The thesis aims to develop a fast trajectory planning framework for repetitive motion planning
tasks of robotic systems. The focus is on scenarios where a 3D gantry crane is used to move
goods or materials from a specific starting point to a target point in a static environment
with known obstacles. The framework should be able to plan a time-optimal trajectory in real
time considering both obstacles and dynamic constraints on state variables and control inputs.
Current state-of-the-art trajectory planning approaches require long computation times to
calculate the entire trajectory from scratch even in scenarios where the starting and target
states are only slightly changed. This limitation results in a waste of computational resources
and makes it impossible to handle moving targets. Therefore the lack of a viable approach in
the existing literature motivates this work. The thesis seeks to answer the question of whether
a trajectory planning framework can be developed that can generate collision-free trajectories
in real-time while accounting for system constraints and a dynamically moving target. By
addressing this question the thesis aims to contribute to the advancement of robotic systems
for moving goods in factories warehouses and ports.
