The robustness of object detection algorithms plays a prominent role in real-world applications, especially in uncontrolled environments due to distortions during image acquisition. It has been proven that the performance of object detection methods suffers from in-capture distortions. In this study, we present a performance evaluation framework for the state-of-the-art object detection methods using a dedicated dataset containing images with various distortions at different levels of severity. Furthermore, we propose an original strategy of image distortion generation applied to the MS-COCO dataset that combines some local and global distortions to reach much better performances. We have shown that training using the proposed dataset improves the robustness of object detection by 31.5%. Finally, we provide a custom dataset including natural images distorted from MS-COCO to perform a more reliable evaluation of the robustness against common distortions. The database and the generation source codes of the different distortions are made publicly available1,2.

This paper addresses the problem of stabilizing and trajectory tracking control of a remotely operated vehicle (ROV-Observer) in the vertical plane. The underwater vehicle is not actuated in the pitch direction and cannot be locally asymptotically stabilized by continuous feedbacks which are functions of the state only. In the present note, exponential convergence is obtained by considering time-varying feedbacks which are only continuous. Furthermore, using the backstepping technique and the tracking error dynamics, the system states are stabilized by forcing the tracking errors to an arbitrarily small neighborhood of zero.

This paper presents a method to design an observer-based controller that simultaneously solves global estimation of state and asymptotic stabilization of an underactuated remotely operated vehicle moving in the in three-dimensional. The vehicle does not have a sway and roll actuator and has only position and orientation measurements available. The control development is based on Lyapunov's direct method for nonlinear system.

We present in this paper the stabilization (tracking) with motion planning of the six independent configurations of a mini unmanned aerial vehicle equipped with four streamlined rotors. Naturally, the yaw-dynamic can be stabilized without difficulties and independently of other motions. The remaining dynamics are linearly approximated around a small roll and pitch angles. It will be shown that the system presents a flat output that is likely to be useful in the motion generation problem. The tracking feedback controller is based on receding horizon point to point steering. The resulting controller involves the lift (collective) time derivative for what flatness and feedback linearization are used. Simulation tests are performed to progress in a region with approximatively ten-meter-buildings.

The tracking control problem of a parallel robot including the electrical actuator dynamics is addressed in the task space. For the electrically-actuated robots, we design a nonlinear control law for armature input voltages. The control technique consists of a cartesian tracking control and a force convergent control. The model obtained is in standard form to allow the application of singular perturbation methods. To validate the proposed corrective controller, a passivity concept and singular perturbation techniques are combined successfully. Simulation results show a good behavior of the proposed task space tracking controller.