C-OPT: Coverage-Aware Trajectory Optimization under Uncertainty

Bobby Davis, Ioannis Karamouzas, Stephen J. Guy

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


We introduce a new problem of continuous, coverage-aware trajectory optimization under localization and sensing uncertainty. In this problem, the goal is to plan a path from a start state to a goal state that maximizes the coverage of a user-specified region while minimizing the control costs of the robot and the probability of collision with the environment. We present a principled method for quantifying the coverage sensing uncertainty of the robot. We use this sensing uncertainty along with the uncertainty in robot localization to develop C-OPT, a coverage-optimization algorithm which optimizes trajectories over belief-space to find locally optimal coverage paths. We highlight the applicability of our approach in multiple simulated scenarios inspired by surveillance, UAV crop analysis, and search-and-rescue tasks. We also present a case study on a physical, differential-drive robot. We also provide quantitative and qualitative analysis of the paths generated by our approach.

Original languageEnglish (US)
Article number7407311
Pages (from-to)1020-1027
Number of pages8
JournalIEEE Robotics and Automation Letters
Issue number2
StatePublished - Jul 2016


  • Collision Avoidance
  • Motion and Path Planning
  • Reactive and Sensor-Based Planning

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