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

Planning for perception and perceiving for decision: POMDP-like online optimization in large complex robotics missions

This ongoing phD work aims at proposing a unified framework to optimize both perception and task planning using extended Partially Observable Markov Decision Processes (POMDPs). Targeted applications are large complex aerial robotics missions where the problem is too large to be solved off-line, and acquiring information about the environment is as important as achieving some symbolic goals. Challenges of this work include: (1) optimizing a dual objective in a single decision-theoretic framework, i.e. environment perception and goal achievement ; (2) properly dealing with action preconditions on belief states in order to guarantee safety constraints or physical limitations, what is crucial in aerial robotics ; (3) modeling the symbolic output of image processing algorithms as input of the POMDP's observation function ; (4) parallel optimization and execution of POMDP policies in constrained time. A global view of each of these topics are presented, as well as some ongoing experimental results

Influence of bioactive particles and onium salt on physical-chemical properties of experimental infiltrants

Orientador: Giselle Maria Marchi BaronTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Odontologia de PiracicabaResumo: O objetivo do estudo foi avaliar a influência do hexafluorofosfato de difeniliodônio (DFI) e da adição do vidro bioativo 58S (VBA) e nanopartículas de hidroxiapatita (HAp) nas propriedades físicas e bioatividade de infiltrantes experimentais. Seis grupos experimentais e um grupo comercial foram utilizados no estudo (Icon). Os grupos experimentais foram compostos de 75% TEGDMA e 25% Bis-EMA e o sistema fotoiniciador de 0,5 mol% de canforoquinona (CQ) e 1 mol% dimetilaminoetil benzoato (EDAB). Após a manipulação das bases monoméricas, foram adicionados ou não 0,5mol% de DFI, 10% de BAG produzido pelo método sol-gel ou nanopartículas de HAp. Todas as manipulações foram realizadas em ambiente com temperatura controlada (25 ºC). A cristalinidade dos infiltrantes foi avaliada qualitativamente após imersão das amostras em FCS (Fluido Corporal Simulado) em diferentes períodos (6h, 1 dia, 3 dias e 7 dias) por meio da difração de raios X (DRX), espectrometria FT-IR e MEV para caracterização das partículas (n=1). Foram realizadas a avaliação da cinética de polimerização e o grau de conversão (GC, n=3), sorção em água, solubilidade (n=10), e viscosidade (n=3). Todos os corpos de prova foram polimerizados com fonte de luz LED, durante 40 segundos (Valo, Ultradent) para os corpos de prova imersos em FCS e para sorção e solubilidade. O tempo total de fotoativação para a cinética de polimerização foi de 5 minutos. Para as análises quantitativas, comparações estatísticas entre os grupos foram feitas usando análise de variância (ANOVA 1 fator) e teste de Tukey, com significância de 5%. Para todos os tempos de imersão no FCS, não foi detectada presença de cristalinidade nos grupos com VBA. DRX e FT-IR demonstraram presença de fase cristalina da HAp nos grupos com HAp. Icon e grupo com VBA sem DFI apresentaram menor grau de conversão em 40s (< 50%) e taxa de polimerização, enquanto a presença de HAp aumentou esses valores. DFI só aumentou a taxa de polimerização e GC em 40s para o grupo com VBA. Após 5 minutos de fotoativação todos os grupos apresentaram GC acima de 80%. Grupos com HAp apresentaram maior viscosidade, porém DFI diminuiu a viscosidade para os grupos com partículas. As partículas não influenciaram a sorção de água. A maior sorção de água foi apresentada pelo Icon. Não houve diferenças estatísticas para os valores de solubilidade. Pode-se concluir que a adição de 10% de VBA não melhora as propriedades físico-químicas estudadas nem produz efeito bioativo nos infiltrantes testados. Além disso, o DFI reduz a viscosidade gerada pela adição de partículas, bem como atenua a diminuição do GC promovida pela adição de VBAAbstract: The objective of this study was to evaluate the influence of diphenyliodonium hexafluorophosphate (onium salt - DPI) and the addition of 58S bioactive glass (BAG) and hydroxyapatite nanoparticles (HAp) on physical properties and bioactivity of experimental infiltrants. Six experimental groups and one commercial control (Icon) were studied. The experimental groups were composed by 75% wt TEGDMA and 25% wt Bis-EMA, the photoinitiator system was 0.5 mol% camphorquinone (CQ) and 1 mol% dimethylaminoethyl benzoate (EDAB). After manipulation of the blends, 0.5 mol% DPI, 10% BAG produced by the sol-gel method or HAp nanoparticles were added or not. Icon was used as a commercial control. All manipulations were performed in an environment with temperature (25 ºC). The crystallinity of the infiltrants was qualitatively evaluated after immersion of the samples in SBF (Stimulated Body Fluid) at different periods (6h, 1 day, 3 days and 7 days) by means of X-ray diffraction (XRD), FT-IR spectrometry and SEM for characterization of the particles (n=1). Polymerization kinetics and degree of conversion (DC, n= 3), water sorption and solubility (n= 10) and viscosity (n = 3) were performed. All specimens were polymerized with LED light source for 40 seconds (Valo, Ultradent) for samples immersed in SBF and water sorption and solubility. For polymerization kinetics the total time of photoactivation was 5 minutes. Statistical comparisons between groups were made using analysis of variance (one-way ANOVA) and Tukey's test with significance of 5%. After all periods of immersion in the SBF, no crystallinity was detected in the groups with BAG. XRD and FT-IR demonstrated presence of HAp crystalline phase in HAp groups. Icon and group B-BAG showed a lower degree of conversion (DC) in 40s (<50%) and polymerization rate, while the presence of HAp increased these values. DPI only increased the polymerization rate and DC in 40s for the BAG group. After 5 minutes of photoactivation, all groups presented DC above 80%. HAp groups showed higher viscosity, but DPI decreased the viscosity for groups with particles. The particles did not influence the sorption. The highest water sorption was presented by Icon. There were no statistical differences for solubility values. It can be concluded that the addition of 10% 58S BAG does not improve the physical-chemical properties studied nor produce bioactive effect. DPI reduces the viscosity presented by particles addition besides attenuate the DC decreasing promoted by BAG additionDoutoradoDentísticaDoutora em Clínica Odontológica140950/2017-688881.188490/2018-12017/14378-6CNPQCAPESFAPES

