Distributed motion prediction network

• US 20040061605A1
• Filed: 02/15/2003
• Published: 04/01/2004
• Est. Priority Date: 02/15/2002
• Status: Active Grant

• Current Assignee: HRL Laboratories LLC (The Boeing Co.)
• Original Assignee: HRL Laboratories LLC (The Boeing Co.)
• Inventors: Howard, Michael D.
• Granted Patent: US 7,319,383 B2
• Time in Patent Office: Days
• US Class Current: 340/539.11
• CPC Class Codes: G01S 17/00 Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
• CPC Class Codes: H04W 48/08 Access restriction or access information delivery, e.g. discovery data delivery signalling during connection H04W76/00
• CPC Class Codes: H04W 8/005 Discovery of network devices, e.g. terminals
• CPC Class Codes: H04W 88/04 adapted for relaying to or from another terminal or user

Abstract

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.

Claims

1. A distributed motion prediction network including a plurality of nodes, the nodes in the network comprising means for:

detecting the presence of an object in an area around a node, with a node that detects an object termed a detecting node;

communicating a signal from a detecting node 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; and

propagating the signal from the local nodes to other nodes that are local to the local nodes such 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.

2. A distributed motion prediction network as set forth in claim 1, wherein the signal includes a hop count that is incremented by each node as it is propagated between successive nodes away from the detecting node, the hop count being indicative of a hop count distance from the detecting node and a current node.

3. A distributed motion prediction network as set forth in claim 2, wherein as the object moves along the direction of travel, it is detected by different detecting nodes along the direction of travel, and where signals originating from different detecting nodes are compared at each current node to determine whether the current node is in front of or behind travel of the object.

4. A distributed motion prediction network as set forth in claim 3, wherein the nodes selectively perform an action based on the hop count distance between a current node and a detecting node and the direction of travel of the object.

5. A distributed motion prediction network as set forth in claim 4, wherein the nodes further comprise a means for detecting an object class for an object within the network.

6. A distributed motion prediction network as set forth in claim 5, wherein the action performed by the nodes based on the hop count distance is tailored based on the object class of an object detected.

7. A distributed motion prediction network as set forth in claim 6, wherein the action performed by a node is selected from a group consisting of a visually detectable action, an auditory action, and a scent-based action.

8. A distributed motion prediction network as set forth in claim 7, wherein at least a portion of the nodes are in a fixed position.

9. A distributed motion prediction network as set forth in claim 8, wherein at least a portion of the nodes are hard-wired together.

10. A distributed motion prediction network as set forth in claim 9, wherein the nodes operate 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.

11. A distributed motion prediction network as set forth in claim 1, wherein as the object moves along the direction of travel, it is detected by different detecting nodes along the direction of travel, and where signals originating from different detecting nodes are compared at each current node to determine whether the current node is in front of or behind travel of the object.

12. A distributed motion prediction network as set forth in claim 1, wherein the nodes selectively perform an action based on the hop count distance between a current node and a detecting node and the direction of travel of the object.

13. A distributed motion prediction network as set forth in claim 1, wherein the nodes further comprise a means for detecting an object class for an object within the network.

14. A distributed motion prediction network as set forth in claim 13, wherein the action performed by the nodes based on the hop count distance is tailored based on the object class of an object detected.

15. A distributed motion prediction network as set forth in claim 14, wherein the action performed by a node is selected from a group consisting of a visually detectable action, an auditory action, and a scent-based action.

16. A distributed motion prediction network as set forth in claim 1, wherein at least a portion of the nodes are in a fixed position.

17. A distributed motion prediction network as set forth in claim 1, wherein at least a portion of the nodes are hard-wired together.

18. A distributed motion prediction network as set forth in claim 1, wherein the nodes operate according to the logical expression, {overscore (C)}·

{overscore (D)}+B·

{overscore (C)}+A·

C·

{overscore (D)}+B·

{overscore (D)}=Y, where A represents a current actionstate, 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.

19. A method for providing a distributed motion prediction network including a plurality of nodes, the nodes in the network performing steps of:

detecting the presence of an object in an area around a node, with a node that detects an object termed a detecting node;

communicating a signal from a detecting node 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; and

propagating the signal from the local nodes to other nodes that are local to the local nodes such 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.

