GRAPE: Geometric Risk-Aware Pursuit-Evasion

  title = {{GRAPE}: {Geometric} {Risk}-{Aware} {Pursuit}-{Evasion}},
  volume = {121},
  issn = {09218890},
  shorttitle = {{GRAPE}},
  url = {},
  abstract = {We present a method for a collaborative team of pursuing robots to contain and capture a single evading robot. We address the practical case in which the pursuers do not know the exact location of the evader but rather must localize the evader with noisy on-board sensors. Under our policy, the pursuers move to maximally reduce the area of space reachable by the evader despite the uncertainty in the evader’s position estimate. Our pursuit policy is distributed in the sense that each pursuer only needs to broadcast its position and estimate to its closest neighbors. The policy guarantees that the evader’s reachable area is non-increasing between measurement updates regardless of the evader’s policy. Furthermore, we show in simulations and hardware that the pursuers capture the evader in spite of the position uncertainty provided that the pursuer’s measurement noise decreases with the distance to the evader.},
  language = {en},
  urldate = {2020-09-15},
  journal = {Robotics and Autonomous Systems},
  author = {Shah, Kunal and Schwager, Mac},
  month = nov,
  year = {2019},
  keywords = {pursuit\_evasion},
  pages = {103246},
  month_numeric = {11}