A distributed paradigm is proposed for behavior-based control of a homogeneous collection of autonomous mobile robots in the lifting and lowering processes of payload transportation. Unlike previous applications of behavior-based control to payload transportation, we examine control of a payload in a vertical plane. Others before have examined moving payloads on a horizontal surface through pushing actions; we demonstrate an ability to both raise and lower a pallet, despite the fact that no robots have a rigid grasp of the pallet. This control paradigm uses parallel behavior pathways within the individual robot and minimal emergent specialization between robots to control both pallet translation and rotation, while maintaining a strong tolerance to environmental uncertainties and changes. We stress simple, feasible methodologies over complex, optimal methodologies, although we show that with some global self-organization of the collective, the feasible solutions approach and become optimal solutions. These mobile robots demonstrate an ability to function in unforeseen environments and with inaccurate sensor data. They also demonstrate an ability to learn their place, or role, within the collective. The robots must learn their relative roles because they possess no predetermined knowledge about pallet mass, pallet inertia, collective size, or their positions relative to the pallet's center of gravity. All of this is achieved using memoryless, behavior-based control algorithms with minimal inter-agent communication. / Master of Science