Abstract
As a sequel to our recent work [1], in which a control framework was developed for large-scale joint swarms of unmanned ground (UGV) and aerial (UAV) vehicles, the present paper proposes cognitive and meta-cognitive supervisor models for this kind of distributed robotic system. The cognitive supervisor model is a formalization of the recently Nobel-awarded research in brain science on mammalian and human path integration and navigation, performed by the hippocampus. This is formalized here as an adaptive Hamiltonian path integral, and efficiently simulated for implementation on robotic vehicles as a pair of coupled nonlinear Schrödinger equations. The meta-cognitive supervisor model is a modal logic of actions and plans that hinges on a weak causality relation that specifies when atoms may change their values without specifying that they must change. This relatively simple logic is decidable yet sufficiently expressive to support the level of inference needed in our application. The atoms and action primitives of the logic framework also provide a straight-forward way of connecting the meta-cognitive supervisor with the cognitive supervisor, with other modules, and to the meta-cognitive supervisors of other robotic platforms in the swarm.
Highlights
We have proposed in [1] a rigorous model for prediction and control of a large-scale joint swarm of unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs), performing an autonomous land-air operation
As a sequel to our recent work [1], in which a control framework was developed for large-scale joint swarms of unmanned ground (UGV) and aerial (UAV) vehicles, the present paper proposes cognitive and meta-cognitive supervisor models for this kind of distributed robotic system
We have presented sophisticated cognitive and meta-cognitive supervisor models for joint swarms of robotic aerial and ground vehicles
Summary
We have proposed in [1] a rigorous model for prediction and control of a large-scale joint swarm of unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs), performing an autonomous land-air operation. According to Edvard Moser, “All network models for grid cells involve continuous attractors ...”—which is similar to our attractor Hamiltonian dynamics of UGVs and UAVs, given by Equations (1)-(2) . As inspired by this discovery in brain science, the present paper proposes a novel probabilistic spatio-temporal model for mammalian path integration and navigation, formulated as an adaptive Hamiltonian path integral. The purpose of the cognitive supervisor is to provide the 2D and 3D inputs to ( ) the recurrent neural nets (1)-(2), or 2D attractors qA2D , pA2D for the ( ) UGV-swarm and 3D attractors q3AD , p3AD for the UAV-swarm (see, e.g. [5])
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