C-OPT: Coverage-Aware Trajectory Optimization under Uncertainty

Bobby Davis, Ioannis Karamouzas, Stephen J. Guy

Research output: Contribution to journalArticlepeer-review

12 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

Bibliographical note

Funding Information:
This work was supported in part by the University of Minnesota's MnDRIVE Initiative on Robotics, Sensors, and Advanced Manufacturing and in part by the National Science Foundation under Grant 1544887.

Publisher Copyright:
© 2016 IEEE.


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


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