Probabilistic Consensus on Feature Distribution for Multi-Robot Systems With Markovian Exploration Dynamics

Aniket Shirsat, Shatadal Mishra, Wenlong Zhang, Spring Berman

Research output: Contribution to journalArticlepeer-review


In this letter, we present a consensus-based decentralized multi-robot approach to reconstruct a discrete distribution of features, modeled as an occupancy grid map, that represent information contained in a bounded planar 2D environment, such as visual cues used for navigation or semantic labels associated with object detection. The robots explore the environment according to a random walk modeled by a discrete-time discrete-state (DTDS) Markov chain and estimate the feature distribution from their own measurements and the estimates communicated by neighboring robots, using a distributed Chernoff fusion protocol. We prove that under this decentralized fusion protocol, each robot's feature distribution converges to the ground truth distribution in an almost sure sense. We verify this result in numerical simulations that show that the Hellinger distance between the estimated and ground truth feature distributions converges to zero over time for each robot. We also validate our strategy through Software-In-The-Loop (SITL) simulations of quadrotors that search a bounded square grid for a set of visual features distributed on a discretized circle.

Original languageEnglish (US)
Pages (from-to)6407-6414
Number of pages8
JournalIEEE Robotics and Automation Letters
Issue number3
StatePublished - Jul 1 2022


  • mapping
  • Multi-robot systems
  • probability and statistical methods

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Biomedical Engineering
  • Human-Computer Interaction
  • Mechanical Engineering
  • Computer Vision and Pattern Recognition
  • Computer Science Applications
  • Control and Optimization
  • Artificial Intelligence


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