20. A method for providing a distributed motion network as set forth in claim 19, wherein the signal includes a hop count that is incremented by each node during the propagating step as the signal is propagated between successive nodes away from the detecting node, the hop count being indicative of a hop count distance from the detecting node and a current node.

21. A method for providing a distributed motion network as set forth in claim 20, wherein as the object moves along the direction of travel, it is detected by different detecting nodes along the direction of travel, and where signals originating from different detecting nodes are compared at each current node in a comparing step to determine whether the current node is in front of or behind travel of the object.

22. A method for providing a distributed motion network as set forth in claim 21, wherein the nodes selectively perform a step of executing an action based on the hop count distance between a current node and a detecting node and the direction of travel of the object.

23. A method for providing a distributed motion network as set forth in claim 22, wherein the nodes further perform a step of detecting an object class for an object within the network.

24. A method for providing a distributed motion network as set forth in claim 23, wherein the action executed by the nodes based on the hop count distance is tailored based on the object class of an object detected.

25. A method for providing a distributed motion network as set forth in claim 24, wherein the action performed by a node is selected from a group consisting of a visually detectable action, an auditory action, and a scent-based action.

26. A method for providing a distributed motion network as set forth in claim 25, further comprising a step of setting at least a portion of the nodes in a fixed position.

27. A method for providing a distributed motion network as set forth in claim 26, further comprising a step of hard-wiring at least a portion of the nodes together.

28. A method for providing a distributed motion network as set forth in claim 27, wherein the nodes selectively execute the action according to the logical expression, {overscore (C)}·

{overscore (D)}+B·

{overscore (C)}+A·

C·

{overscore (D)}+B·

C·

{overscore (D)}=Y, where A represents acurrent 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.

29. A method for providing a distributed motion network as set forth in claim 19, wherein as the object moves along the direction of travel, it is detected by different detecting nodes along the direction of travel, and where signals originating from different detecting nodes are compared at each current node in a comparing step to determine whether the current node is in front of or behind travel of the object.

30. A method for providing a distributed motion network as set forth in claim 19, wherein the nodes selectively perform a step of executing an action based on the hop count distance between a current node and a detecting node and the direction of travel of the object.

31. A method for providing a distributed motion network as set forth in claim 19, wherein the nodes further perform a step of detecting an object class for an object within the network.

32. A method for providing a distributed motion network as set forth in claim 31, wherein the action executed by the nodes based on the hop count distance is tailored based on the object class of an object detected.

33. A method for providing a distributed motion network as set forth in claim 32, wherein the action performed by a node is selected from a group consisting of a visually detectable action, an auditory action, and a scent-based action.

34. A method for providing a distributed motion network as set forth in claim 19, further comprising a step of setting at least a portion of the nodes in a fixed position.

35. A method for providing a distributed motion network as set forth in claim 19, further comprising a step of hard-wiring at least a portion of the nodes together.

36. A method for providing a distributed motion network as set forth in claim 19, wherein the nodes selectively execute the action according to the logical expression, {overscore (C)}·

{overscore (D)}+B·

{overscore (C)}+A·

C·

{overscore (D)}+B·

C·

{overscore (D)}=Y, where A represents acurrent 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.

37. 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:

detecting the presence of an object in an area around a node, with a node that detects an object termed a detecting node;

communicating a signal from a detecting node 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; and

propagating the signal from the local nodes to other nodes that are local to the local nodes such 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.

38. A computer program product as set forth in claim 37, the signal includes a hop count, and wherein the computer program product further comprises means for incrementing the hop count at each node as it is propagated between successive nodes away from the detecting node, the hop count being indicative of a hop count distance from the detecting node and a current node.

39. A computer program product as set forth in claim 38, further comprising means for comparing signals originating from different detecting nodes at each current node as the object moves along the direction of travel to determine whether the current node is in front of or behind travel of the object.

40. A computer program product as set forth in claim 39, further comprising means for causing the nodes to selectively perform an action based on the hop count distance between a current node and a detecting node and the direction of travel of the object.

41. A computer program product as set forth in claim 40, further comprising means for detecting, at each node, an object class for an object within the network.

42. A computer program product as set forth in claim 41, wherein the action performed by the nodes based on the hop count distance is tailored based on the object class of an object detected.

43. A computer program product as set forth in claim 42, wherein the action performed by a node is selected from a group consisting of a visually detectable action, an auditory action, and a scent-based action.

