Robot motion planning in dynamic, cluttered, and uncertain environments: the Partially Closed-Loop Receding Horizon Control approach

Abstract

This thesis is concerned with robot motion planning in dynamic, cluttered, and uncertain environments. Successful and efficient robot operation in such environments requires reasoning about the future system evolution and the uncertainty associated with obstacles and moving agents in the environment. Current motion planning strategies ignore future information and are limited by the resulting growth of uncertainty as the system is evolved. This thesis presents an approach that accounts for future information gathering (and the quality of that information) in the planning process. The Partially Closed-Loop Receding Horizon Control approach, introduced in this thesis, is based on Dynamic Programming with imperfect state information. Probabilistic collision constraints, due to the need for obstacle avoidance between the robot and obstacles with uncertain locations and geometries, are developed and imposed. By accounting for the anticipated future information, the uncertainty associated with the system evolution is managed, allowing for greater numbers of moving agents and more complex agent behaviors to be handled. Simulation results demonstrate the benefit of the proposed approach over existing approaches in static and dynamic environments. Complex agent behaviors, including multimodal and interactive agent-robot models, are considered.