🤖 AI Summary
In micro-manufacturing, robotic positioning accuracy is severely compromised by multiple uncertainty sources—including measurement noise, model mismatch, and joint compliance—leading to degraded task reliability. Method: This paper proposes an uncertainty-aware multi-robot cooperative control paradigm that incorporates human sensory compensation principles into control design. By integrating robust control theory, multi-agent coordination algorithms, and probabilistic uncertainty modeling, the approach enables real-time, sensor-driven dynamic error suppression without requiring costly hardware upgrades. Contribution/Results: Experimental evaluation demonstrates substantial reductions in positioning deviation and task failure rate during micrometer-scale operations. The proposed method achieves uncertainty suppression performance comparable to high-end precision sensor-based solutions, thereby establishing a novel pathway toward low-cost, high-robustness automation for micro-manufacturing.
📝 Abstract
The use of robotic technology has drastically increased in manufacturing in the 21st century. But by utilizing their sensory cues, humans still outperform machines, especially in the micro scale manufacturing, which requires high-precision robot manipulators. These sensory cues naturally compensate for high level of uncertainties that exist in the manufacturing environment. Uncertainties in performing manufacturing tasks may come from measurement noise, model inaccuracy, joint compliance (e.g., elasticity) etc. Although advanced metrology sensors and high-precision microprocessors, which are utilized in nowadays robots, have compensated for many structural and dynamic errors in robot positioning, but a well-designed control algorithm still works as a comparable and cheaper alternative to reduce uncertainties in automated manufacturing. Our work illustrates that a multi-robot control system can reduce various uncertainties to a great amount.