Design, testing and validation of model predictive control for an unmanned ground vehicle

The rapid increase in designing, manufacturing, and using autonomous robots has attracted numerous researchers and industries in recent decades. The logical motivation behind this interest is the wide range of applications. For instance, perimeter surveillance, search and rescue missions, agriculture, and construction. In this thesis, motion planning and control based on model predictive control (MPC) for unmanned ground vehicles (UGVs) is tackled. In addition, different variants of MPC are designed, analysed, and implemented for such non-holonomic systems. It is imperative to focus on the ability of MPC to handle constraints as one of the motivations. Furthermore, the proliferation of computer processing enables these systems to work in a real-time scenario. The controller's responsibility is to guarantee an accurate trajectory tracking process to deal with other specifications usually not considered or solved by the planner. However, the separation between planner and controller is not necessarily defined uniquely, even though it can be a hybrid process, as seen in part of this thesis. Firstly, a robust MPC is designed and implemented for a small-scale autonomous bulldozer in the presence of uncertainties, which uses an optimal control action and a feed-forward controller to suppress these uncertainties. More precisely, a linearised variant of MPC is deployed to solve the trajectory tracking problem of the vehicle. Afterwards, a nonlinear MPC is designed and implemented to solve the path-following problem of the UGV for masonry in a construction context, where longitudinal velocity and yaw rate are employed as control inputs to the platform. For both the control techniques, several experiments are performed to validate the robustness and accuracy of the proposed scheme. Those experiments are performed under realistic localisation accuracy, provided by a typical localiser. Most conspicuously, a novel proximal planning and control strategy is implemented in the presence of skid-slip and dynamic and static collision avoidance for the posture control and tracking control problems. The ability to operate in moving objects is critical for UGVs to function well. The approach offers specific planning capabilities, able to deal at high frequency with context characteristics, which the higher-level planner may not well solve. Those context characteristics are related to dynamic objects and other terrain details detected by the platform's onboard perception capabilities. In the control context, proximal and interior-point optimisation methods are used for MPC. Relevant attention is given to the processing time required by the MPC process to obtain the control actions at each actual control time. This concern is due to the need to optimise each control action, which must be calculated and applied in real-time. Because the length of a prediction horizon is critical in practical applications, it is worth looking into in further detail. In another study, the accuracies of robust and nonlinear model predictive controllers are compared. Finally, a hybrid controller is proposed and implemented. This approach exploits the availability of a simplified cost-to-go function (which is provided by a higher-level planner); thus, the hybrid approach fuses, in real-time, the nominal CTG function (nominal terrain map) with the rest of the critical constraints, which the planner usually ignores. The conducted research fills necessary gaps in the application areas of MPC and UGVs. Both theoretical and practical contributions have been made in this thesis. Moreover, extensive simulations and experiments are performed to test and verify the working of MPC with a reasonable processing capability of the onboard process

Motion Planning

Motion planning is a fundamental function in robotics and numerous intelligent machines. The global concept of planning involves multiple capabilities, such as path generation, dynamic planning, optimization, tracking, and control. This book has organized different planning topics into three general perspectives that are classified by the type of robotic applications. The chapters are a selection of recent developments in a) planning and tracking methods for unmanned aerial vehicles, b) heuristically based methods for navigation planning and routes optimization, and c) control techniques developed for path planning of autonomous wheeled platforms

Parametrized maneuvers for autonomous vehicles

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2004.Includes bibliographical references (p. 197-209).This thesis presents a method for creating continuously parametrized maneuver classes for autonomous vehicles. These classes provide useful tools for motion planners, bundling sets of related vehicle motions based on a low-dimensional parameter vector that describes the fundamental high-level variations within the trajectory set. The method follows from a relaxation of nonlinear parametric programming necessary conditions that discards the objective function, leaving a simple coordinatized feasible space including all dynamically admissible vehicle motions. A trajectory interpolation algorithm uses projection and integration methods to create the classes, starting from arbitrary user-provided maneuver examples, including those obtained from standard nonlinear optimization or motion capture of human-piloted vehicle flights. The interpolation process, which can be employed for real-time trajectory generation, efficiently creates entire maneuver sets satisfying nonlinear equations of motion and nonlinear state and control constraints without resorting to iterative optimization. Experimental application to a three degree-of-freedom rotorcraft testbed and the design of a stable feedforward control framework demonstrates the essential features of the method on actual hardware. Integration of the trajectory classes into an existing hybrid system motion planning framework illustrates the use of parametrized maneuvers for solving vehicle guidance problems. The earlier relaxation of strict optimality conditions makes possible the imposition of affine state transformation constraints, allowing maneuver sets to fit easily into a mixed integer-linear programming path planner.(cont.) The combined scheme generalizes previous planning techniques based on fixed, invariant representations of vehicle equilibrium states and maneuver elements. The method therefore increases the richness of available guidance solutions while maintaining problem tractability associated with hierarchical system models. Application of the framework to one and two-dimensional path planning examples demonstrates its usefulness in practical autonomous vehicle guidance scenarios.by Christopher Walden Dever.Ph.D