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Co-design of dynamic real-time scheduling and cooperative control for human-agent collaboration systems based on mutual trust

Mutual trust is a key factor in human-human collaboration. Inspired by this social interaction, we analyze human-agent mutual trust in the collaboration of one human and (semi)autonomous multi-agent systems. In the thesis, we derive time-series human-agent mutual trust models based on results from human factors engineering. To avoid both overtrust and under-trust, we set up dynamic timing models for the multi-agent scheduling problem and develop necessary and sufficient conditions to test the schedulability of the human multi-agent collaborative task. Furthermore, we extend the collaboration between one human and multiple agents into the collaboration between multi-human network and swarm-based agents network. To measure the collaboration between these two networks, we propose a novel measurement, called fitness. By fitness, we can simplify multi-human and swarms collaboration into one-human and swarms collaboration. Cooperative control is incorporated into the swarm systems to enable several large-scale agent teams to simultaneously reach navigational goals and avoid collisions. Our simulation results show that the proposed algorithm can be applied to humanagent collaboration systems and guarantee effective real-time scheduling of collaboration systems while ensuring a proper level of human-agent mutual trust.