KU Leuven

Optimization of Mechatronic Systems H04U1a

Authors: Elias Rached,Mohamad Sayegh

Based on the research paper “Model Predictive Control for Aerial Collision Avoidance in dynamic environments”

Goal is to safely navigate in a workspace populated by static and moving obstacles

• Implement point-to-point Model Predictive Control

• Obstacle Avoidance in static and dynamic environment (Moving obstacles)

• Finding the best formulation of the problem ( MPC and Obstacles )

• Exploring time optimality (comparison several methods / parameters)

• How to escape Local minima

• Using Rockit: Rockit (Rapid Optimal Control kit) is a software framework to quickly prototype optimal control problems (aka dynamic optimization) that may arise in engineering: iterative learning (ILC), model predictive control (NMPC), motion planning. Link: https://github.com/meco-group/rockit

• Solver used: IPOPT (Interior Point OPTimizer)

• Control input limits: −𝟏 ≤𝑢 ≤𝟏

• OCP horizon length: N = 10 - 20 control intervals

• Sampling time: dt = 0.05 - 0.1s

• Discretization method: multiple shooting

• Numerical integration method: Runge-Kutta 4th order

• Using warmstarting

Methods of escaping the local minimum are proposed:

• Obstacle merging: when obstacles are close enough they are merged into one obstacle

• A* trajectory guiding: when confirmed to be stuck in a local minimum, an A* trajectory is found, and intermediate points are extracted to guide the drone away

• Potential Field method could help escape certain local minima (if close to obstacles)

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