44. A computer program product as set forth in claim 43, 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.

45. A computer program product as set forth in claim 37, further comprising means for comparing signals originating from different detecting nodes at each current node as the object moves along the direction of travel to determine whether the current node is in front of or behind travel of the object.

46. A computer program product as set forth in claim 37, further comprising means for causing the nodes to selectively perform an action based on the hop count distance between a current node and a detecting node and the direction of travel of the object.

47. A computer program product as set forth in claim 37, further comprising means for detecting, at each node, an object class for an object within the network.

48. A computer program product as set forth in claim 47, wherein the action performed by the nodes based on the hop count distance is tailored based on the object class of an object detected.

49. A computer program product as set forth in claim 48, wherein the action performed by a node is selected from a group consisting of a visually detectable action, an auditory action, and a scent-based action.

50. 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.

51. A node for use in a distributed motion prediction network, the node comprising means for:

detecting the presence of an object in an area around the node, with the node that detects an object termed a detecting node;

when the node is a detecting node, communicating a signal from the detecting node to other 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; and

when the node is not a detecting node, but is a node that is local with the detecting node, propagating the signal to other nodes local to the node such that information regarding the presence of the object is progressively propagated to nodes away from a detecting node and so that as the object moves, the propagated signal is used to predict a direction of travel of the object.

52. A node for use in a distributed motion prediction network as set forth in claim 51, wherein the signal includes a hop count that is incremented by the node as it is propagated so that it is incremented between successive nodes away from a detecting node, the hop count, at the node, being indicative of a hop count distance from a detecting node.

53. A node for use in a distributed motion prediction network as set forth in claim 52, wherein as the object moves along the direction of travel, it is detected by different detecting nodes along the direction of travel, and where signals originating from different detecting nodes are compared at the node to determine whether a the node is in front of or behind travel of the object.

61. A node for use in a distributed motion prediction network as set forth in claim 51, wherein as the object moves along the direction of travel, it is detected by different detecting nodes along the direction of travel, and where signals originating from different detecting nodes are compared at the node to determine whether the node is in front of or behind travel of the object.

62. A node for use in a distributed motion prediction network as set forth in claim 51, 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.

63. A node for use in a distributed motion prediction network as set forth in claim 51, wherein the node further comprises a means for detecting an object class for an object within the network.

64. A node for use in a distributed motion prediction network as set forth in claim 63, wherein the action performed by the node based on the hop count distance is tailored based on the object class of an object detected.

65. A node for use in a distributed motion prediction network as set forth in claim 64, 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.

66. A node for use in a distributed motion prediction network as set forth in claim 51, wherein the node is configured to reside in a fixed position with respect to other nodes.

67. A node for use in a distributed motion prediction network as set forth in claim 51, wherein the node is configured to be hard-wired with other nodes.

68. A node for use in a distributed motion prediction network as set forth in claim 51, wherein the node operates 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.

54. 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.

55. A node for use in a distributed motion prediction network as set forth in claim 54, wherein the node further comprises a means for detecting an object class for an object within the network.

56. A node for use in a distributed motion prediction network as set forth in claim 55, wherein the action performed by the node based on the hop count distance is tailored based on the object class of an object detected.

57. A node for use in a distributed motion prediction network as set forth in claim 56, 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.

58. A node for use in a distributed motion prediction network as set forth in claim 57, wherein the node is configured to reside in a fixed position with respect to other nodes.

59. A node for use in a distributed motion prediction network as set forth in claim 58, wherein the node is configured to be hard-wired with other nodes.

60. A node for use in a distributed motion prediction network as set forth in claim 59, wherein the node operates 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.

69. A method for operating a node for use in a distributed motion prediction network including a plurality of nodes, the node performing steps of:

detecting the presence of an object in an area around the node, with the node that detects an object termed a detecting node;

when the node is a detecting node, communicating a signal from the detecting node 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; and

when the node is not a detecting node, but is a node that is local with the detecting node, propagating the signal to other nodes local to the node such that information regarding the presence of the object is progressively propagated to nodes away from a detecting node and so that as the object moves, the propagated signal is used to predict a direction of travel of the object.

70. A method for operating a node for use in a distributed motion network as set forth in claim 69, wherein the signal includes a hop count that is incremented by the node as it is propagated so that it is incremented between successive nodes away from a detecting node, the hop count, at the node, being indicative of a hop count distance from a detecting node.

