マルコフ的探索ダイナミクス下でのマルチロボット特徴分布の確率的合意形成
Probabilistic Consensus on Feature Distribution for Multi-robot Systems with Markovian Exploration Dynamics
複数ロボットがランダムウォークで環境を探索し、近隣と通信しながら特徴分布を分散融合で推定、真の分布への収束を証明した研究。
著者: Aniket Shirsat, Shatadal Mishra, Wenlong Zhang, Spring Berman
分類: cs.RO, cs.MA, cs.SY, eess.SY
原文アブストラクト
In this paper, 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.
