Fast trajectory planning frameworks for robotic systems

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.