https://blog.pouceheure.fr/tags/robotics_autonomy/Recent content in Robotics_autonomy 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/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:https://blog.pouceheure.fr/articles/thesis/Thu, 28 Mar 2024 00:00:00 +0000https://blog.pouceheure.fr/articles/thesis/Abstract My thesis is based on shared navigation between the autonomous system and the human. In our research, we focus on command fusion. In our approach, both entities, the human and the autonomous system, simultaneously control the vehicle, and a module acquires their commands and performs the command fusion. This approach involves studying the intentions of both the human and the autonomous system to ensure the most appropriate fusion of their choices and to evaluate the decision-making of each entity.https://blog.pouceheure.fr/projects/intention-quantification/Sun, 10 Dec 2023 00:00:00 +0000https://blog.pouceheure.fr/projects/intention-quantification/Abstract As part of a collaborative research effort on shared control between an autonomous driving system and a human driver, I contributed to work focused on evaluating the quality of driving intentions. A driving intention is defined as a short sequence of planned control actions (speed, steering) over a time horizon: $$ I_t = \{(v,w)_{t+0\cdot\Delta t},(v,w)_{t+1\cdot\Delta t},...,(v,w)_{t+n\cdot\Delta t}\} \quad \text{Eq (1)} $$ For example, an intention can be expressed as a sequence of linear and angular velocities applied to the vehicle.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/cooperative-architecture/Sun, 01 Jan 2023 00:00:00 +0000https://blog.pouceheure.fr/articles/cooperative-architecture/Abstract A cooperative navigation architecture for the search and recognition of targets using aerial and ground vehicles is proposed in this paper. The architecture allows managing aerial and ground vehicles to autonomously perform different tasks independently or cooperatively. For our application, two main tasks are conceived: aerial monitoring of a surface to search for targets and target ground recognition. In the target aerial detection task, the aerial drone autonomously follows a trajectory computed to cover the entire surface to be monitored, searching for targets using vision algorithms.https://blog.pouceheure.fr/articles/general-multi-criteria/Sun, 01 Jan 2023 00:00:00 +0000https://blog.pouceheure.fr/articles/general-multi-criteria/Abstract Determining the admissibility and quality of driving intentions, generated by an automated intelligent vehicle and by a human driver, is a sine qua none task in a human-intelligent vehicle share navigation. Our paper proposes a generic method to quantify driving intentions. These intentions are defined by a sequence of velocities. This formulation is based on metrics already discussed in the literature. It proposes a way to use them to evaluate a state, making a judgment, and to extend this evaluation to a sequence of states.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/projects/multi-robots/Wed, 10 Aug 2022 00:00:00 +0000https://blog.pouceheure.fr/projects/multi-robots/Context As part of a research internship project, a modular ROS architecture was designed to enable cooperative navigation between heterogeneous autonomous vehicles: drones (UAVs) and ground robots (UGVs). The contribution focused on supporting the development and implementation of this system, which aimed to be scalable, with dynamic task allocation and coordinated execution across different hardware platforms. The architecture uses a unified communication protocol, distributed ROS nodes, and a namespace layout allowing multiple robots to be managed from a central ROS master.https://blog.pouceheure.fr/projects/human-prediction/Thu, 10 Mar 2022 00:00:00 +0000https://blog.pouceheure.fr/projects/human-prediction/Abstract A multimodal deep learning model was developed to predict human driving behavior over a short time horizon. The training dataset was recorded specifically for this project. Video 1 - Video demo, human driving behavior prediction. More details about this work are provided in PredictionPousseur 2022 . Introduction Driving intention is represented as a sequence of vehicle states. Let $I$ be: $$ I = \{x_0, x_1, ..., x_n\} \quad \text{Eq (1)} $$ Here, $(x_i) = (v_i, w_i)$ denotes the state at time $i$, where $v_i$ is the linear velocity and $w_i$ is the angular velocity.https://blog.pouceheure.fr/projects/lane-detection/Mon, 10 Jan 2022 00:00:00 +0000https://blog.pouceheure.fr/projects/lane-detection/Abstract Lane detection is an essential function for autonomous vehicles. While GPS and HD maps offer accurate localization under optimal conditions, vision-based detection provides an important backup, especially in areas with weak GNSS signals or outdated maps. This system implements a deep-learning lane detection pipeline using a convolutional autoencoder, designed to identify and segment multiple lane boundaries in various road conditions using only front-facing camera images. The approach integrates image preprocessing, deep-learning inference, optional temporal tracking, and curve fitting to generate stable and clean lane boundaries.https://blog.pouceheure.fr/articles/context-awareness/Sat, 01 Jan 2022 00:00:00 +0000https://blog.pouceheure.fr/articles/context-awareness/Abstract Many studies faced the problem of vehicle autonomous navigation in different fields, but nowadays just a few of them uses all the implicit information coming from the context in which such navigation is occurring. This results in a huge potential information loss that prevents us from adapting the vehicle’s behavior to each different situation it may be in. In a previous work, we defined a method to model the static context of navigation using ontologies and take it into account in the command law when performing a local navigation task.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/articles/human-driving-prediction/Sat, 01 Jan 2022 00:00:00 +0000https://blog.pouceheure.fr/articles/human-driving-prediction/Abstract Predicting the intentions of the human and the machine on a near future is required to the human-machine shared control of automated intelligent vehicles. The autonomous system is able to inform about its future intentions, however it is not possible for the human to provide this information, it is necessary then to predict it. This paper proposes a deep learning methodology to predict human navigation intentions in a time horizon of a few seconds, using a recurrent neural network (RNN) architecture based on the Long Short-Term Memory (LSTM) architecture.https://blog.pouceheure.fr/articles/context-modelling/Fri, 01 Jan 2021 00:00:00 +0000https://blog.pouceheure.fr/articles/context-modelling/Abstract This paper faces the problem of intelligent vehicles in interaction with their occupants and the environment, by modelling the semantic context associated with navigation. By semantically modelling context, an intelligent vehicle can not only drive itself safely but also reason about the situation and act accordingly. To achieve this, it is necessary to first define the Context of Navigation and then establish inference rules to enrich the vehicle’s understanding of the situation.https://blog.pouceheure.fr/articles/shared-decision/Fri, 01 Jan 2021 00:00:00 +0000https://blog.pouceheure.fr/articles/shared-decision/Abstract In this paper, a non-haptic shared control is applied to navigate an intelligent vehicle based on two inputs: one by a human driver and the other by an autonomous system. The proposed shared decision-making model aims to improve the safety and efficiency of autonomous vehicle navigation by integrating human intuition and machine precision. Experimental results demonstrate the effectiveness of this approach in various driving scenarios. :contentReference[oaicite:0]{index=0}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.