Multi-target detection and recognition by UAVs using online POMDPs

This paper tackles high-level decision-making techniques for robotic missions, which involve both active sensing and symbolic goal reaching, under uncertain probabilistic environments and strong time constraints. Our case study is a POMDP model of an online multi-target detection and recognition mission by an autonomous UAV.The POMDP model of the multi-target detection and recognition problem is generated online from a list of areas of interest, which are automatically extracted at the beginning of the flight from a coarse-grained high altitude observation of the scene. The POMDP observation model relies on a statistical abstraction of an image processing algorithm's output used to detect targets. As the POMDP problem cannot be known and thus optimized before the beginning of the flight, our main contribution is an ``optimize-while-execute'' algorithmic framework: it drives a POMDP sub-planner to optimize and execute the POMDP policy in parallel under action duration constraints. We present new results from real outdoor flights and SAIL simulations, which highlight both the benefits of using POMDPs in multi-target detection and recognition missions, and of our`optimize-while-execute'' paradigm

Validation of real-time properties of a robotic software architecture

National audienceIn this paper, we propose a mechanism allowing to evaluate the schedulability of a robotic software architecture, and then validate its real-time properties. The robotic software architecture is described through a Domain Specific Language (DSL), MAUVE, that allows to model communicating components. The evaluation of schedulability of the architecture consists in first computing the Worst-Case Execution Time (WCET) of the elementary functions of the components. Then the Worst Case Response Time (WCRT) of the component is computed from the elementary WCET and the component models, allowing to validate the schedulatiblity of the architecture. We illustrate our methodology on the evaluation of a control architecture for a ground mobile robot

Un module de gestion de mission autonome pour des satellites d'observation de la Terre

International audienceThis paper presents an autonomous controller developed for managing the activities of a new generation of Earth Observing Satellites (EOSs). This controller uses a hierarchy of reactors as in previously existing architectures, and it exploits specific r easoning p rocedures a t t he l evel o f e ach r eactor to get fast deliberations on-board. It is able to take into account the arrival of urgent acquisition requests, late cloud predictions , and information about the real volume of data, while meeting several operational requirements from the end-users

Mauve: a Component-based Modeling Framework for Real-time Analysis of Robotic Applications.

Robots are more and more used in very diverse situations (services to persons, military missions, crisis management, . . . ) in which robots must give some guarantees of safety and reliability. To be really integrated in everyday life, robots must fulfil some requirements. Among these requirements, we focus on the nonfunctional requirements on embedded software [1], and more specifically on real-time software requirements. These requirements are most of the time fulfilled by proving the schedulability of the embedded software. Analysing and validating such properties on an existing hand-coded software requires some reverse modelling of the software, leading to approximations of its behaviour. These approximations may have certification authorities not be confident on the robot dependability. This paper proposes an integrated development methodology that starts from software component modelling, and leads to both validation of the embedded software and generation of deployable embedded software

Method and device for detecting piloting conflicts between the crew and the autopilot of an aircraft

According to the invention, the method comprises checking that the actual values of navigation parameters become closer to desired values within a predetermined time period. If this is not the case, the method comprises carrying out a predictive calculation of the value of at least one particular parameter at consecutive future moments, and optionally transmitting an alarm for the crew

Détection et reconnaissance de cibles en ligne pour des UAV autonomes avec un modèle de type POMDP

Cet article présente une mission pour la détection et reconnaissance de cibles menée par un véhicule aérien inhabité (UAV) autonome. La mission est modélisée par un Processus de Markov Partiellement Observable (POMDP). Le modèle POMDP traite dans un cadre unique des actions de perception (comme l'angle de prise de vue de la caméra) et des actions qui mènent à l'accomplissement de la mission (changement de zone, altitude de vol, atterrissage). La mission consiste à atterrir dans la zone qui contient une voiture dont le modèle reconnu est celui recherché, avec un état de croyance suffisant. Nous expliquons comment nous avons appris le modèle d'observation probabiliste du POMDP à partir d'une étude statistique des sorties de l'algorithme de traitement d'image. Cet algorithme utilisé pour reconnaître des objets dans la scène est embarquée sur notre UAV. Nous présentons aussi notre cadre \emph{optimize-while-executing}, qui administre un sous-planificateur POMDP pour optimiser et exécuter en parallèle la politique avec des contraintes de temps associées à la durée des actions, et qui raisonne sur les états futurs possibles du système robotique. Finalement, nos résultats expérimentaux sont présentés. Ils démontrent que des techniques d'intelligence artificielle comme les POMDP peuvent être appliquées avec succès pour contrôler automatiquement des actions de perception et d'accomplissement de mission pour des missions complexes en temps contraint pour un UAV autonome

POMDP-based online target detection and recognition for autonomous UAVs

This paper presents a target detection and recognition mission by an autonomous Unmanned Aerial Vehicule (UAV) modeled as a Partially Observable Markov Decision Process (POMDP). The POMDP model deals in a single framework with both perception actions (controlling the camera's view angle), and mission actions (moving between zones and flight levels, landing) needed to achieve the goal of the mission, i.e. landing in a zone containing a car whose model is recognized as a desired target model with sufficient belief. We explain how we automatically learned the probabilistic observation POMDP model from statistical analysis of the image processing algorithm used on-board the UAV to analyze objects in the scene. We also present our "optimize-while-execute" framework, which drives a POMDP sub-planner to optimize and execute the POMDP policy in parallel under action duration constraints, reasoning about the future possible execution states of the robotic system. Finally, we present experimental results, which demonstrate that Artificial Intelligence techniques like POMDP planning can be successfully applied in order to automatically control perception and mission actions hand-in-hand for complex time-constrained UAV missions

Planning for perception and perceiving for decision: POMDP-like online target detection and recognition for autonomous UAVs

This paper studies the use of POMDP-like techniques to tackle an online multi-target detection and recognition mission by an autonomous rotorcraft UAV. Such robotics missions are complex and too large to be solved off-line, and acquiring information about the environment is as important as achieving some symbolic goals. The POMDP model deals in a single framework with both perception actions (controlling the camera's view angle), and mission actions (moving between zones and flight levels, landing) needed to achieve the goal of the mission, i.e. landing in a zone containing a car whose model is recognized as a desired target model with sufficient belief. We explain how we automatically learned the probabilistic observation POMDP model from statistical analysis of the image processing algorithm used on-board the UAV to analyze objects in the scene. We also present our "optimize-while-execute" framework, which drives a POMDP sub-planner to optimize and execute the POMDP policy in parallel under action duration constraints, reasoning about the future possible execution states of the robotic system. Finally, we present experimental results, which demonstrate that Artificial Intelligence techniques like POMDP planning can be successfully applied in order to automatically control perception and mission actions hand-in-hand for complex time-constrained UAV missions