Safety is a critical issue for robotic and autonomous systems that must traverse through uncertain environments. More sophisticated motion planning and control algorithms are needed as environments become increasingly dynamic and uncertain to ensure safe and effective autonomous behavior. Safely deploying robots in such dynamic environments requires a systematic accounting of various risks both within and across layers in an autonomy stack from perception to motion planning and control. Many widely used motion planning algorithms have been developed in deterministic settings. However, since motion planning algorithms must be coupled with the outputs of inherently uncertain perception systems, there is a crucial need for more tightly coupled perception and planning frameworks that explicitly incorporate perception and prediction uncertainties.
We have developed a distributionally robust incremental sampling-based motion planning framework that explicitly and coherently incorporates perception and prediction uncertainties. We utilize advanced techniques in robust and stochastic optimization to ensure that safety constraints are satisfied despite these inherent uncertainties. We have demonstrated our proposed algorithms and approaches using nonlinear vehicle models in an open urban driving simulator with static and dynamic obstacles} and show that risk bounded motion planning can be achieved effectively for nonlinear robotic systems. We are currently exploring the development of full autonomy stacks that tightly integrate layers from perception and prediction to planning and control and incorporate modern artificial intelligence and machine learning technologies.
Stochastic and robust optimization; motion planning under uncertainty; vehicle dynamics; feedback control;. artificial intelligence; machine learning.