System, methods and apparatus for integrating behavior-based approach into hybrid control model for use with mobile robotic vehicles

• US 20040068351A1
• Filed: 04/22/2003
• Published: 04/08/2004
• Est. Priority Date: 04/22/2002
• Status: Abandoned Application

• Current Assignee: Solomon Research LLC
• Original Assignee: Solomon Research LLC
• Inventors: Solomon, Neal
• Application Number:
  US10/421,547
• Publication Number:
  US 20040068351A1
• Time in Patent Office: Days
• US Class Current: 701/24
• CPC Class Codes: F41H 13/00 Means of attack or defence not otherwise provided for details of radar or sonar systems G01S7/00
• CPC Class Codes: G05D 1/0088 characterized by the autonomous decision making process, e.g. artificial intelligence, predefined behaviours using knowledge based models G06N5/00
• CPC Class Codes: G05D 1/0236 in combination with a laser lasers per se H01S
• CPC Class Codes: G05D 1/0255 using acoustic signals, e.g. ultra-sonic singals sonar systems designed for anti-collision purposes G01S15/93
• CPC Class Codes: G05D 1/0257 using a radar radar systems designed for anti-collision purposes between land vehicles or between land vehicle and fixed obstacles G01S13/931
• CPC Class Codes: G05D 1/0274 using mapping information stored in a memory device navigation using map-matching G01C21/30
• CPC Class Codes: G05D 1/0278 using satellite positioning signals, e.g. GPS
• CPC Class Codes: G05D 1/0295 by at least one leading vehicle of the fleet
• CPC Class Codes: G05D 1/104 involving a plurality of aircrafts, e.g. formation flying traffic control systems for aircraft G08G5/00

Abstract

Behavior-based models of robotic coordination are integrated into a synthetic hybrid control architecture of a multi-robotic system (MRS). The MRS uses rules to organize the priorities of squads of mobile robotic vehicles (MRVs) in a broader system that includes a central control model for planning, hierarchy and simulations. The system, and methods and apparatus thereof, is applied to the organization of the swarm weapon system.

Claims

1. A system for integrating behavior-based approach into a hybrid model for use with a plurality of mobile robotic vehicles (MRVs), comprising:

a plurality of central control systems;

a plurality of reactive control systems;

a central planning control configured to control the plurality of central control systems;

a behavior-based reactive control configured to control the plurality of reactive control systems;

an intermediated control layer configured to control the central planning control and the behavior-based reactive control;

a plurality of hybrid control models configured to communicate with the intermediate control layer;

a plurality of synthetic control models configured to communicate with the plurality of hybrid control models; and

a plurality of synthetic hybrid control models configured based on combinations of the plurality of hybrid control models and the plurality of synthetic control models.

2. The system of claim 1 wherein the plurality of hybrid control models include a planning driven model, an advice mediation model, an adaptation model and a postponement model.

3. The system of claim 1 wherein at least one of the plurality of central control models includes control of the plurality of MRVs, sensor data, computation resources, database memory and computation analyses that are shared amongst the plurality of MRVs.

5. The system of claim 1 wherein one or more of the plurality of MRVs make up a squad;

wherein the squad is decentralized to pure behavior-based interactions amongst the MRVs within the squad;

wherein environmental feedback stimulates MRV interactions according to a plurality of rules of behavior;

wherein each MRV within the squad responds to an environmental stimulus;

wherein the MRVs within the squad react to the environmental stimulus in a coordinated manner.

6. The system of claim 5 wherein the squad has a lead MRV and member MRVs;

wherein sensor data is transmitted from the member MRVs to the lead MRV;

wherein the lead MRV uses a plurality of metarules that identify situations in an environment and construct local rules based on initial program parameters;

wherein the lead MRV transmits the local rules to the member MRVs; and

wherein the member MRVs use the local rules to interact with each other and with the environment.

7. The system of claim 6 wherein the local rules include “

move toward center of a pack,”

“

avoid collisions with neighbors,” and

“

follow leader”

.

4. The system of claim 51 wherein the plurality of MRVs collectively function as a network;

and

wherein the network operates as follows;

checking hardware operations;

loading software to the plurality of MRVs in the network;

initializing program parameters, strategic goals and mission;

using sensor data from the plurality of MRVs to provide an initial terrain map and set up a path of action;

directing the plurality of MRVs to proceed on a mission along the path of action;

identifying one or more targets;

selecting one or more of the plurality of MRVs to attack the identified one or more targets;

directing the selected one or more of the plurality of MRVs to attack the identified one or more targets;

reporting effects of the attack; and

completing the attack based on the reported effects.