https://blog.pouceheure.fr/tags/control_optimization/Recent content in Control_optimization on Hugo POUSSEURHugoenMon, 10 Nov 2025 00:00:00 +0000https://blog.pouceheure.fr/projects/autosys-teleop/Mon, 10 Nov 2025 00:00:00 +0000https://blog.pouceheure.fr/projects/autosys-teleop/Demonstration Video Video 1 - Teleoperation Demo. (Train Demo) This Video 1: Teleoperation Demo. (Train Demo) illustrates the longitudinal control of the car via teleoperation. Example of speed saturation: 0:07 - 0:16 (limited by lateral acceleration) Example of braking: 1:20 (vehicle is stopped by teleop command) Context The vehicle autonomously plans a path and follows it. A teleoperator controls only the longitudinal speed using a joystick, similar to driving a train.https://blog.pouceheure.fr/projects/autosys-traffic-light-detection/Thu, 15 May 2025 00:00:00 +0000https://blog.pouceheure.fr/projects/autosys-traffic-light-detection/Motivation As part of an autonomous driving project, I contributed to the development of a system enabling a vehicle to adapt its behavior to traffic lights. The vehicle adjusts its speed according to the current traffic light state. The project is divided into four main components: Matching: Identifying traffic light positions on the map Detection: Determining the current state of the traffic light Planning: Adjusting vehicle speed according to the detection result Control: Applying the computed velocity to the vehicle The detection pipeline is implemented in Python, while control is handled in C++ within a ROS2 environment.https://blog.pouceheure.fr/projects/autosys-control/Fri, 20 Dec 2024 00:00:00 +0000https://blog.pouceheure.fr/projects/autosys-control/Demonstration Video 1 - Demo video: planning and control in action. Overview As part of a collaborative autonomous driving project, I contributed to the low-level control system responsible for translating navigation commands into actuator signals (steering and torque). This system is fully integrated into a ROS2-based autonomous stack and has been deployed and tested on a real electric vehicle with in-wheel motors. Fig. 1 - Interfaces between planning and control modules.https://blog.pouceheure.fr/projects/visual-control/Wed, 01 Feb 2023 00:00:00 +0000https://blog.pouceheure.fr/projects/visual-control/Abstract As part of a research project, I contributed to the development and testing of a Visual Servoing framework, a control strategy that links image feature variations to robot velocities. Applied to autonomous vehicles, this approach enables lane-keeping purely from camera input, without GPS or map dependency. It integrates lane detection using deep learning and transforms the output into features used in a control law that centers the vehicle in the lane.https://blog.pouceheure.fr/articles/machine-learning-controller/Sun, 01 Jan 2023 00:00:00 +0000https://blog.pouceheure.fr/articles/machine-learning-controller/Abstract Researchers have been attempting to make the car drive autonomously. The environment perception together with safe guidance and control is an important task and are one of the big challenges when developing this kind of system. Geometrical or physical based models, machine learning based models and those based on a mixture of both models, are the three types of navigation methods used to resolve this problem. The last method takes advantage of the learning capability of machine learning models and uses the safeness of geometric models in order to better perform the navigation task.https://blog.pouceheure.fr/articles/camera-based-control/Sun, 01 Jan 2023 00:00:00 +0000https://blog.pouceheure.fr/articles/camera-based-control/Abstract By utilizing the camera installed in the front of a vehicle, this paper proposes on-board camera-based driving force control (CDFC) methods for autonomous electric vehicles driven by in-wheel motors. The image processing algorithm can detect the change in road surface conditions quickly and accurately. This enables the CDFC to update the slip-ratio limiter in real-time. Test results show that the proposed methods can improve traction control performance and reduce inverter input energy.https://blog.pouceheure.fr/articles/gradient-descent/Sat, 01 Jan 2022 00:00:00 +0000https://blog.pouceheure.fr/articles/gradient-descent/Abstract Avoiding obstacles is a key feature in a vehicle autonomous navigation methodology. The Dynamic Window Approach (DWA), proposed several decades ago, has emerged as a responsive navigation methodology suitable for reactive obstacle avoidance. In the initial approach of the DWA, the optimization of an objective function is realized with an exhaustive computation, which can be costly in computational time. This is not useful in a real-time scenario where an autonomous vehicle needs to avoid obstacles in urban or road velocity conditions.https://blog.pouceheure.fr/articles/motion-control/Sat, 01 Jan 2022 00:00:00 +0000https://blog.pouceheure.fr/articles/motion-control/Abstract In this paper, a navigation control for a platoon composed of a ground and an aerial vehicle is proposed. The aim of this work is to define a platooning system that exploits the advantages of both types of robots: while the aerial drone is faster and is not affected by the terrain, the ground vehicle has more autonomy and can carry heavier payloads. The proposed control system allows the aerial vehicle to follow the ground vehicle, maintaining a desired relative position, and to assist in navigation by providing information about the environment.https://blog.pouceheure.fr/projects/dwa-optimization/Fri, 10 Dec 2021 00:00:00 +0000https://blog.pouceheure.fr/projects/dwa-optimization/Overview The Dynamic Window Approach (DWA) is a reactive motion planning method used in mobile robotics. At each control cycle, it samples admissible velocity pairs $(v, \omega)$ and evaluates them using an objective function, selecting the pair with the highest score. The original method presents two main issues: Discrete sampling, which limits precision High computational cost in evaluation loops These limitations can be addressed by defining a convex, differentiable objective function and applying gradient descent for continuous optimization.https://blog.pouceheure.fr/projects/drone-control/Fri, 01 Feb 2019 00:00:00 +0000https://blog.pouceheure.fr/projects/drone-control/Overview This project was part of a final-year master’s program and involved developing a drone capable of autonomously flying over a predefined area. The work was split into two main parts: path planning and control. My responsibility was to control a Parrot Bebop drone using ROS, leveraging an existing ROS package that communicates with Parrot’s SDK. Video 1 - Drone control demonstration. Additional Features Controller Watchdog: Added a manual override system allowing the operator to take full control of the drone if the autonomous system fails.