Multiagent Task Allocation for Dynamic Intelligent Space: Auction and Preemption With Ontology Knowledge Graph

Abstract

This paper introduces a pioneering dynamic system optimisation for multiagent (DySOMA) framework, revolutionising task scheduling in dynamic intelligent spaces with an emphasis on multirobot systems. The core of DySOMA is an advanced auction-based algorithm coupled with a novel task preemption ranking mechanism, seamlessly integrated with an ontology knowledge graph that dynamically updates. This integration not only enhances the efficiency of task allocation among robots but also significantly improves the adaptability of the system to environmental changes. Compared to other advanced algorithms, the DySOMA algorithm shows significant performance improvements, with its RLB 26.8% higher than that of the best-performing Consensus-Based Parallel Auction and Execution (CBPAE) algorithm at 10 robots and 29.7% higher at 20 robots, demonstrating its superior capability in balancing task loads and optimising task completion times in larger, more complex environments. DySOMA sets a new benchmark for intelligent robot task scheduling, promising significant advancements in the autonomy and flexibility of robotic systems in complex evolving environments.