  Agile attitude maneuvering maximizes the utility of remote sensing satellite
constellations. By taking into account a satellite's physical properties and
its actuator specifications, we may leverage the full performance potential of
the attitude control system to conduct agile remote sensing beyond conventional
slew-and-stabilize maneuvers. Employing a constellation of agile satellites,
coordinated by an autonomous and responsive scheduler, can significantly
increase overall response rate, revisit time and global coverage for the
mission. In this paper, we use recent advances in sequential convex programming
based trajectory optimization to enable rapid-target acquisition, pointing and
tracking capabilities for a scheduler-based constellation. We present two
problem formulations. The Minimum-Time Slew Optimal Control Problem determines
the minimum time, required energy, and optimal trajectory to slew between any
two orientations given nonlinear quaternion kinematics, gyrostat and actuator
dynamics, and state/input constraints. By gridding the space of 3D rotations
and efficiently solving this problem on the grid, we produce lookup tables or
parametric fits off-line that can then be used on-line by a scheduler to
compute accurate estimates of minimum-time and maneuver energy for real-time
constellation scheduling. The Minimum-Effort Multi-Target Pointing Optimal
Control Problem is used on-line by each satellite to produce continuous
attitude-state and control-input trajectories that realize a given schedule
while minimizing attitude error and control effort. The optimal trajectory may
then be achieved by a low-level tracking controller. We demonstrate our
approach with an example of a reference satellite in Sun-synchronous orbit
passing over globally-distributed, Earth-observation targets.
