Balanced Collaborative Exploration via Distributed Topological Graph Voronoi Partition

2025-10-28

AI Summary

This paper addresses the problem of autonomous online collaborative exploration by multi-robot systems in cluttered, non-convex environments with complex obstacles. To this end, we propose a distributed topological graph-based Voronoi partitioning method. Our key contributions are threefold: (1) an incremental topological map that simultaneously ensures spatial connectivity and global coverage representation; (2) a distributed weighted topological graph Voronoi algorithm, theoretically guaranteed to achieve consensus convergence and fair partitioning, while supporting online replanning and global guidance; and (3) an integrated framework combining receding-horizon task reallocation with local trajectory smoothing for safe and efficient collaborative exploration. Extensive experiments demonstrate that our approach significantly outperforms state-of-the-art methods in exploration efficiency, coverage completeness, and inter-robot load balancing.

Technology Category

• Intelligent Robots: Multi-Robot Systems
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• 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
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

This work addresses the collaborative multi-robot autonomous online exploration problem, particularly focusing on distributed exploration planning for dynamically balanced exploration area partition and task allocation among a team of mobile robots operating in obstacle-dense non-convex environments. We present a novel topological map structure that simultaneously characterizes both spatial connectivity and global exploration completeness of the environment. The topological map is updated incrementally to utilize known spatial information for updating reachable spaces, while exploration targets are planned in a receding horizon fashion under global coverage guidance. A distributed weighted topological graph Voronoi algorithm is introduced implementing balanced graph space partitions of the fused topological maps. Theoretical guarantees are provided for distributed consensus convergence and equitable graph space partitions with constant bounds. A local planner optimizes the visitation sequence of exploration targets within the balanced partitioned graph space to minimize travel distance, while generating safe, smooth, and dynamically feasible motion trajectories. Comprehensive benchmarking against state-of-the-art methods demonstrates significant improvements in exploration efficiency, completeness, and workload balance across the robot team.

Problem

• Distributed multi-robot exploration planning in obstacle-dense non-convex environments
• Balanced exploration area partition and task allocation among mobile robots
• Optimizing visitation sequences to minimize travel distance with safe trajectories

Innovation

• Distributed topological graph Voronoi partitioning for balanced exploration
• Incremental topological map updating with global coverage guidance
• Local planner optimizing target sequence for minimal travel distance