This paper develops and applies a variant of the Min-Conflict algorithm to the problem of sensor allocation with incomplete information for mobile robots. A categorization of the types of contention over sensing resources is provided, as well as a taxonomy of available information for the sensor scheduling task. The Min-Conflict with Happiness (MCH) heuristic algorithm, which performs sensor scheduling for situations in which no information is known about future assignments, is then described. The primary contribution of this modification to Min-Conflict is that it permits the optimization of sensor certainty over the set of all active behaviors, thereby producing the best sensing state for the robot at any given time. Data are taken from simulation experiments and runs from a pair of Nomad200 robots using the SFX hybrid deliberative/reactive architecture. Results from these experiments demonstrate that MCH is able to satisfy more sensor assignments (up to 142%) and maintain a higher overall utility of sensing than greedy or random assignments (a 7-24% increase), even in the presence of sensor failures. In addition, MCH supports behavioral sensor fusion allocations. The practical advantages of MCH include fast, dynamic repair of broken schedules allowing it to be used on computationally constrained systems, compatibility with the dominant hybrid robot architectural style, and least-disturbance of prior assignments minimizing interruptions to reactive behaviors.