Simulated Annealing for Multi-Robot Ergodic Information Acquisition Using Graph-Based Discretization

2025-09-27

AI Summary

To address uneven spatial uncertainty and unreliable initial observation noise estimates—leading to unstable sampling quality in multi-robot active information gathering—this paper proposes an adaptive sampling distribution generation method based on graph-structured environmental discretization and simulated annealing. By constructing a Boltzmann distribution and dynamically tuning its “cooling” parameter, the algorithm progressively converges from a uniform prior to an entropy-optimal distribution, thereby mitigating the impact of early-stage noise estimation bias. Integrating ergodic coverage constraints with online local sampling entropy estimation ensures balanced uncertainty reduction across regions. Simulation and TurtleBot experiments demonstrate that the method significantly outperforms uniform sampling and direct ergodic traversal in both transient and steady-state information entropy, while maintaining theoretical robustness and practical feasibility on physical robotic platforms.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Intelligent Robots: State Estimation
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Content-based information diffusion
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

One of the goals of active information acquisition using multi-robot teams is to keep the relative uncertainty in each region at the same level to maintain identical acquisition quality (e.g., consistent target detection) in all the regions. To achieve this goal, ergodic coverage can be used to assign the number of samples according to the quality of observation, i.e., sampling noise levels. However, the noise levels are unknown to the robots. Although this noise can be estimated from samples, the estimates are unreliable at first and can generate fluctuating values. The main contribution of this paper is to use simulated annealing to generate the target sampling distribution, starting from uniform and gradually shifting to an estimated optimal distribution, by varying the coldness parameter of a Boltzmann distribution with the estimated sampling entropy as energy. Simulation results show a substantial improvement of both transient and asymptotic entropy compared to both uniform and direct-ergodic searches. Finally, a demonstration is performed with a TurtleBot swarm system to validate the physical applicability of the algorithm.

Problem

• Achieving uniform uncertainty levels across regions using multi-robot teams
• Handling unreliable noise estimates in ergodic information acquisition
• Improving transient and asymptotic entropy in robot sampling strategies

Innovation

• Simulated annealing optimizes multi-robot sampling distribution
• Gradual shift from uniform to estimated optimal distribution
• Graph-based discretization enables ergodic information acquisition