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Automated Planning addresses the problem of finding a sequence of actions, a plan, transforming the environment from its initial state to some goal state. In real-world environments, exogenous events might occur and might modify the environment without agent's consent. Besides disrupting agent's plan, events might hinder agent's pursuit towards its goals and even cause damage (e.g. destroying the robot). In this paper, we leverage the notion of Safe States in dynamic environments under presence of non-deterministic exogenous events that might eventually cause dead-ends (e.g. “damage” the agent) if the agent is not careful while executing its plan. We introduce a technique for generating plans that constrains the number of consecutive “unsafe” actions in a plan and a technique for generating “robust” plans that effectively evade event effects. Combination of both approaches plans and executes robust plans between safe states. We empirically show that such an approach effectively navigates the agent towards its goals in spite of presence of dead-ends.