Runtime SES planning: Online motion planning in environments with stochastic dynamics and uncertainty

Abstract

Motion planning in stochastic dynamic uncertain environments is critical in several applications such as human interacting robots, autonomous vehicles and assistive robots. In order to address these complex applications, several methods have been developed. The most successful methods often predict future obstacle locations in order identify collision-free paths. Since prediction can be computationally expensive, offline computations are commonly used, and simplifications such as the inability to consider the dynamics of interacting obstacles or possible stochastic dynamics are often applied. Online methods can be preferable to simulate potential obstacle interactions, but recent methods have been restricted to Gaussian interaction processes and uncertainty. In this paper we present an online motion planning method, Runtime Stochastic Ensemble Simulation (Runtime SES) planning, an inexpensive method for predicting obstacle motion with generic stochastic dynamics while maintaining a high planning success rate despite the potential presence of obstacle position error. Runtime SES planning evaluates the likelihood of collision for any state-time coordinate around the robot by performing Monte Carlo simulations online. This prediction is used to construct a customized Rapidly Exploring Random Tree (RRT) in order to quickly identify paths that avoid obstacles while moving toward a goal. We demonstrate Runtime SES planning in problems that benefit from online predictions, environments with strongly-interacting obstacles with stochastic dynamics and positional error. Through experiments that explore the impact of various parametrizations, robot dynamics and obstacle interaction models, we show that real-time capable planning with a high success rate is achievable in several complex environments.