Classical power line inspection and maintenance are dangerous, costly and time consuming. Drones could mitigate the risk for humans and minimize the cost for the direct benefit of the power line infrastructure.
Coupling perception and path planning with control has become increasingly popular in aerial vehicles. This project will continue to investigate vision-based navigation tightly couple approaches of perception and control to satisfy the drone dynamics and compute feasible trajectories with respect to the input saturation. This involves to further development the research on perception aware Model Predictive Control (MPC) for quadrotors, solving the challenging aspects of a power line inspection scenario. A perception aware MPC approach would ideally improve the aerial vehicle's behavior during an approaching maneuver.
Classical power line inspection and maintenance are dangerous, costly and time consuming. Drones could mitigate the risk for humans and minimize the cost for the direct benefit of the power line infrastructure.
Coupling perception and path planning with control has become increasingly popular in aerial vehicles. This project will continue to investigate vision-based navigation tightly couple approaches of perception and control to satisfy the drone dynamics and compute feasible trajectories with respect to the input saturation. This involves to further development the research on perception aware Model Predictive Control (MPC) for quadrotors, solving the challenging aspects of a power line inspection scenario. A perception aware MPC approach would ideally improve the aerial vehicle's behavior during an approaching maneuver.
Pursue research on a unified control and planning approach that integrates the action and perception objectives. We look for students with strong computer vision background and hands-on control experience with MPC. This work involves a final demonstration to accomplish field testing results in challenging conditions (e.g.: HDR, high speed).
Pursue research on a unified control and planning approach that integrates the action and perception objectives. We look for students with strong computer vision background and hands-on control experience with MPC. This work involves a final demonstration to accomplish field testing results in challenging conditions (e.g.: HDR, high speed).
Javier Hidalgo-CarriĆ³ (jhidalgocarrio@ifi.uzh.ch)/Giovanni Cioffi (cioffi@ifi.uzh.ch)
Javier Hidalgo-CarriĆ³ (jhidalgocarrio@ifi.uzh.ch)/Giovanni Cioffi (cioffi@ifi.uzh.ch)