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US 2004/0061605 A1Published application
Distributed motion prediction network
AssigneeHRL Laboratories, LLC
InventorMichael D. Howard
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AssigneeHRL Laboratories, LLC
InventorMichael D. Howard
Estimating the term…
The present invention provides a distributed motion prediction network including a plurality of nodes. The nodes in the network comprise a mechanism for detecting the presence of an object in an area around a node. A node that detects an object is termed a “detecting node.” After a node has detected an object, it communicates a signal to local nodes that are local with the detecting node to inform the local nodes of the presence of the object in the area around the detecting node. After the local nodes receive the signal, they further propagate the signal to other nodes (local to the local nodes) so that information regarding the presence of the object is progressively propagated to nodes away from the detecting node and so that as the object moves, the propagated signal is used to predict a direction of travel of the object.
A distributed motion prediction network including a plurality of nodes, the nodes in the network comprising means for:
A method for providing a distributed motion prediction network including a plurality of nodes, the nodes in the network performing steps of:
A computer program product for providing a computer program product using a plurality nodes, the computer program product comprising a computer readable medium having embedded therein, means for:
A computer program product as set forth in claim 50, further comprising means for operating the nodes according to the logical expression, {overscore (C)}·{overscore (D)}+B·{overscore (C)}+A·C·{overscore (D)}+B·C·{overscore (D)}=Y, where A represents a current action state, B represents a current sensor state, C represents the result of a determination of whether the signal is equal to a memory state, D represents a signal-memory sign bit, and Y represents a next action state.
A node for use in a distributed motion prediction network, the node comprising means for:
A node for use in a distributed motion prediction network as set forth in claim 54, wherein the node selectively performs an action based on the hop count distance from a detecting node and the direction of travel of the object.
A method for operating a node for use in a distributed motion prediction network including a plurality of nodes, the node performing steps of:
A method for operating a node for use in a distributed motion network as set forth in claim 76, further comprising a step of setting the node in a fixed position.
A computer program product for operating a node for use in a distributed motion network using a plurality nodes, the computer program product comprising a computer readable medium having embedded therein, means for:
A computer program product as set forth in claim 90, further comprising means for causing the node to selectively perform an action based on the hop count distance between the node and a detecting node and the direction of travel of the object.
A computer program product as set forth in claim 91, further comprising means for detecting, at the node, an object class for an object within the network.
A computer program product as set forth in claim 92, wherein the action performed by the node is based on the hop count distance is tailored based on the object class of an object detected.
A computer program product as set forth in claim 93, wherein the action performed by the node is selected from a group consisting of a visually detectable action, an auditory action, and a scent-based action.
A computer program product as set forth in claim 94, further comprising means for operating the node according to the logical expression, {overscore (C)}·{overscore (D)}+B·{overscore (C)}+A·C·{overscore (D)}+B·C·{overscore (D)}=Y, where A represents a current action state, B represents a current sensor state, C represents the result of a determination of whether the signal is equal to a memory state, D represents a signal-memory sign bit, and Y represents a next action state.
Dependencies are inferred from the claim text.