Autonomous search and rescue rotorcraft mission stochastic planning with generic DBNs

This paper proposes an original generic hierarchical framework in order to facilitate the modeling stage of complex autonomous robotics mission planning problems with action uncertainties. Such stochastic planning problems can be modeled as Markov Decision Processes [5]. This work is motivated by a real application to autonomous search and rescue rotorcraft within the ReSSAC1 project at ONERA. As shown in Figure 1.a, an autonomous rotorcraft must y and explore over regions, using waypoints, and in order to nd one (roughly localized) person per region (dark small areas). Uncertainties can come from the unpredictability of the environment (wind, visibility) or from a partial knowledge of it: map of obstacles, or elevation map etc. After a short presentation of the framework of structured Markov Decision Processes (MDPs), we present a new original hierarchical MDP model based on generic Dynamic Bayesian Network templates. We illustrate the bene ts of our approach on the basis of search and rescue missions of the ReSSAC project.IFIP International Conference on Artificial Intelligence in Theory and Practice - Planning and SchedulingRed de Universidades con Carreras en Informática (RedUNCI

Autonomous search and rescue rotorcraft mission stochastic planning with generic DBNs

This paper proposes an original generic hierarchical framework in order to facilitate the modeling stage of complex autonomous robotics mission planning problems with action uncertainties. Such stochastic planning problems can be modeled as Markov Decision Processes [5]. This work is motivated by a real application to autonomous search and rescue rotorcraft within the ReSSAC1 project at ONERA. As shown in Figure 1.a, an autonomous rotorcraft must y and explore over regions, using waypoints, and in order to nd one (roughly localized) person per region (dark small areas). Uncertainties can come from the unpredictability of the environment (wind, visibility) or from a partial knowledge of it: map of obstacles, or elevation map etc. After a short presentation of the framework of structured Markov Decision Processes (MDPs), we present a new original hierarchical MDP model based on generic Dynamic Bayesian Network templates. We illustrate the bene ts of our approach on the basis of search and rescue missions of the ReSSAC project.IFIP International Conference on Artificial Intelligence in Theory and Practice - Planning and SchedulingRed de Universidades con Carreras en Informática (RedUNCI

A Review of Deep Learning Methods and Applications for Unmanned Aerial Vehicles

Deep learning is recently showing outstanding results for solving a wide variety of robotic tasks in the areas of perception, planning, localization, and control. Its excellent capabilities for learning representations from the complex data acquired in real environments make it extremely suitable for many kinds of autonomous robotic applications. In parallel, Unmanned Aerial Vehicles (UAVs) are currently being extensively applied for several types of civilian tasks in applications going from security, surveillance, and disaster rescue to parcel delivery or warehouse management. In this paper, a thorough review has been performed on recent reported uses and applications of deep learning for UAVs, including the most relevant developments as well as their performances and limitations. In addition, a detailed explanation of the main deep learning techniques is provided. We conclude with a description of the main challenges for the application of deep learning for UAV-based solutions

Punctual versus continuous auction coordination for multi-robot and multi-task topological navigation

International audienceThis paper addresses the interest of using Punctual versus Continuous coordination for mobile multi-robot systems where robots use auction sales to allocate tasks between them and to compute their policies in a distributed way. In Continuous coordination, one task at a time is assigned and performed per robot. In Punctual coordination, all the tasks are distributed in Rendezvous phases during the mission execution. However , tasks allocation problem grows exponentially with the number of tasks. The proposed approach consists in two aspects: (1) a control architecture based on topo-logical representation of the environment which reduces the planning complexity and (2) a protocol based on Sequential Simultaneous Auctions (SSA) to coordinate Robots' policies. The policies are individually computed using Markov Decision Processes oriented by several goal-task positions to reach. Experimental results on both real robots and simulation describe an evaluation of the proposed robot architecture coupled wih the SSA protocol. The efficiency of missions' execution is empirically evaluated regarding continuous planning

Computational Algorithm for Dynamic Hybrid Bayesian Network in On-line System Health Management Applications

