Sensor Planning for Large Numbers of Robots
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*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.
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