https://blog.pouceheure.fr/tags/ros2/Recent content in Ros2 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-lidar-and-decision/Tue, 14 Oct 2025 00:00:00 +0000https://blog.pouceheure.fr/projects/autosys-lidar-and-decision/Demonstration Video 1 - Video demo of the LiDAR Detection pipeline Info: A video demonstration of the decision-making system is coming soon. Introduction In autonomous driving, the vehicle must be able to make a series of decisions, such as slowing down, stopping at an intersection, or waiting until the road is clear before proceeding. These decisions depend on the behavior of other road users. The system must therefore identify surrounding obstacles and determine which ones are relevant for decision-making.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/dashboard/Thu, 01 May 2025 00:00:00 +0000https://blog.pouceheure.fr/projects/dashboard/Overview In robotics and system development, having a clear and flexible interface to visualize system states, sensor data, and real-time metrics is important. This project offers a lightweight dashboard framework designed for ROS 2, allowing quick creation of dashboards through simple configuration files. Video 1 - Demo video of the dashboard overlaid on video. The Video 1: Demo video of the dashboard overlaid on video. shows a demo of the dashboard running over a video stream.https://blog.pouceheure.fr/projects/rviz-virtual-camera/Thu, 01 May 2025 00:00:00 +0000https://blog.pouceheure.fr/projects/rviz-virtual-camera/Demonstration Video 1 - Demo: Example output from a virtual camera. The following demontration, Video 1: Demo: Example output from a virtual camera. , shows the video created from a virtual camera. The virtual camera is attached to a frame, fixed to vehicle. Motivation At first, I recorded the screen during experiments, but this was not a good solution because: process it’s more complex to setup, it uses too many resources, the video quality is low, I must keep the window and RViz view in the same place.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/autosys-local-planning/Tue, 10 Dec 2024 00:00:00 +0000https://blog.pouceheure.fr/projects/autosys-local-planning/Demonstration Video 1 - Demo video, pedestrian avoidance using lidar detection. Video 2 - Demo video, slalom test. Video 3 - Demo video, curve test. Overview As part of a collaborative autonomous driving project, I contributed to the implementation of the core planning functionalities for autonomous driving. The navigation stack transforms a target position into a safe and efficient motion trajectory using environmental context and map information. The process is divided into three stages: