The deployment of robot swarms is influenced by real-world factors, such as motor issues, sensor failure, and wind disturbances. These factors cause the appearance of faulty robots. In a decentralized swarm, sharing incorrect information from faulty robots will lead to undesired swarm behaviors, such as swarm disconnection and incorrect heading directions. We envision a system where a human operator is exerting supervisory control over a remote swarm by indicating changes in trust to the swarm via a "trust signal". By correcting faulty behaviors, trust between the human and the swarm is maintained to facilitate human-swarm cooperation. In this research, a trust-aware behavior reflection method — Trust-R — is designed based on a weighted mean subsequence reduced algorithm (WMSR). By using Trust-R, detected faulty behaviors are automatically corrected by the swarm in a decentralized fashion by referring to the motion status of their trusted neighbors and isolating failed robots from the others. Based on real-world scenarios, three types of robot faults — degraded performance caused by motor wear, abnormal motion caused by system uncertainty, and motion deviation caused by an external disturbance such as wind — were simulated to test the effectiveness of Trust-R. Results show that Trust-R is effective in correcting swarm behaviors for swarm self-healing.
Katia Sycara (Advisor)
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