Teaming in swarm intelligent robot sets

• US 10,409,291 B2
• Filed: 03/27/2017
• Issued: 09/10/2019
• Est. Priority Date: 03/27/2017
• Status: Active Grant

• Current Assignee: Kyndryl Incorporated
• Original Assignee: International Business Machines Corporation
• Inventors: Baughman, Aaron K., Bastian, David C., McCrory, Nicholas A., Whitman, Todd R.
• Primary Examiner(s): Tran, Khoi H
• Assistant Examiner(s): Peche, Jorge O
• Application Number:
  US15/469,635
• Publication Number:
  US 20180275679A1
• Time in Patent Office: 897 Days
• CPC Class Codes: B25J 13/006 by means of a wireless system for controlling one or several manipulators
• CPC Class Codes: B25J 9/162 Mobile manipulator, movable base with manipulator arm mounted on it
• CPC Class Codes: B25J 9/1661 characterised by task planning, object-oriented languages
• CPC Class Codes: B25J 9/1669 characterised by special application, e.g. multi-arm co-operation, assembly, grasping
• CPC Class Codes: G05B 2219/39146 Swarm, multiagent, distributed multitask fusion, cooperation multi robots
• CPC Class Codes: G05D 1/0027 involving a plurality of vehicles, e.g. fleet or convoy travelling fleet control of land vehicles from a control room G05D1/0297; traffic control systems for road vehicles G08G1/00; for marine craft G08G3/00; for aircraft G08G5/00
• CPC Class Codes: G05D 1/0221 involving a learning process
• CPC Class Codes: G05D 1/0246 using a video camera in combination with image processing means
• CPC Class Codes: G05D 1/0291 Fleet control monitoring fleets in traffic control systems for road vehicles G08G1/127, G08G1/127
• CPC Class Codes: G05D 1/0297 by controlling means in a control room
• CPC Class Codes: G06Q 10/00 Administration; Management
• CPC Class Codes: G06V 20/10 Terrestrial scenes scenes under surveillance with static cameras G06V20/52; scenes perceived from the exterior of a vehicle G06V20/56; scenes perceived from the interior of a vehicle G06V20/59

Abstract

Embodiments of the present invention provide a method, system and computer program product for teaming in swarm intelligent robot sets. The method includes programming a multiplicity of robots in a multi-robot set with a particular locomotive model and assigning each of the robots to different individual tasks corresponding to different individual objectives of a problem based upon the particular locomotive model of the corresponding robots. The method additionally includes deploying the multi-robot set into a confined geographic area and surveilling each robot to ensure that each robot assigned to a corresponding task is achieving the assigned task. Finally, the method includes responding to one of the robots appearing to be unable to complete a corresponding assigned one of the tasks by selecting a different robot with a locomotive model considered compatible with the corresponding assigned one of the tasks to complete the corresponding assigned one of the tasks.

Claims

1. A method of teaming in swarm intelligent robot sets having stronger and weaker performing robots, the method comprising:

programming a multiplicity of robots in a multi-robot set with a particular locomotive model;

assigning each of the multiplicity of robots to different individual tasks corresponding to different individual objectives of a problem based upon the particular locomotive model of the corresponding robots;

deploying the multi-robot set into a confined geographic area;

surveilling each robot to ensure that each robot assigned to a corresponding one of the different tasks is achieving the assigned one of the tasks; and

,

responsive to one of the robots in the multi-robot set failing to complete a corresponding assigned one of the tasks, selecting a different one of the robots in the multi-robot set with a locomotive model considered compatible with the corresponding assigned one of the tasks to complete the corresponding assigned one of the tasks and assigning to the one of the robots failing to complete the corresponding assigned one of the tasks, a different task assigned to the different one of the robots and training weaker performing ones of the robots in the set by stronger performing ones of the robots in set so that tasks of the problem are re-assigned more effectively to the robots in the set.

2. The method of claim 1, further comprising modifying the locomotive model of the one of the robots with the locomotive model of the selected different one of the robots.

