the mean absolute translation error by 16% and the runtime by 88% compared to the NDT OM Fusion on the Ford dataset.
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    OAST: Obstacle Avoidance System for Teleoperation of UAVs
    (IEEE, 2022-01-27) Courtois, Hugo; Aouf, Nabil; Ahiska, Kenan; Cecotti, Marco
    This article presents a novel flight assistance system, obstacle avoidance system for teleoperation (OAST), whose main role is to make teleoperation of small multirotor unmanned aerial vehicles (UAVs) safer and more efficient in closed spaces. The OAST allows the operator to avoid obstacles while keeping a liberty of movement. The UAV is controlled through a 3-D haptic controller and the OAST amends the user input to increase safety and efficiency of the teleoperation. The design of the OAST is verified in computerized experiments. Moreover, a simulation involving 20 participants is carried out to validate the proposed scheme. This experiment shows that the OAST improves the completion time of the scenarios by 41% on average while reducing the workload of the operator from 57 to 27 points on the NASA Task Load Index test. The number of collisions with the environment is all but eliminated in these scenarios.
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    Obstacle voidance for Unmanned Aerial Vehicles during teleoperation
    (2019-09) Courtois, Hugo; Aouf, Nabil
    Unmanned Aerial Vehicles (UAVs) use is on the rise, both for civilian and military applications. Autonomous UAV navigation is an active research topic, but human operators still provide a flexibility that currently matches or outperforms computers controlled aerial vehicles. For this reason, the remote control of a UAV by a human operator, or teleoperation, is an important subject of study. The challenge for UAV teleoperation comes from the loss of sensory information available for the operator who has to rely on onboard sensors to perceive the environment and the state of the UAV. Navigation in cluttered environment or small spaces is especially hard and demanding. A flight assistance framework could then bring significant benefits to the operator. In this thesis, an intelligent flight assistance framework for the teleoperation of rotary wings UAVs in small spaces is designed. A 3D haptic device serves as a remote control to improve ease of UAV manipulation for the operator. Moreover, the designed system provides benefits regarding three essential criteria: safety of the UAV, efficiency of the teleoperation and workload of the operator. In order to leverage the use of a 3D haptic controller, the initial obstacle avoidance algorithm proposed in this thesis is based on haptic feedback, where the feedback repels the UAV away from obstacles. This method is tested by human subjects, showing safety benefits but no manoeuvrability improvements. In order to improve on those criteria, the perception of the environment is studied using Light Detection And Ranging (LIDAR) and stereo cameras sensors data. The result of this led to the development of a mobile map of the obstacles surrounding the UAV using the LIDAR in addition to the stereo camera adopted to improve density. This map allows the creation of a flight assistance system that analyses and corrects the user’s inputs so that collisions are avoided while improving manoeuvrability. The proposed flight assistance system is validated through experiments involving untrained human subjects in a synthetically simulated environment. The results show that the proposed flight assistance system sharply reduces the number of collisions, the time required to complete the navigation task and the workload of the participants