  *The following abbreviates the abstract. Please refer to the thesis for the
full abstract.*
  After a disaster, locating and extracting victims quickly is critical because
mortality rises rapidly after the first two days. To assist search and rescue
teams and improve response times, teams of camera-equipped aerial robots can
engage in tasks such as mapping buildings and locating victims.
  These sensing tasks encapsulate difficult (NP-Hard) problems. One way to
simplify planning for these tasks is to focus on maximizing sensing performance
over a short time horizon. Specifically, consider the problem of how to select
motions for a team of robots to maximize a notion of sensing quality (the
sensing objective) over the near future, say by maximizing the amount of
unknown space in a map that robots will observe over the next several seconds.
By repeating this process regularly, the team can react quickly to new
observations as they work to complete the sensing task. In technical terms,
this planning and control process forms an example of receding-horizon control.
Fortunately, common sensing objectives benefit from well-known monotonicity
properties (e.g. submodularity), and greedy algorithms can exploit these
monotonicity properties to solve the receding-horizon optimization problems
that we study near-optimally.
  However, greedy algorithms typically force robots to make decisions
sequentially so that planning time grows with the number of robots. Further,
recent works that investigate sequential greedy planning, have demonstrated
that reducing the number of sequential steps while retaining suboptimality
guarantees can be hard or impossible.
  We demonstrate that halting growth in planning time is sometimes possible. To
do so, we introduce novel greedy algorithms involving fixed numbers of
sequential steps.
