Distributed Multi-Robot Active-Sensing of a Diffusive Source
Francesca Pagano, Nicola De Carli, Esteban Restrepo, Antonio Marino, Paolo Robuffo Giordano
AI summary
Problem
Estimating the parameters of a continuously releasing diffusive source remains challenging for mobile robot swarms due to unbounded field gradients, lack of informative initial measurements, and the need for safe, decentralized coordination.
Approach
The method combines an Extended Information Consensus Filter with a decentralized gradient-based motion controller that minimizes a Gramian information metric, blended with an exploratory strategy and enforced via Control Barrier Functions in a quadratic program.
Key results
- Novel distributed active-sensing framework leveraging A-optimality Gramian metric and mixed exploration strategy
- Statistical simulations demonstrate faster estimation convergence and more efficient trajectories compared to three baselines, particularly with few robots
- Quadcopter experiments validate online, decentralized, collision-free, and informative motion generation in physical settings
Why it matters
Provides a practical, scalable solution for real-time environmental monitoring and hazard detection using decentralized robot swarms without relying on centralized control or instantaneous release assumptions.
Abstract
This paper considers the problem of coordinating a group of mobile robots for distributedly estimating the param- eters of a diffusion model that generates a time-varying spatial field. We assume that each robot can measure the local concen- tration of a substance continuously released in the environment and base the proposed distributed estimation strategy on an Extended Information Consensus Filter (E-ICF) with a forgetting factor. We then develop a decentralized online motion strategy aimed at minimizing a Gramian-based information metric that improves the E-ICF convergence. Additional constraints, among which collision avoidance, are integrated as Control Barrier Functions (CBFs) in a Quadratic Program (QP). Finally, we present statistical comparisons against three baselines which show the improved performance of the proposed method in a range of simulated scenarios, and we also report the results of experiments carried out with quadcopters to demonstrate the actual implementability of the approach and its effectiveness in generating online, collision-free, and informative motions.