"A motion planner for complex trajectories with cable suspended parallel robots"

Cable-suspended parallel robots (CSPRs) are a type of parallel manipulator that offer several advantages over traditional rigid-link robots, including large workspace, high payload capacity, and low inertia. However, motion planning for CSPRs can be challenging due to their complex kinematics and dynamics, particularly for complex trajectories. In this thesis, we propose a motion planner for CSPRs that can generate and execute complex trajectories with high accuracy and reliability. We first develop a kinematic and dynamic model of a CSPR, taking into account the cable tension, cable length, and robot geometry. We then formulate the motion planning problem as an optimization problem, where the objective is to minimize the cable tension and energy consumption while satisfying the desired trajectory and avoiding singularities and obstacles. We use a combination of numerical optimization techniques, such as gradient-based optimization and evolutionary algorithms, to solve the optimization problem. We also develop a real-time control algorithm that can track the planned trajectory with high accuracy and robustness. The control algorithm takes into account the cable tension, robot dynamics, and external disturbances, and uses a feedback control strategy to minimize the tracking error. We validate the motion planner and control algorithm through simulations and experiments, using a variety of complex trajectories, including spiral, circular, and linear trajectories. Our results show that the proposed motion planner can generate and execute complex trajectories with high accuracy and reliability, and can handle various types of trajectories and robot configurations. The motion planner can also optimize the cable tension and energy consumption, which is important for reducing wear and tear and increasing the lifespan of the cables. The real-time control algorithm can track the planned trajectory with high accuracy and robustness, and can handle various types of disturbances and uncertainties. Overall, this thesis provides a comprehensive solution for motion planning and control of CSPRs for complex trajectories, which can have applications in various fields, such as aerial robots, space robotics, underwater robotics, industrial automation, and human-robot interaction.