With the increasing complexity of today's engineering systems that contain various component dependencies and degradation behaviors, there has been increasing interest in on-line System Health Management (SHM) capability to continuously monitor (via sensors and other methods of observation) system software, and hardware components for detection and diagnostic of safety-critical systems. Bayesian Network (BN) and their extension for time-series modeling known as Dynamic Bayesian Network (DBN) have been shown by recent studies to be capable of providing a unified framework for system health diagnosis and prognosis. BN has many modeling features, such as multi-state variables, noisy gates, dependent failures, and general posterior analysis. BN also allows a compact representation of the temporal and functional dependencies among system components. However, one of the barriers to applying BN in real-world problems is limitation in adequately handle "hybrid models", which contain both discrete and continuous variables, with both static and time-dependent failure distributions. This research presents a new modeling approach, computational algorithm, and an example application for health monitoring and learning in on-line SHM. A hybrid DBN is introduced to represent complex engineering systems with underlying physics of failure by modeling a theoretical or empirical degradation model with continuous variables. The methodology is designed to be flexible and intuitive, and scalable from small, localized functionality to large complex dynamic systems. Markov Chain Monte Carlo (MCMC) inference is optimized using a pre-computation strategy and dynamic programming for on-line monitoring of system health. Proposed Monitoring and Anomaly Detection algorithm uses pattern recognition to improve failure detection and estimation of Remaining Useful Life (RUL). Pre-computation inference database enables efficient on-line learning and maintenance decision-making. The scope of this research includes a new modeling approach, computation algorithm, and an example application for on-line SHM

Collision Avoidance on Unmanned Aerial Vehicles using Deep Neural Networks

Unmanned Aerial Vehicles (UAVs), although hardly a new technology, have recently gained a prominent role in many industries, being widely used not only among enthusiastic consumers but also in high demanding professional situations, and will have a massive societal impact over the coming years. However, the operation of UAVs is full of serious safety risks, such as collisions with dynamic obstacles (birds, other UAVs, or randomly thrown objects). These collision scenarios are complex to analyze in real-time, sometimes being computationally impossible to solve with existing State of the Art (SoA) algorithms, making the use of UAVs an operational hazard and therefore significantly reducing their commercial applicability in urban environments. In this work, a conceptual framework for both stand-alone and swarm (networked) UAVs is introduced, focusing on the architectural requirements of the collision avoidance subsystem to achieve acceptable levels of safety and reliability. First, the SoA principles for collision avoidance against stationary objects are reviewed. Afterward, a novel image processing approach that uses deep learning and optical flow is presented. This approach is capable of detecting and generating escape trajectories against potential collisions with dynamic objects. Finally, novel models and algorithms combinations were tested, providing a new approach for the collision avoidance of UAVs using Deep Neural Networks. The feasibility of the proposed approach was demonstrated through experimental tests using a UAV, created from scratch using the framework developed.Os veículos aéreos não tripulados (VANTs), embora dificilmente considerados uma nova tecnologia, ganharam recentemente um papel de destaque em muitas indústrias, sendo amplamente utilizados não apenas por amadores, mas também em situações profissionais de alta exigência, sendo expectável um impacto social massivo nos próximos anos. No entanto, a operação de VANTs está repleta de sérios riscos de segurança, como colisões com obstáculos dinâmicos (pássaros, outros VANTs ou objetos arremessados). Estes cenários de colisão são complexos para analisar em tempo real, às vezes sendo computacionalmente impossível de resolver com os algoritmos existentes, tornando o uso de VANTs um risco operacional e, portanto, reduzindo significativamente a sua aplicabilidade comercial em ambientes citadinos. Neste trabalho, uma arquitectura conceptual para VANTs autônomos e em rede é apresentada, com foco nos requisitos arquitetônicos do subsistema de prevenção de colisão para atingir níveis aceitáveis de segurança e confiabilidade. Os estudos presentes na literatura para prevenção de colisão contra objectos estacionários são revistos e uma nova abordagem é descrita. Esta tecnica usa técnicas de aprendizagem profunda e processamento de imagem, para realizar a prevenção de colisões em tempo real com objetos móveis. Por fim, novos modelos e combinações de algoritmos são propostos, fornecendo uma nova abordagem para evitar colisões de VANTs usando Redes Neurais Profundas. A viabilidade da abordagem foi demonstrada através de testes experimentais utilizando um VANT, desenvolvido a partir da arquitectura apresentada