Research projects using RTI DDS

The Evolvable Assembly systems project will adopt the methods of context-awareness, multi-agent swarm intelligence and self-adaptation. Context-awareness will provide each individual element with the understating of the surrounding environment; multi-agent swarm intelligence will support system self-organisation based on a community of autonomous and cooperative entities; and self-adaptation will allow the development of a goal driven collective behaviour leading to purposeful system evolution. The proposed programme is a radical departure from the current philosophy of reconfigurable manufacturing - it will create a framework for autonomous context-aware and adaptable assembly and manufacturing systems that can co-evolve with products, processes and business and social environment. This transformational approach presents theoretical, technical and social challenges that demand new fundamental multidisciplinary research.

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The Cloud Manufacturing research project investigates how digital technologies can enable 'on demand' manufacturing. The research adopts the methods of cloud computing and crowdsourcing, hence bringing new models for open innovation within the manufacturing space to enable new organisations to arise without the need for a large capital investment. This approach proposes a radical departure from the current philosophy of manufacturing ICT and we are exploring the benefits of using Connext DDS as part of a multi-layered architecture where manufacturing entities participate and contribute with information, and support different services to the manufacturing life cycle, supply chain, consumers and users of the products.

Cloud Manufacturing: a proof of concept of Manufacturing-as-a-Service. G. Terrazas, D. Sanderson, E. Kelly, S. Ratchev. In 3rd EPSRC Manufacturing the Future Conference, 2014

 

CRIAQ AVIO-509 project is a research project on the design of modular avionics architectures and integrated commonly called IMA (Integrated and Modular Avionics). The main purpose of this research project aims to explore the design methodologies IMA systems and evaluate the impact of architectural decisions. A platform for experimentation will be developed to enable prototyping IMA systems. An application of synthetic vision increased (ESVS) will be implemented on this demonstrator IMA. CMC Electronics Inc. and CAE companies. are partners in this project and the École Polytechnique de Montreal. The project has a duration of three years (2011-2014).

Within this project, we are analysing the benefits of using Connext DDS for communications between different nodes of an avionic system.

Author:

Building large and complex distributed systems required in Defence pose challenges in ensuring that the functional system specifications of processing and network performance are achieved while at the same time the non-functional properties of space, weight and power are optimised. Current research into performance measurement and prediction is achieved through modelling the system behaviour and interactions and observing the execution on several alternative deployment environments. We use RTI DDS as the middleware extensively for data mining and logging of system executions. Observation of the execution is enhanced by the RTI DDS built-in management topic for publications and the dynamic data features available. Data Distribution Service (DDS) middleware enables a distributed system to be modelled and executed on a wide range of hardware such as IBM blade servers through to low cost single board computers such as the Raspberry Pi. Such a broad range of deployment environments is essential to our current research into software architectures, model-driven engineering and distributed systems design.

Further information about this research project is available from:
http://blogs.adelaide.edu.au/dig/

The University of Adelaide

The main objective of PLANET project is the design, development and validation of an integrated platform to enable the deployment, operation and maintenance of large-scale/complex systems of heterogeneous networked Cooperating Objects, including Wireless Sensor and Actuator Networks and mobile objects. The platform supports optimal and adaptive deployment and operation by means of mobile cooperating objects, i.e. vehicles, networked with static nodes. The platform is validated in two complementary scenarios: the monitoring of the Doñana Biological Reserve with very high ecological value and very sensitive to the impact of pollution, and the highly automated airfield scenario in which security plays an important role and where wireless communication and cooperative techniques pose significant challenges.

PLANET addresses the design methodology and development of the platform as well as the algorithms required to support the deployment and maintenance of heterogeneous systems with mobile and static nodes. 

We use RTI DDS as the middleware for the communication between heterogeneous Cooperating Objects, such as UAVs, UGVs and ground sensor networks. Upon RTI DDS we build a set of PLANET services to ease the deployment, operation and maintenance of heterogeneous networked Cooperating Objects. 

For more information of the Project PLANET, you are welcome to visit the website: http://www.planet-ict.eu/

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The Intelligent Robotics Group (IRG) explores extreme environments, remote locations, and uncharted worlds. We conduct applied research in computer vision, geospatial data systems, human-robot interaction, planetary mapping and robot software. (IRG brochure)

 

IRG operates the "K10" series of planetary rovers (slideshowfact sheet). We conduct robotic field tests each year in planetary analog sites, such as Black Point Lava Flow and Haughton Crater.

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Organization:

The Robot Application Programming Interface Delegate (RAPID) is a set of software data structures and routines that simplify the process of communicating between multiple diverse robots and their command and control systems. RAPID is not intended to be an all-encompassing API for robot communication, but rather it’s a compatibility layer that permits tools and robotic assets to exchange data and information and allows operators to communicate with heterogeneous robots in a uniform way. RAPID is a compatibility layer that delegates information between robots that speak different languages.

The RAPID specification includes definitions and APIs for messages and services that support supervisory telerobotics operations over near-Earth time delay. RAPID is not a middleware specification, although safety and time-delay capabilities do imply requirements on implementing middleware systems. As currently implemented, the RAPID system can be considered a software reference implementation for remote operations.

For more information, contact David.S.Mittman@jpl.nasa.gov.

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The Machine Intelligence Lab (MIL) at the University of Tennessee focuses on theory and practice of novel machine learning technologies that are applicable to a broad range of domains.

We use RTI DDS in our reserach of biologically-inspired deep machine learning architectures that are combined with reinforcement learning algorithms to yield high-performance decision making systems.

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We use RTI DDS to develop applications in supervisory control as well as teleoperated and autonomous robot control. Our group receives funding from various sources including TEKES (The Finnish Funding Agency for Technology and Innovation, the main public funding organization for research, development and innovation in Finland), as well as the Academy of Finland, and the European Fusion Development Agreement (EFDA).

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We use RTI DDS in a number of projects that build upon our core rover navigation software, RASM. The acronym stands for "Reliable Autonomous Surface Mobility". A site will be available in the coming months.

The research addresses navigation and autonomy for mobile robots with emphasis on scientific rovers operating in planetary terrain. The research is typified by the Life in the Atacama project ( http://www.frc.ri.cmu.edu/atacama ). Other projects can be found here: http://www.frc.ri.cmu.edu

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