The present disclosure relates to the intelligent distribution of data for robotic, autonomous, and similar systems. To reduce the impact of multi-agent coordination on networked systems embodiments are disclosed that include the use of action-based constraints which yield constrained-action POMDP (CA-POMDP) models, and probabilistic constraint satisfaction for the resulting infinite-horizon finite state controllers. To enable constraint analysis over an infinite horizon, an unconstrained policy is first represented as a finite state controller (FSC). A combination of a Markov chain Monte Carlo (MCMC) routine and a discrete optimization routine can be performed on the finite state controller to improve probabilistic constraint satisfaction of the finite state controller, while minimizing impact to a value function.

A control system includes a clustered architecture having a master controller, a central control section including one or more first remote controllers under direct control of the master controller, and a distributed control section including a cluster controller controlled by the master controller. The cluster controller controls the activities of one or more second remote controllers. Each of the first and second remote controllers are utilized to drive one or more axes.

The present disclosure relates to the intelligent distribution of data for robotic, autonomous, and similar systems. To reduce the impact of multi-agent coordination on networked systems embodiments are disclosed that include the use of action-based constraints which yield constrained-action POMDP (CA-POMDP) models, and probabilistic constraint satisfaction for the resulting infinite-horizon finite state controllers. To enable constraint analysis over an infinite horizon, an unconstrained policy is first represented as a finite state controller (FSC). A combination of a Markov chain Monte Carlo (MCMC) routine and a discrete optimization routine can be performed on the finite state controller to improve probabilistic constraint satisfaction of the finite state controller, while minimizing impact to a value function.

A control system includes a clustered architecture having a master controller, a central control section including one or more first remote controllers under direct control of the master controller, and a distributed control section including a cluster controller controlled by the master controller. The cluster controller controls the activities of one or more second remote controllers. Each of the first and second remote controllers are utilized to drive one or more axes.