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Implementation of a Model Predictive Control Algorithm with Extension to Obstacle Avoidance
Model Predictive Control allows to execute fast and agile maneuvers with quadrotors in a robust and safe way. It is extremely versatile and covers many control scenarios and goals, and could even integrate planning like obstacle avoidance. This project aims at implementing MPC for quadrotors.
Keywords: MAV quadrotor non linear model predictive control optimal optimization LQR
To control a quadrotor in very fast, dynamic and agile maneuvers, it is important to have reliable controllers running at high speed onboard the quadrotor, which can also take into account nonlinearities and predict behaviour over a short time horizon. Model Predictive Control (MPC) algorithms cover this need by (re-)generating trajectories of a short future time horizon that are optimal to some cost with respect to some reference. They offer a very powerful and versatile way to control quadrotors even in complex environments and fast maneuvers. This project therefore aims at an implementation of such an MPC controller for trajectory tracking and navigation based on our existing state-dependent LQR controllers or with a new approach. This will be tested on a new, small state-of-the-art quadrotor platform and optionally extended with obstacle avoidance, for example based on a stereo camera.
To control a quadrotor in very fast, dynamic and agile maneuvers, it is important to have reliable controllers running at high speed onboard the quadrotor, which can also take into account nonlinearities and predict behaviour over a short time horizon. Model Predictive Control (MPC) algorithms cover this need by (re-)generating trajectories of a short future time horizon that are optimal to some cost with respect to some reference. They offer a very powerful and versatile way to control quadrotors even in complex environments and fast maneuvers. This project therefore aims at an implementation of such an MPC controller for trajectory tracking and navigation based on our existing state-dependent LQR controllers or with a new approach. This will be tested on a new, small state-of-the-art quadrotor platform and optionally extended with obstacle avoidance, for example based on a stereo camera.
- Implement and test (simulation and real) an iterative LQR algorithm.
- Make an efficient and fast implementation on our new small quadrotor.
- Optional: Extend the approach to obstacle avoidance.
- Implement and test (simulation and real) an iterative LQR algorithm. - Make an efficient and fast implementation on our new small quadrotor. - Optional: Extend the approach to obstacle avoidance.