Spline-Based Motion Planning for Autonomous Mechatronic Systems

In modern industry there is an ever lasting quest to obtain a higher productivity at lower costs. Introducing autonomous motion systems provides a huge potential to make progress in this quest, since they allow drastically improving the efficiency of various industrial processes and tasks, including harvesting fields with tractors, order picking in eCommerce warehouses and container transport through ports. In addition, automation may also be beneficial in our everyday life, for example in applications such as robotic surgery, personal assistance robots or parcel delivery by drones. When studying the case of autonomous systems moving around in industrial environments, it becomes clear that the currently existing set-ups are conservative and require strongly conditioned environments, limiting their capabilities. As a consequence, there is a growing interest in more flexible techniques, allowing free motion of autonomous systems through unconditioned environments. This thesis proposes a novel spline-based motion planning approach to compute trajectories that steer holonomic and nonholonomic systems trough uncertain and dynamic environments while taking into account the kinematic vehicle limits and avoiding collision with all obstacles. In order to deal with uncertainties and changes in the environment, the motion planning problem is solved online, during the vehicle motion. By exploiting spline properties, a small-scale optimization problem is obtained, allowing a low solving time and therefore a swift reaction to changes. In addition, these spline properties allow obtaining guaranteed constraint satisfaction at all time instants. In vast environments containing a multitude of obstacles, solving the complete motion planning problem at once is very complicated. In addition, obstacles moving near the goal position influence the complete computed trajectories, while due to uncertainties it is not useful to already take these obstacles into account at the start of the system's motion. Therefore, in the case of a vast environment, a scheduler combines the presented spline-based method with a global path planner in order to split the complete motion planning problem over a series of subproblems that are easier to solve. The resulting approach outperforms most existing ones since it allows substantial deviation from the global path and fluently deals with uncertainties and changes in the environment. When considering the case of motion planning for CNC machines, trajectories must be computed that move a machine tool as fast as possible along the contour of the desired workpiece. For this case, a similar approach can be applied as in a vast environment: instead of solving the complete problem at once, only the trajectories for a specified number of segments, building up the contour, are computed simultaneously. The problem formulation includes the kinematic limits of the tool, together with the process limits and the given tolerance. This allows computing feasible trajectories that exploit the tolerance to cut corners, reducing the machining time. In addition, to avoid decoupling between subsequent sets of segments and further reduce the required machining time, the problem is solved with a moving horizon. The potential of the designed approach is demonstrated with extensive numerical simulations. In addition, experiments are performed on respectively the KUKA youBot and a 3-axis micro-milling machine. OMG-tools, a user-friendly open-source motion planning toolbox, is extended to include all methods presented in this thesis. The toolbox makes it easy to set-up, solve and simulate problems by using the proposed spline-based motion planning approach.

Jaar van publicatie:2018

Toegankelijkheid:Open