Receding Horizon Model-Predictive Control for Mobile Robot Navigation of Intricate Paths

As mobile robots venture into more complex environments, more arbitrary feasible state-space trajectories and paths are required to move safely and efficiently. The capacity to effectively navigate such paths in the face of disturbances and changes in mobility can mean the difference between mission failure and success. This paper describes a technique for model predictive control of a mobile robot that utilizes the structure of a regional motion plan to effectively search the local continuum for an improved solution. The contribution, the receding horizon model-predictive control algorithm, specifically addresses the problem of path following and obstacle avoidance through cusps, turn-in-place, and multi-point turn maneuvers in environments where terrain shape and vehicle mobility effects are non-negligible. The technique is formulated as an optimal controller that utilizes a model-predictive trajectory generator to determine parameterized control inputs that navigate general mobile robots safely through the environment. Experimental results are presented for a a six-wheeled skid-steered field robot in natural terrain.