NavEX: A Multi-Agent Coverage in Non-Convex and Uneven Environments via Exemplar-Clustering

2025-04-29

AI Summary

This paper addresses the multi-agent coverage deployment problem in non-convex, terrain-varying environments, unifying two distinct objectives: fair access (minimizing average distance to agents) and hotspot prioritization (enhancing coverage in high-density regions). We propose a non-Euclidean coverage utility metric based on exemplar clustering, relaxing the conventional triangle inequality constraint. To jointly model obstacles and terrain traversability, we integrate visibility graphs with traversability-aware RRT*. Deployment optimization leverages submodular function maximization to achieve efficient near-optimal solutions. Theoretically, our approach guarantees a constant-factor approximation ratio. Extensive simulations demonstrate that the method significantly outperforms existing baselines in both deployment quality and computational efficiency under complex geometric and topographic constraints.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

This paper addresses multi-agent deployment in non-convex and uneven environments. To overcome the limitations of traditional approaches, we introduce Navigable Exemplar-Based Dispatch Coverage (NavEX), a novel dispatch coverage framework that combines exemplar-clustering with obstacle-aware and traversability-aware shortest distances, offering a deployment framework based on submodular optimization. NavEX provides a unified approach to solve two critical coverage tasks: (a) fair-access deployment, aiming to provide equitable service by minimizing agent-target distances, and (b) hotspot deployment, prioritizing high-density target regions. A key feature of NavEX is the use of exemplar-clustering for the coverage utility measure, which provides the flexibility to employ non-Euclidean distance metrics that do not necessarily conform to the triangle inequality. This allows NavEX to incorporate visibility graphs for shortest-path computation in environments with planar obstacles, and traversability-aware RRT* for complex, rugged terrains. By leveraging submodular optimization, the NavEX framework enables efficient, near-optimal solutions with provable performance guarantees for multi-agent deployment in realistic and complex settings, as demonstrated by our simulations.

Problem

• Multi-agent deployment in non-convex, uneven environments
• Fair-access and hotspot coverage tasks in complex terrains
• Obstacle-aware path planning with non-Euclidean distance metrics

Innovation

• Exemplar-clustering for non-Euclidean coverage utility
• Obstacle-aware visibility graphs for shortest-path computation
• Traversability-aware RRT* for rugged terrain navigation