There is an increasing interest in automated mobile equipment in the construction, agriculture and mining industries to improve productivity, efficiency and operator safety. In general, these machines belong to a class of mobile vehicles with a tool for manipulating its environment to accomplish a repetitive task. Forces and motions are inherently coupled between the tool (e.g. bucket or blade) and the means of vehicle propulsion (e.g. wheels or tracks). Furthermore, they are often operated within uncertain and unstructured environments. A particularly challenging case involves the use of a bulldozer for the removal of excavated material. Modeling and control of mobile robots that interact forcibly with their environment, such as robotic excavation machinery, is a challenging problem that has not been adequately addressed in prior research. This thesis investigates the low-level modeling and control of a 3-DOF robotic bulldozing operation.

Motivated by a bulldozing process in an underground mining application, a theoretical nonlinear hybrid dynamic model was developed. The model includes discrete operation modes contained within a hybrid dynamic model framework. The dynamics of the individual modes are represented by a set of linear and nonlinear differential equations. An instrumented scaled-down bulldozer and environment were developed to emulate the full scale operation. Model parameter estimation and validation was completed using experimental data from this system. The model was refined based on a global sensitivity analysis. The refined model was found to be suitable for simulation and design of robotic bulldozing control laws.

Optimal blade position control laws were designed based on the hybrid dynamic model to maximize the predicted material removal rate of the bulldozing process. A stability analysis of the underlying deterministic closed-loop process dynamics was performed using Lyapunov’s second method. Monte Carlo simulation was used for further performance and stability analysis of the closed-loop process dynamics including stochastic state disturbances and input constraints. Results of the Monte Carlo simulation were also used for tuning the blade position control laws. Experiments were conducted with the scaled-down robotic bulldozing system. The control laws were implemented with various tuning values. As a comparison, a rule-based blade control algorithm was also designed and implemented. The experimental results with the optimal control laws demonstrated a 33% increase in the average material removal rate compared to the rule-based controller.