Balanced Collaborative Exploration via Distributed Topological Graph Voronoi Partition

TL;DR

This paper introduces a novel distributed topological graph Voronoi partition method for multi-robot exploration, improving efficiency, balance, and completeness in obstacle-rich environments through a new topological map structure and planning algorithms.

Contribution

It presents a new topological map structure and a distributed Voronoi partition algorithm that ensure balanced exploration and task allocation among robots in complex environments.

Findings

Significant improvements in exploration efficiency and completeness.

Enhanced workload balance among robots.

Theoretical guarantees for convergence and partition quality.

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.