Grouping Auction-Consensus Algorithm for Decentralized Task Allocation in Multi-Robot Systems

2026-08-16

AI Summary

This study addresses the misalignment between individual bidding and global optimality in decentralized multi-robot task allocation by proposing a Grouped Auction Consensus Algorithm. The method reconstructs task-level bids into group-level structured actions via spatial clustering and employs a two-stage negotiation architecture compatible with partial acquisition mechanisms to effectively bridge individual-team objective gaps. Across 4,000 test scenarios, the algorithm achieves a median optimality rate of 97%, significantly outperforming CBBA’s 81–84% while demonstrating faster convergence and superior scalability. Validated against mixed-integer linear programming benchmarks as near-globally optimal, this approach establishes a novel paradigm for efficient coordination in large-scale robotic swarms.

Abstract

Decentralized multi-robot task allocation (MRTA) is essential for scalable and resilient autonomous systems. The Consensus-Based Bundle Algorithm (CBBA) is a widely adopted decentralized baseline. However, its individual task-level bidding is poorly aligned with the min-sum objective of minimizing total team travel distance, leading to suboptimal allocations in spatially distributed environments. This paper introduces the Grouping Auction-Consensus Algorithm (GACA). This decentralized MRTA framework adopts the two-phase auction-consensus architecture of CBBA while fundamentally redesigning its bidding mechanism to reason over groups of spatially proximate tasks. A nearest-neighbor preprocessing step partitions tasks into spatially coherent groups before allocation. Agents then iteratively propose structured group-level actions: claiming unassigned groups, acquiring partial groups, or contesting groups held by other agents. Competing actions are resolved through a consensus phase. Operating in the MT-SR-IA problem class, GACA is evaluated against CBBA using a Mixed-Integer Linear Program as the ground-truth optimality reference. Across four swarm sizes and 4,000 test worlds, GACA achieves a median percent optimality of approximately 97% compared to 81--84% for CBBA, while converging in equal or fewer iterations. A scalability evaluation over 3,280 additional problem instances spanning swarm sizes of 5 to 20 agents and task counts of 10 to 50 confirms that these gains generalize robustly across a wide range of problem configurations.

Problem

• Decentralized Multi-Robot Task Allocation
• Consensus-Based Bundle Algorithm
• Min-Sum Objective
• Suboptimal Allocation

Innovation

• Grouping Auction-Consensus Algorithm
• Decentralized Task Allocation
• Spatially Coherent Grouping
• Group-level Bidding
• CBBA Redesign