Mechanical and Civil Engineering Seminar
Towards Learning Robotic Dynamics: Application to Multirotor Takeoff and Landing
Ph.D. Thesis Defense
Abstract: Multirotors have become widely spread but their usage is still limited. Ensuring safety during take-off and landing is still an open problem. Towards this goal this thesis proposes two different solutions to address this problem. Both approaches complement each other and they are tested on hardware.
The first approach is to design a vehicle that is stable during take-off, despite hardware failures or unsteady take-off platforms. A solution is to use a ballistic launch to impose a deterministic path, preventing collisions with its environment. Following this approach led to the development of several SQUID (Streamlined Quick Unfolding Investigation Drone) vehicles. The main challenges are the ballistic initial flight, large accelerations during launch, and limited volume. A first prototype was developed, able to transition mid-flight from stable ballistic flight to a fully controllable multirotor. The system has been fabricated and field tested from a moving vehicle up to 50mph to successfully demonstrate the feasibility of the concept and experimentally validate the design's aerodynamic stability and deployment reliability. A second prototype expanded the capabilities incorporating fully-autonomous vision-based navigation, while keeping the ballistic passive stability and stable transition abilities. The new design includes a more reliable plate-based structure and more effective folding fins.
The second approach focuses on designing controllers that are safe regardless of the platform. For that purpose, a Model Predictive Control (MPC) is used to ensure state and input constraints. Given the highly non-linear dynamics platforms, and fast dynamics that require a quick controller evaluation, the work in this thesis is built using Koopman Operator theory, which allows tools from linear analysis to be applied to systems with inherently non-linear dynamics. One of the main contributions is a novel method to find Koopman Eigenfunctions directly from data. Another key contribution is an episodic approach to model non-linear actuation dynamics. The proposed method is first tested on simulation and it outperforms comparable approaches. The method is also demonstrated on-board a multirotor for a fast landing application, where the nonlinear ground effect is learned and used to improve landing speed and quality. An additional extension considers model uncertainty in the MPC architecture, where an Ensemble Kalman Sampler is used to learn the uncertainty distribution.
Please virtually attend this thesis defense:
Zoom Link: https://caltech.zoom.us/j/84329334205