71. A method for operating a node for use in a distributed motion network as set forth in claim 70, wherein as the object moves along the direction of travel, it is detected by different detecting nodes along the direction of travel, and where signals originating from different detecting nodes are compared at the node in a comparing step to determine whether the node is in front of or behind travel of the object.

72. A method for operating a node for use in a distributed motion network as set forth in claim 71, wherein the node selectively performs a step of executing an action based on the hop count distance between the node and a detecting node and the direction of travel of the object.

73. A method for operating a node for use in a distributed motion network as set forth in claim 72, wherein the node further performs a step of detecting an object class for an object within the network.

74. A method for operating a node for use in a distributed motion network as set forth in claim 73, wherein the action executed by the node is based on the hop count distance is tailored based on the object class of an object detected.

75. A method for operating a node for use in a distributed motion network as set forth in claim 74, 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.

79. A method for operating a node for use in a distributed motion network as set forth in claim 69, wherein as the object moves along the direction of travel, it is detected by different detecting nodes along the direction of travel, and where signals originating from different detecting nodes are compared at the node in a comparing step to determine whether the node is in front of or behind travel of the object.

80. A method for operating a node for use in a distributed motion network as set forth in claim 69, wherein the node selectively performs a step of executing an action based on the hop count distance between the node and a detecting node and the direction of travel of the object.

81. A method for operating a node for use in a distributed motion network as set forth in claim 69, wherein the node further performs a step of detecting an object class for an object within the network.

82. A method for operating a node for use in a distributed motion network as set forth in claim 81, wherein the action executed by the node is based on the hop count distance is tailored based on the object class of an object detected.

83. A method for operating a node for use in a distributed motion network as set forth in claim 82, 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.

84. A method for operating a node for use in a distributed motion network as set forth in claim 69, further comprising a step of setting the node in a fixed position.

85. A method for operating a node for use in a distributed motion network as set forth in claim 69, further comprising a step of hard-wiring the node in communicative contact with other nodes.

86. A method for operating a node for use in a distributed motion network as set forth in claim 69, wherein the node selectively executes the action according to the logical expression, {overscore (C)}·

{overscore (D)}+B·

{overscore (C)}+A·

C·

{overscore (D)}+B·

C·

{overscore (D)}=Y, where Arepresents 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.

76. 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.

77. A method for operating a node for use in a distributed motion network as set forth in claim 76, further comprising a step of hard-wiring the node in communicative contact with other nodes.

78. A method for operating a node for use in a distributed motion network as set forth in claim 77, wherein the node selectively executes the action according to the logical expression, {overscore (C)}·

{overscore (D)}+B·

{overscore (C)}+A·

C·

{overscore (D)}+B·

C·

{overscore (D)}=Y, where Arepresents 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.

87. 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:

detecting the presence of an object in an area around the node, with the node that detects an object termed a detecting node;

when the node is a detecting node, communicating a signal from the detecting node 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; and

when the node is not a detecting node, but is a node that is local with the detecting node, propagating the signal to other nodes local to the node such that information regarding the presence of the object is progressively propagated to nodes away from a detecting node and so that as the object moves, the propagated signal is used to predict a direction of travel of the object.

88. A computer program product as set forth in claim 87, the signal includes a hop count, and wherein the computer program product further comprises means for providing a hop count in the signal, and for incrementing the hop count by the node as it is propagated so that it is incremented between successive nodes away from a detecting node, the hop count, at the node, being indicative of a hop count distance from a detecting node.

89. A computer program product as set forth in claim 88, further comprising means for comparing signals originating from different detecting nodes at the node as the object moves along the direction of travel to determine whether the node is in front of or behind travel of the object.

95. A computer program product as set forth in claim 87, further comprising means for comparing signals originating from different detecting nodes at the node as the object moves along the direction of travel to determine whether the node is in front of or behind travel of the object.

96. A computer program product as set forth in claim 87, 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.

97. A computer program product as set forth in claim 87, further comprising means for detecting, at the node, an object class for an object within the network.

98. A computer program product as set forth in claim 97, 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.

99. A computer program product as set forth in claim 98, 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.

100. A computer program product as set forth in claim 87, 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.

90. 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.

91. 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.

92. 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.

93. 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.

94. 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.