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Nowadays, the potential of autonomous vehicles for order picking and material transport in vast environments with large amounts of obstacles is only exploited to a limited extent. In order to realize free, time-optimal motion of autonomous vehicles through such complex environments, this paper presents a novel motion planning approach. The approach combines a global path planner with a local trajectory generator. The global planner finds a path through the complete environment, taking only the stationary obstacles into account. The local trajectory generator computes a detailed trajectory in a local frame around the global path, accounting for both stationary and moving obstacles. This trajectory is parameterized as a spline, and is obtained by solving an optimal control problem. In order to always include the latest information about the environment, the optimal control problem is solved online with a receding horizon. The paper demonstrates the potential of the proposed method with extensive numerical simulations. In addition, it presents an experimental validation in which a KUKA youBot moves through an obstructed environment. To facilitate the numerical and experimental validation of the presented method, it is embodied in a user-friendly open-source software toolbox.

Manufacturing of workpieces with CNC machines requires computing machine tool trajectories that fast and accurately track the desired workpiece contour. This paper presents a novel B-spline trajectory generation method for machine tools. The method solves an optimal control problem to minimize the motion time of the tool, while taking into account the velocity, acceleration and jerk limits of the tool axes. Furthermore, it directly includes the allowed workpiece tolerance, by constraining the trajectory to lie inside a tube around the nominal geometry contour. This allows exploring the trade-off between accuracy and productivity, while computing near-optimal trajectories. The presented method creates fluent connections between segments that build up the contour by simultaneously optimizing trajectories for multiple segments. On the other hand, limiting the amount of simultaneously optimized segments and using an efficient problem formulation keeps the computation time acceptable. The trajectory generation method is validated in simulation by comparison with industrial benchmarks, showing a reduction in machining time of more than 10%. The comparison to a state-of-the-art corner smoothing approach shows that the presented method obtains similar or slightly faster trajectories, at a computation time that is up to 45 times lower. In addition, the method is validated experimentally on a 3-axis micro-milling machine. To easily generate trajectories for different workpieces and machines, the method is included in a user-friendly open-source software toolbox.