Analysis of Robot Wi-Fi Communication Control Stability Based on Markov Model

As the demand for intelligent robots in complex environments grows, the dynamic stability assessment of Wi-Fi communication has become a key issue in ensuring reliable control. To address the shortcomings of traditional methods in dynamic adaptability and long-term risk assessment, a Multi-Metric Markov State Prediction Model (MMSPM) is proposed. This model combines signal strength (Receive Signal Strength Indicator, RSSI) and communication delay to construct a joint state space, and employs a Markov chain to model state transition patterns, while incorporating steady-state distributions to quantify communication disruption risks. Experimental results show that the MMSPM achieves a five-step prediction accuracy of 93.5% with a 200ms time interval, which represents improvements of 30% and 15.1% over the static threshold method (63.5%) and the LSTM model (78.4%), respectively, while maintaining a prediction accuracy of 89.7% in multi-interference scenarios.