  We present a perception constrained visual predictive control (PCVPC)
algorithm for quadrotors to enable aggressive flights without using any
position information. Our framework leverages nonlinear model predictive
control (NMPC) to formulate a constrained image-based visual servoing (IBVS)
problem. The quadrotor dynamics, image dynamics, actuation constraints, and
visibility constraints are taken into account to handle quadrotor maneuvers
with high agility. Two main challenges of applying IBVS to agile drones are
considered: (i) high sensitivity of depths to intense orientation changes, and
(ii) conflict between the visual servoing objective and action objective due to
the underactuated nature. To deal with the first challenge, we parameterize a
visual feature by a bearing vector and a distance, by which the depth will no
longer be involved in the image dynamics. Meanwhile, we settle the conflict
problem by compensating for the rotation in the future visual servoing cost
using the predicted orientations of the quadrotor. Our approach in simulation
shows that (i) it can work without any position information, (ii) it can
achieve a maximum referebce speed of 9 m/s in trajectory tracking without
losing the target, and (iii) it can reach a landmark, e.g., a gate in drone
racing, from varied initial configurations.
