Chance-constrained motion planning using modeled distance-to-collision functions

Proc. IEEE International Conference on Automation Science and Engineering (CASE), 2021

Jacob J Johnson, Michael C Yip

Publisher Link: http://ieeexplore.ieee.org/abstract/document/9551655/

Abstract: This paper introduces Chance Constrained Gaussian Process-Motion Planning (CCGP-MP), a motion planning algorithm for robotic systems under motion and state estimate uncertainties. The paper’s key idea is to capture the variations in the distance-to-collision measurements caused by the uncertainty in state estimation techniques using a Gaussian Process (GP) model. We formulate the planning problem as a chance constraint problem and propose a deterministic constraint that uses the modeled distance function to verify the chance-constraints. We apply Simplicial Homology Global Optimization (SHGO) approach to find the global minimum of the deterministic constraint function along the trajectory and use the minimum value to verify the chance-constraints. Under this formulation, we can show that the optimization function is smooth under certain conditions and that SHGO converges to the global minimum …

Johnson et al. (2021) Chance-constrained motion planning using modeled distance-to-collision functions, Proc. IEEE International Conference on Automation Science and Engineering (CASE), pp. 1582-1589.