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.
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