Advancement: Scalable optimal control approaches to Dubins vehicle navigation problems under uncertainty

Speaker Name: 
Alexey Munishkin
Speaker Title: 
PhD Student (Advisor: Dejan Milutinovic)
Speaker Organization: 
Computer Engineering
Start Time: 
Friday, March 8, 2019 - 10:00am
End Time: 
Friday, March 8, 2019 - 12:00pm
Location: 
Engineering 2, Room 280
Organizer: 
Dejan Milutinovic

Abstract:  The environment around an autonomously navigated vehicle can have an unpredictable number of other vehicles and stationary or moving obstacles that may or may not have harmful intentions. The safe navigation of the autonomous vehicle in the presence of other vehicles and obstacles can be formulated as a stochastic optimal control problem. While in theory one can write down the corresponding Hamilton-Jacobi-Bellman (HJB) equation for any state space control problem, practically solving the equation is computationally infeasible when the state space is large. Moreover, once it is accounted for a time varying number of obstacles and other vehicles, and the associated time varying dimension of the state space, it is clear that new approaches to the design of vehicle navigation have to be considered. This work addresses the problem of autonomous navigation by a scalable, safe navigation algorithm approach. It is based on stochastic optimal control solutions for vehicle-to-vehicle, vehicle-to-obstacle and vehicle-to-goal problems that are integrated into scalable, safe navigation strategies to time efficiently navigate the vehicle or a team of vehicles towards their goal. The work is based on the Dubins nonholonomic vehicle model and is illustrated by multiple scenarios in simulations and with real robots.