Stochastic Optimization for Autonomous Vehicles with Limited Control Authority

Abstract

In this work, we present a Stochastic Gradient Ascent (SGA) algorithm for multi-vehicle information gathering that accounts for limitations on a vehicle's control authority caused by external forces. By representing vehicle paths using a novel action space representation, rather than a state space representation, we remove the need to perform feasibility calculations on the vehicle's path. Our algorithm uses a stochastic optimization scheme by sampling perturbed action sequences around the current best known sequence to estimate the gradient of a state space information function with respect to the action sequence. Additionally, we use sequential greedy allocation to plan for multiple vehicles. Results are shown using a Navy Coastal Ocean Model (NCOM) for the Gulf of Mexico (GoM). SGA shows improvement in the amount of information gained over a greedy baseline. Additionally, we compare to Monte Carlo Tree Search (MCTS) Method, which is able to gather competitive amounts of information but is more computationally intensive than our approach.