Backstepping control law application to path tracking with an indoor quadrotor

This paper presents an application of the backstepping control to a path tracking mission using an indoor quadrotor. This study case starts on modeling the quadrotor dynamics in order to design a backstepping control which we applied directly to the Lagrangian dynamic equations. The backstepping control is chosen due to its applicability to this class of nonlinear and under-actuate system. To test the designed control law, a complete quadrotor model identification was performed, using a motion capture system. The procedure used to obtain a good model approximation is presented. Experimental results illustrate the validity of the designed control law, including rich simulations and real indoor flight tests

Creative Methods to Envision Nursing Practices Addressing Antimicrobial Resistance (AMR): a report on the use of arts and humanities approaches to co-design healthcare service innovation

Re-envisaging Infection Practice Ecologies in Nursing (RIPEN) through Arts and Humanities Approaches is a collaborative research project that has used a novel combination of methods to explore and develop nursing’s engagement with the pressing problem of antimicrobial resistance (AMR). This report presents an overview of the rationale for, and design of, the project before featuring the methods used in the workshops that were central to its progress. In the third section we reflect on the learning that has accrued and this is then summarised in the final section along with specific recommendations. In presenting this material we hope to give the reader insight into how we have addressed RIPEN’s central question: How can relevant arts and humanities-based approaches help nurses to re-envisage their infection control practice ecologies in response to antimicrobial resistance? We believe this should be of relevance to four main audiences: Nursing and healthcare professionals engaged in practice, education and/or research to address antimicrobial resistance and infection prevention Designers, artists and researchers using and developing creative methodologies applied to healthcare practice and service improvement. Policymakers, activists, public officials and funders seeking to further understand and explore the creative potential of innovative approaches to complex healthcare challenges. Communities of practice interested in exploring the use of co-design and visual methods to understand complex challenges and opportunities in healthcare

