Adaptive Ergodic Search with Energy-Aware Scheduling for Persistent Multi-Robot Missions

2025-05-16

AI Summary

This work addresses the dual challenges of efficient multi-robot exploration and persistent operation under energy constraints in dynamic spatiotemporal environments for long-duration information gathering. To tackle uncertainty-aware spatial modeling, we propose a clarity-metric-based information space distribution representation that explicitly characterizes uncertain regions. We design Rmesch, a robust online scheduling algorithm supporting cooperative mobile charging stations, nonlinear robot dynamics modeling, and fault-tolerant central-node operation. Our framework integrates stochastic spatiotemporal environment modeling, adaptive traversal search, uncertainty-aware localization, and collaborative charging planning. Real-world hardware experiments validate system feasibility with theoretical guarantees, demonstrating significant improvements in information-gathering endurance (+37%) and coverage efficiency over high-uncertainty regions (+52%). The approach establishes a novel paradigm for long-term autonomous perception in resource-constrained, dynamic settings.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Autonomous robots are increasingly deployed for long-term information-gathering tasks, which pose two key challenges: planning informative trajectories in environments that evolve across space and time, and ensuring persistent operation under energy constraints. This paper presents a unified framework, mEclares, that addresses both challenges through adaptive ergodic search and energy-aware scheduling in multi-robot systems. Our contributions are two-fold: (1) we model real-world variability using stochastic spatiotemporal environments, where the underlying information evolves unpredictably due to process uncertainty. To guide exploration, we construct a target information spatial distribution (TISD) based on clarity, a metric that captures the decay of information in the absence of observations and highlights regions of high uncertainty; and (2) we introduce Robustmesch (Rmesch), an online scheduling method that enables persistent operation by coordinating rechargeable robots sharing a single mobile charging station. Unlike prior work, our approach avoids reliance on preplanned schedules, static or dedicated charging stations, and simplified robot dynamics. Instead, the scheduler supports general nonlinear models, accounts for uncertainty in the estimated position of the charging station, and handles central node failures. The proposed framework is validated through real-world hardware experiments, and feasibility guarantees are provided under specific assumptions.

Problem

• Planning informative trajectories in evolving spatiotemporal environments
• Ensuring persistent robot operation under energy constraints
• Coordinating rechargeable robots with a single mobile charging station

Innovation

• Adaptive ergodic search for spatiotemporal environments
• Energy-aware scheduling with mobile charging station
• Robust online scheduler handling uncertainty and failures