Motion Planning for Mobile Robots and Manipulators

The focus of our motion planning research is to develop algorithms that will enable both mobile robots and manipulators to operate intelligently in extreme environments or for extreme tasks. For mobile robots such as autonomous ground vehicles (AGVs), autonomous air vehicles (AAVs), or autonomous underwater vehicles (AUVs) an example of an extreme environment is a cluttered environment. This problem has been addressed using both reactive and deliberative planning algorithms. For AGVs extreme environments also include difficult terrains such as sand, ice and mud, and highly undulating terrains. For manipulators an extreme task is lifting objects that are so heavy that they cannot be lifted quasi-statically. A unifying feature of each of these latter problems is that they benefit from using a dynamic model in the planning process. Hence, a major focus of this research is the development and refinement of Sampling Based Model Predictive Control (SBMPC), a novel nonlinear MPC (NMPC) approach that enables motion planning with dynamic models as well as the solution of more traditional MPC problems.