International audienceA large number of robotic software have been developed but cannot or can hardly interoperate with each other because of their dependencies on specific hardware or software platform is hard-wired into the code. Consequently, robotic software is hard and expensive to develop because there is little opportunity of reuse and because low-level details must be taken into account in early phases. Moreover, robotic experts can hardly develop their application without programming knowledge or the help of programming experts and robotic software is difficult to adapt to hardware or target-platform changes. In this paper we report on the development of RobotML, a Robotic Modeling Language that eases the design of robotic applications, their simulation and their deployment to multiple target execution platforms
MORSE 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.
To cite this version:Patrick Taillandier, Serge Stinckwich. Using the PROMETHEE multi-criteria decision making method to define new exploration strategies for rescue robots. IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), 2011, Kyoto, Japan. pp. 321 -326, 2011321 -326, , <10.1109321 -326, /SSRR.2011 Using the PROMETHEE Abstract -The exploration of an unknown environment by a robot system (an individual robot or a team of robots) is a well-studied problem in robotics. This problem has many applications and, among them, the post-disaster search of victims in an urban space. Most of proposed exploration algorithms are based on the use of specific criteria to define the quality of the possible movements. In this paper, we propose an exploration approach based on the combination of several criteria thanks to the PROMETHEE II multi-criteria decision making method. The PROMETHEE II method allows one to establish a complete ranking between possible movements based on outranking relations. Experimental results show that this approach can be used to effectively combine different criteria and outperforms several classic exploration strategies.
Background
Mathematical and computational models are widely used to study the transmission, pathogenicity, and propagation of infectious diseases. Unfortunately, complex mathematical models are difficult to define, reuse and reproduce because they are composed of several concerns that are intertwined. The problem is even worse for computational models because the epidemiological concerns are also intertwined with low-level implementation details that are not easily accessible to non-computing scientists. Our goal is to make compartmental epidemiological models easier to define, reuse and reproduce by facilitating implementation of different simulation approaches with only very little programming knowledge.
Results
We achieve our goal through the definition of a domain-specific language (DSL), Kendrick, that relies on a very general mathematical definition of epidemiological concerns as stochastic automata that are combined using tensor-algebra operators. A very large class of epidemiological concerns, including multi-species, spatial concerns, control policies, sex or age structures, are supported and can be defined independently of each other and combined into models to be simulated by different methods. Implementing models does not require sophisticated programming skills any more. The various concerns involved within a model can be changed independently of the others as well as reused within other models. They are not plagued by low-level implementation details.
Conclusions
Kendrick
is one of the few DSLs for epidemiological modelling that does not burden its users with implementation details or required sophisticated programming skills. It is also currently the only language for epidemiology modelling that supports modularity through clear separation of concerns hence fostering reproducibility and reuse of models and simulations. Future work includes extending Kendrick to support non-compartmental models and improving its interoperability with existing complementary tools.
Electronic supplementary material
The online version of this article (10.1186/s12859-019-2843-0) contains supplementary material, which is available to authorized users.
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