Decentralized Heterogeneous Multi-Robot Collaborative Exploration for Indoor and Outdoor 3D Environments

2026-04-26

AI Summary

This work addresses the challenges of low exploration efficiency and high communication overhead in heterogeneous multi-robot collaborative exploration within complex indoor–outdoor 3D environments. The authors propose a fully decentralized coordination framework that integrates terrain and observation data to construct a lightweight supervoxel-based perceptual map. Task viewpoints are clustered, and the exploration problem is formulated as a Heterogeneous Multi-Depot Multiple Traveling Salesman Problem (HMDMTSP) with constraints reflecting robots’ diverse capabilities. An enhanced genetic algorithm computes optimal exploration paths, complemented by a conflict-resolution mechanism to ensure motion coordination. Experimental results demonstrate that the proposed approach significantly improves exploration efficiency, reduces communication costs, and effectively aligns individual robot capabilities with task requirements.

Technology Category

• Intelligent Robots: Multi-Robot Systems
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Multiagent Systems: Coordination and Collaboration

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Heterogeneous multi-robot systems feature significant adaptability for complex environments. However, effective collaboration that fully exploits the robots' potential remains a core challenge. This paper proposes a decentralized collaborative framework for heterogeneous multi-robot systems to autonomously explore indoor and outdoor 3D environments. First, a basic perception map that integrates terrain and observation metrics is designed. Improved supervoxel segmentation is developed to simplify the map structure and form a high-level representation that supports lightweight communication. Second, the traversal and observation capabilities of heterogeneous robots are modeled to evaluate the requirements of task views derived from incomplete supervoxels. These task views are grouped by requirements and clustered to streamline assignment. Subsequently, the view-cluster assignment is formulated as a heterogeneous multi-depot multi-traveling salesman problem (HMDMTSP) that incorporates constraints between view-cluster requirements and robot capabilities. An improved genetic algorithm is developed to efficiently solve this problem while ensuring global consistency. Based on the assignments, redundant views within clusters are eliminated to refine exploration routes. Finally, conflicts between robots' motion paths are resolved. Simulations and field experiments in cluttered indoor and outdoor environments demonstrate that our approach effectively coordinates exploration tasks among heterogeneous robots, achieving superior exploration efficiency and communication savings compared to state-of-the-art approaches.

Problem

• heterogeneous multi-robot
• collaborative exploration
• 3D environments
• decentralized coordination
• autonomous exploration

Innovation

• heterogeneous multi-robot
• decentralized collaboration
• 3D exploration
• supervoxel segmentation
• HMDMTSP