One of the main assertions of sensorimotor contingency theory is that sensory experience is not generated by activating an internal representation of the outside world through sensory signals, but corresponds to a mode of exploration and hence is an active process. Perception and sensory awareness emerge from using the structure of changes in the sensory input resulting from these exploratory actions, called sensorimotor contingencies (SMCs), for planning, reasoning, and goal achievement. Using a previously developed computational model of SMCs we show how an artificial agent can plan ahead with SMCs and use them for action guidance. Our main assumption is that SMCs are associated with a utility for the agent, and that the agent selects actions that maximize this utility. We analyze the properties of the resulting actions in a robot that is endowed with several sensory modalities and controlled by our model in a simple environment. The results demonstrate that its actions avoid aversive events, and that it can achieve a low-level form of spatial awareness that is resilient to the complete loss of a sensory modality.