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An Agent-Based Intentional Multi-Robot Task Allocation Framework

When multiple robots are supposed to operate together, coordination and communication issues arise. "Which robot should execute which task?" is the key question of Multi-Robot Task Allocation (MRTA) problem. Among several autonomy level approaches being centralized or distributed, hybrid approaches have become popular at reaching optimal task allocation combining advantages of both centralized and distributed approaches. Hybrid approaches like auction-based or market-based are also named as intentional according to their cooperation model. Intentional type allocation model differs from swarm-type basically for number of robots employed. This study presents development of an intentional MRTA model based on multiagent concepts using Java Agent Development Environment (JADE). A simple model is built and proposed on prior approaches in market-based task allocation in order to improve optimality. Experiments are performed using selected cases of MRTA literature and results prove that proposed model in this study produces optimal solution at each case.