Monday, April 7, 2014

2014-03-17: Autonomy Incubator Seminar Series, Dr. Jon How

Dr. Jonathan P. "Jon" How, Professor of Aeronautics and Astronautics at the Massachusetts Institute of Technology (MIT) was the March speaker in our monthly seminar series on autonomy. He presented recent algorithms and results for the safe, real-time motion planning under uncertainty of single and multi-agent systems in dynamic environments. In order to safely navigate in a dynamic environment, an autonomous system must be able to overcome uncertainty in both its own motion and the motion of other agents. Robust planning can be achieved by embedding probabilistic uncertainty models into a chance-constrained RRT* planner (CC-RRT*). This algorithm leverages the sampling-based nature of RRTs to generate probabilistically feasible solutions, with guarantees on maximum risk of constraint violation, in real-time. CC-RRT* has been demonstrated to efficiently produce risk-aware trajectories for a variety of complex aerospace-related motion planning problems, including applications for urban driving and parafoil terminal guidance.

Reliable robust planning also depends on the availability of probabilistic models that accurately represent the uncertainty in the environment and its evolution. This talk will thus present recent results for efficiently learning these models for dynamic environments. We utilize Bayesian nonparametric models that uniquely provide the flexibility to learn model size and parameters, which are often very difficult to determine a priori. For example, Gaussian processes (GPs) are used to represent the trajectory velocity fields of the obstacles (static & dynamic) in the environment, a Dirichlet process GP mixture (DP-GP) is used to learn the number of motion models and their velocity fields, and the dependent Dirichlet process GP mixture (DDP-GP) is also used to capture the same quantities and their temporal evolution. These techniques have been used to learn models of motion/intent behaviors of other drivers and pedestrians to improve the performance of an autonomous car.         

Here are his charts:

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