Using the DiaSpec design language and compiler to develop robotics systems

A Sense/Compute/Control (SCC) application is one that interacts with the physical environment. Such applications are pervasive in domains such as building automation, assisted living, and autonomic computing. Developing an SCC application is complex because: (1) the implementation must address both the interaction with the environment and the application logic; (2) any evolution in the environment must be reflected in the implementation of the application; (3) correctness is essential, as effects on the physical environment can have irreversible consequences. The SCC architectural pattern and the DiaSpec domain-specific design language propose a framework to guide the design of such applications. From a design description in DiaSpec, the DiaSpec compiler is capable of generating a programming framework that guides the developer in implementing the design and that provides runtime support. In this paper, we report on an experiment using DiaSpec (both the design language and compiler) to develop a standard robotics application. We discuss the benefits and problems of using DiaSpec in a robotics setting and present some changes that would make DiaSpec a better framework in this setting.Comment: DSLRob'11: Domain-Specific Languages and models for ROBotic systems (2011

A framework to manage environment models in multi-robot teams

International audienceEnvironment models are the primary matter to autonomous decisions for mobile robots, and also to cooperation within teams of robots that operate in the same environment. The decisions to take within a robot or a robot team relate to motions, perceptions and communications: various types of environment models are therefore required to evaluate and plan these actions. While the literature abounds with approaches to environment modeling using data perceived by the robots, very few work tackle the problem of managing such models within a team of robots. Managing environment models implies defining the proper data structures and associated mechanisms that allow their efficient update and use by the decisional processes that require them, ensuring the models consistency as the robots evolve. This article presents on-going work on the definition of a framework dedicated to the managing of environment models within a robot team. It establishes some principles for such a framework, proposes data structures to store and manage data and models, and is illustrated by some simple examples

Managing environment models in multi-robot teams

International audienceEnvironment models are the primary matter to autonomous decisions for mobile robots, and also to cooperation within teams of robots that operate in the same environment. The decisions to take within a robot or a robot team relate to motions, perceptions and communications: various types of environment models are therefore required to evaluate and plan these actions. While the literature abounds with approaches to environment modeling using data perceived by the robots, very few work tackle the problem of managing such models within a team of robots. Managing environment models implies first defining the proper data structures and associated mechanisms that allow both their efficient update and use by the decisional processes that require them, and second ensuring the models consistency as the robots evolve. This article presents the definition of a framework dedicated to the managing of environment models within a robot team. It establishes the principles that govern the framework design, and illustrates them throughout some examples

Managing environment models in multi-robot teams

International audienceMulti-robot cooperation in a dynamic environment implies that each involved robot is able to create, update and exchange environment models – which are essential to the autonomy of mobile robots. The ability to exchange data, in other words to communicate, rely on the fact that robots share a common language and common frames in space and time. In this paper we present an open-source architecture dedicated to build a collection of environment models of a dynamic environment with multiple robots. This work is lead with air-ground cooperation scenarios in mind, such as the ones considered within the Action project. Managing models during the mission means defining methods and protocols to transmit and merge models built from different sources and on different robots, while respecting time and bandwidth constraints and being robust to unavoidable inconsistencies between the models. Our first work builds upon GDAL, a well known standard in geographic information systems (GIS). The architecture is currently able to build models with different robots in simulation. Current work aims at detecting and solving inconsistencies between the various models, and at porting the architecture on-board real robots

ICP-based pose-graph SLAM

International audienceOdometry-like localization solutions can be built upon Light Detection And Ranging (LIDAR) sensors, by sequentially registering the point clouds acquired along a robot trajectory. Yet such solutions inherently drift over time: we propose an approach that adds a graphical model layer on top of such LIDAR odometry layer, using the Iterative Closest Points (ICP) algorithm for registration. Reference frames called keyframes are defined along the robot trajectory, and ICP results are used to build a pose graph, that in turn is used to solve an optimization problem that provides updates for the keyframes upon loop closing, enabling the correction of the path of the robot and of the map of the environment. We present in details the configuration used to register data from the Velodyne High Definition LIDAR (HDL), and a strategy to build local maps upon which current frames are registered, either when discovering new areas or revisiting previously mapped areas. Experiments show that it is possible to build the graph using data from ICP and that the loop closings in the graph level reduces the overall drift of the system

