Wireless rechargeable sensor networks have become a hot research issue as it can overcome the limited energy bottleneck of wireless sensor networks owing to the recent breakthrough of wireless power transfer technology. Though network lifetime is prolonged and sensor nodes can sustain immortally, the issue of network robustness is overlooked, yielding most theoretical work unsuitable for practical applications when confronting with unpredictable packet loss. In this paper, we address the network robustness issue by maximizing the charging utility in a risk-averse view. First, we build a risk-averse model based on the concept of CVaR (Conditional Value at Risk), which trades-off charging utility and risk aversion for quantifying robustness. Then, we propose a spatial discretization scheme to construct a charging route for mobile charger, which can reduce computational overhead. Afterwards, a path optimization scheme is designed to further improve the charging utility. We convert the original problem into the submodular function maximization problem and propose a method with a performance guarantee while maximizing the system robustness. Finally, testbed experiments and simulations are conducted, and the results demonstrate that our schemes outperform comparison algorithms by at least 22.4% in effective energy in the presence of risks to guarantee system robustness.