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We present a perception constrained visual predictive control (PCVPC)algorithm for quadrotors to enable aggressive flights without using anyposition information. Our framework leverages nonlinear model predictivecontrol (NMPC) to formulate a constrained image-based visual servoing (IBVS)problem. The quadrotor dynamics, image dynamics, actuation constraints, andvisibility constraints are taken into account to handle quadrotor maneuverswith high agility. Two main challenges of applying IBVS to agile drones areconsidered: (i) high sensitivity of depths to intense orientation changes, and(ii) conflict between the visual servoing objective and action objective due tothe underactuated nature. To deal with the first challenge, we parameterize avisual feature by a bearing vector and a distance, by which the depth will nolonger be involved in the image dynamics. Meanwhile, we settle the conflictproblem by compensating for the rotation in the future visual servoing costusing the predicted orientations of the quadrotor. Our approach in simulationshows that (i) it can work without any position information, (ii) it canachieve a maximum referebce speed of 9 m/s in trajectory tracking withoutlosing the target, and (iii) it can reach a landmark, e.g., a gate in droneracing, from varied initial configurations.

PCVPC: Perception Constrained Visual Predictive Control For Agile Quadrotors