https://blog.pouceheure.fr/tags/planning_navigation/Recent content in Planning_navigation on Hugo POUSSEURHugoenTue, 10 Dec 2024 00:00:00 +0000https://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/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/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/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/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/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}