Integrating realistic simulation engines within the MORSE framework

International audienceThe complexity of robotics comes from the tight interactions between hardware, complex softwares, and environments. While real world experience is the only way to assess the efficiency and robustness of a robotics system, simulations help to pave the way to actual experiments. But an overall robotics system requires simulations at a level of realism which no holistic simulator can provide, given the wide spectrum of disciplines and physical processes involved. This paper presents a way to integrate various simulators, in a distributed, scalable and repeatable way, to benefit from their different advantages and get the best fitted and accurate simulation for a given robotics system. It depicts how the MORSE open-source robotics simulator is adapted to comply with the High Level Architecture standard, thus allowing the reuse of numerous dedicated realistic simulators. Two examples of the integration of simulators are provided

Simulating complex robotic scenarios with MORSE

International audienceMORSE is a robotic simulation software developed by roboticists from several research laboratories. It is a framework to evaluate robotic algorithms and their integration in complex environments, modeled with the Blender 3D real-time engine which brings realistic rendering and physics simulation. The simulations can be specified at various levels of abstraction. This enables researchers to focus on their field of interest, that can range from processing low-level sensor data to the integration of a complete team of robots. After nearly three years of development, MORSE is a mature tool with a large collection of components, that provides many innovative features: software-in-the-loop connectivity, multiple middleware support, configurable components, varying levels of simulation abstraction, distributed implementation for large scale multi-robot simulations and a human avatar that can interact with robots in virtual environments. This paper presents the current state of MORSE, highlighting its unique features in use cases

Induction of amyloid-β42 production by fipronil and other pyrazole insecticides

Generation of amyloid β peptides (Aβs) by proteolytic cleavage of the amyloid precursor protein (AβPP), especially increased production of Aβ42/Aβ43 over Aβ40, and their aggregation as oligomers and plaques, represent a characteristic feature of Alzheimer’s disease (AD). In familial AD (FAD), altered Aβ production originates from specific mutations of AβPP or presenilins 1/2 (PS1/PS2), the catalytic subunits of γ-secretase. In sporadic AD, the origin of altered production of Aβs remains unknown. We hypothesize that the ‘human chemical exposome’ contains products able to favor the production of Aβ42/Aβ43 over Aβ40 and shorter Aβs. To detect such products we screened a library of 3500+ compounds in a cell-based assay for enhanced Aβ42/Aβ43 production. Nine pyrazole insecticides were found to induce a β- and γ-secretase-dependent, 3-10 fold increase in the production of extracellular Aβ42 in various cell lines and neurons differentiated from induced pluripotent stem cells derived from healthy and FAD patients. Immunoprecipitation/mass spectrometry analyses showed increased production of Aβs cleaved at positions 42/43, and reduced production of peptides cleaved at positions 38 and shorter. Strongly supporting a direct effect on γ-secretase activity, pyrazoles shifted the cleavage pattern of another γ-secretase substrate, alcadeinα, and shifted the cleavage of AβPP by highly purified γ-secretase towards Aβ42/Aβ43. Focusing on fipronil, we showed that some of its metabolites, in particular the persistent fipronil sulfone, also favor the production of Aβ42/Aβ43 in both cell-based and cell-free systems. Fipronil administered orally to mice and rats is known to be metabolized rapidly, mostly to fipronil sulfone, which stably accumulates in adipose tissue and brain. In conclusion, several widely used pyrazole insecticides enhance the production of toxic, aggregation prone Aβ42/Aβ43 peptides, suggesting the possible existence of environmental “Alzheimerogens” which may contribute to the initiation and propagation of the amyloidogenic process in sporadic AD. <br/