The field of time-domain astronomy has experienced unprecedented growth due to the increasing deployment of robotic telescopes capable of autonomous, round-the-clock sky monitoring. These instruments have revolutionized the detection and characterization of transient phenomena such as supernovae, gamma-ray bursts, variable stars, and gravitational wave counterparts. This paper explores the transformative role of robotic telescopes such as ZTF, ATLAS, and LCOGT in enabling rapid-response observations and building large time-series datasets. We review the design principles and scheduling algorithms behind robotic observatories and assess their scientific contributions across different wavelength regimes. Particular attention is given to the synergy between robotic systems and machine learning pipelines that enable real-time classification of transient events. We also discuss challenges such as data deluge, follow-up prioritization, and observational biases, as well as future directions in global telescope networks.
Vwavware et al. (Sun,) studied this question.