To address the surge in charging demand driven by the rapid adoption of electric vehicles (EVs), this study proposes an integrated optimization strategy that combines photovoltaic (PV) generation, energy storage systems (ESS), vehicle-to-grid (V2G) technology, and mobile charging robots (MCRs). A mathematical model and multi-stage scheduling algorithm are developed, incorporating practical constraints such as time-of-use electricity pricing, dynamic EV arrival/departure schedules, energy demands, and charging/discharging power limits. A day-ahead planning stage first determines the baseline charging/discharging scheme. Subsequently, a rolling-horizon framework dynamically adjusts short-term decisions based on updated EV arrivals, ESS status, and real-time electricity prices, enhancing scheduling flexibility and accuracy. Numerical simulations demonstrate that integrating PV and ESS reduces peak-hour grid electricity costs. The V2G capability enables energy resale during high-price periods, while deploying MCRs improves concurrent charging capacity, reduces idle/waiting times, and increases operational revenue. Multi-scenario comparisons confirm that incorporating MCRs yields higher profits even after accounting for equipment costs. These findings provide valuable insights for planning and investing in next-generation smart charging infrastructures.