Décision séquentielle pour la perception active : p-POMDP versus POMDP

Cet article propose une étude du compromis entre la prise d’information et la décision dans un cadre applicatif qui se rapporte à une mission d’exploration, où l’agent interagit avec son environnement pour identifier l’état caché du système. Dans ce problème de décision séquentielle pour la perception, il est possible de faire reposer la fonction de récompense sur une mesure de l’incertitude sur l’état de croyance de l’agent (Araya-López et al., 2010; Candido & Hutchinson, 2011; Eidenberger & Scharinger, 2010). Sa forme est donc différente de celle utilisée dans le cadre classique des POMDP qui est, pour sa part, basée sur la paire état-action. Nous comparons donc deux approches d’optimisation des politiques pour ce type de problème. D’une part nous proposons un critère mixte qui couple une mesure de l’incertitude sur l’état de croyance avec les récompenses définies par les paires état-action et nous développons un schéma algorithmique de résolution pour ce critère. D’autre part, nous proposons d’ajouter au modèle des états but fictifs au moyen des actions de classification afin de revenir à une modélisation sous-forme de POMDP classique (critère non mixte). Une étude comparative de ces approches est ici présentée afin de vérifier leur équivalence en termes de prise d’informations. Les résultats nous mènent à conclure que ces approches sont non seulement comparables et équivalentes en termes de réduction d’incertitude, mais aussi, qu’elles peuvent être utilisées en parfaite complémentarité de façon à permettre : de caractériser une politique correspondant aux taux acceptables des bonnes et mauvaises classifications et de déterminer les bonnes valeurs des coûts et des récompenses du modèle POMDP classique

Towards a MOMDP model for UAV safe path planning in urban environment

This paper tackles a problem of UAV safe path planning in an urban environment in which UAV is at risks of GPS signal occlusion and obstacle collision. The key idea is to perform the UAV path planning along with its navigation and guidance mode planning, where each of these modes uses different sensors whose availability and performance are environment-dependent. A partial knowledge on the environment is supposed to be available in the form of probability maps of obstacles and sensor availabilities. This paper proposes a planner model based on Mixed Observability Markov Decision Process (MOMDP). It allows the planner to propagate such probability map information to the future path for choosing the best action. This paper provides a MOMDP model for the planner with an approximation of the belief states by Mixture of Gaussian functions

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

Optimisation des Processus Décisionnels de Markov Partiellement Observables avec prise en compte explicite du gain d’information

Traditionnellement, les travaux de recherche en décision séquentielle dans l'incertain avec observabilité partielle reposent sur les Processus Décisionnels de Markov Partiellement Observables (POMDP), optimisés avec un critère de maximisation de revenus cumulés pondérés sur un horizon d'action donné. Or, ce critère est pessimiste dans la mesure où la décision est optimisée sur une distribution de probabilité sur l'état de croyance de l'agent autonome, sans que l'algorithme ne réduise explicitement cette incertitude. Autrement dit, les critères classiques d'optimisation des POMDP raisonnent sur toutes les hypothèses possibles, sans favoriser explicitement les actions qui pourraient acquérir de l'information et réduire le champ d’hypothèses. Au contraire, les travaux en traitement d'image et particulièrement en perception active s'intéressent plutôt à trouver les actions qui minimisent l'entropie de croyance, c'est-à-dire l'incertitude sur l'état caché, mais sans optimiser une récompense globale liée à la mission du robot. Ainsi, afin de résoudre au mieux des problèmes robotiques alliant à la fois des objectifs de perception et de mission, nous proposons deux nouveaux critères mixtes, l'un additif et l’autre multiplicatif, qui agrègent les récompenses cumulées (mission) et les entropies de croyance cumulées (perception), toutes deux pondérées sur un horizon d'action commun. À l'aide d’évaluations statistiques sur plusieurs exécutions de la politique optimisée, nous montrons que nos critères mixtes sont optimaux par rapport à un critère purement entropique, et que le critère additif améliore même un critère basé purement sur les récompenses de la mission. Ce dernier point démontre que le critère classique, qui repose uniquement sur les récompenses cumulées, n'est pas optimal lors de l’exécution, car il ne prend pas en compte explicitement le gain d'information et la réduction de l’incertitude sur l'état caché du système

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