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Research

Decision Theory and Motion Planning

Motion planning and decision making are at the core of Robotics and A.I. In theory, these problems can be solved using optimal control or dynamic programming. However, computational cost for solving many real world problems is prohibitively high and is exacerbated by the “curse of dimensionality”. Randomized sampling-based methods (like RRT*) ameliorate this problem by avoiding apriori griding of the environment and incrementally building a graph online. On the other hand, deterministic search algorithms (such as A*) can be augmented with intelligent abstractions to speed up their performance, and decision theory can borrow ideas from information theory to model agents that are resource-aware. Current research in this area lies at the intersection of A.I, machine learning, optimal control and information theory.

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Following is a list of current and prior research projects at the DCSL. Click on each one to see more details.

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Current Projects

Selected Previous Projects

 

 
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