Archive for the ‘Research Projects’ Category

Planning and Execution Monitoring

Posted on August 8th, 2011
Planning and Execution Monitoring

We propose the use of causality-based formal representation and automated reasoning methods from artificial intelligence to endow multiple teams of robots in a factory, with high-level cognitive capabilities, such as optimal planning and diagnostic reasoning. We present a framework that features bilateral interaction between task and motion planning, and embeds geometric reasoning in causal reasoning. We embed this planning framework inside an execution and monitoring framework and show its applicability

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Ontological Reasoning for Rehabilitation Robotics

Posted on June 19th, 2011
Ontological Reasoning for Rehabilitation Robotics

Physical rehabilitation therapy is indispensable for treating neurological disabilities. Using robotic devices to assist repetitive and labor intensive rehabilitation exercises help decrease physical burden of the therapists and application related costs. As the number of rehabilitation robots increase, the information about them also increases, but most of the time in unstructured forms (e.g., as text in publications). This makes it harder to access the requested knowledge and thus reason about it. To facilitate access to requested knowledge about rehabilitation robots, we have designed and developed the first formal rehabilitation robotics ontology, called RehabRobo-Onto, in OWL, collaborating with experts in robotics and in physical medicine.

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Cloud Robotics

Posted on May 29th, 2011
Cloud Robotics

In this project, we investigate the possibility of robots offloading computation-intensive tasks and downloading new skills instantly, Matrix-style, making use of cloud-computing.

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Multi-Agent Path Planning

Posted on May 29th, 2011
Multi-Agent Path Planning

Many tasks, such as computer games, street sweeping, mail delivery, and robotic surveillance and patrol, vehicle routing, environmental monitoring, require multiple agents to visit points in an environment to accomplish a goal, ensuring that they do not collide with static obstacles or other moving agents.

We study such problems in a general framework using high-level representation formalism and efficient solvers of the declarative programming paradigm Answer Set Programming.

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