3. The method of claim 1, wherein the robots of the multi-robot set are communicatively linked to one another over a wireless computer communications network and each of the robots surveils at least one other of the robots in the multi-robot set to ensure that the surveilled other robot assigned to a corresponding one of the different tasks is achieving the assigned one of the tasks.

4. The method of claim 1, further comprising responding to one of the robots in the multi-robot set failing to complete a corresponding assigned one of the tasks by re-assigning each of the multiplicity of robots to the different individual tasks corresponding to the different individual objectives of a problem based upon the particular locomotive model of the corresponding robots and in consideration of the inability of the one of the robots in the multi-robot set failing to complete the corresponding assigned one of the tasks.

5. The method of claim 1, wherein each of the multiplicity of robots is assigned to different individual tasks corresponding to different individual objectives of a problem based upon the particular locomotive model of the corresponding robots utilizing a genetic algorithm in consideration of a population of a candidate set of possible assignments, and wherein ones of the multiplicity of robots are repeatedly re-assigned over time to other different individual tasks corresponding to the different individual objectives of the problem utilizing different iterations of the population of the candidate set of possible assignments.

6. A swarm robotics data processing system configured for teaming in swarm intelligent robot sets having stronger and weaker performing robots, the system comprising:

a host computing platform comprising one or more computers each with memory and at least one processor;

a multiplicity of robots in a multi-robot set deployed in a confined geographic area and in wireless communication with the host computing platform, each of the robots having at least one microprocessor, memory and a locomotive platform comprising one or more locomotive elements driven by a motor controlled by a controller coupled to the at least one microprocessor, each of the robots additionally including programming specifying a particular locomotive model defining a permissible range of movement of a corresponding one of the robots;

a teaming module executing in memory of the host computing platform, the module comprising program code that assigns each of the multiplicity of robots to different individual tasks corresponding to different individual objectives of a problem based upon the particular locomotive model of the corresponding robots, surveils each robot to ensure that each robot assigned to a corresponding one of the different tasks is achieving the assigned one of the tasks and responds to one of the robots in the multi-robot set failing to complete a corresponding assigned one of the tasks by selecting a different one of the robots in the multi-robot set with a locomotive model considered compatible with the corresponding assigned one of the tasks to complete the corresponding assigned one of the tasks and assigning to the one of the robots failing to complete the corresponding assigned one of the tasks, a different task assigned to the different one of the robots and training weaker performing ones of the robots in the set by stronger performing ones of the robots in set so that tasks of the problem are re-assigned more effectively to the robots in the set.

7. The system of claim 6, wherein the program code of the teaming module further modifies the locomotive model of the one of the robots with the locomotive model of the selected different one of the robots.

8. The system of claim 6, wherein the robots of the multi-robot set are communicatively linked to one another over a wireless computer communications network and each of the robots surveils at least one other of the robots in the multi-robot set to ensure that the surveilled other robot assigned to a corresponding one of the different tasks is achieving the assigned one of the tasks.

9. The system of claim 6, wherein the program code of the teaming module is enabled to respond to one of the robots in the multi-robot set failing to complete a corresponding assigned one of the tasks by re-assigning each of the multiplicity of robots to the different individual tasks corresponding to the different individual objectives of a problem based upon the particular locomotive model of the corresponding robots and in consideration of the inability of the one of the robots in the multi-robot set failing to complete the corresponding assigned one of the tasks.

10. The system of claim 6, wherein each of the multiplicity of robots is assigned to different individual tasks corresponding to different individual objectives of a problem based upon the particular locomotive model of the corresponding robots utilizing a genetic algorithm in consideration of a population of a candidate set of possible assignments, and wherein ones of the multiplicity of robots are repeatedly re-assigned over time to other different individual tasks corresponding to the different individual objectives of the problem utilizing different iterations of the population of the candidate set of possible assignments.

11. A computer program product for teaming in swarm intelligent robot sets having stronger and weaker performing robots, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, wherein the computer readable storage medium is not a transitory signal per se, the program instructions executable by a device to cause the device to perform a method comprising:

programming a multiplicity of robots in a multi-robot set with a particular locomotive model;

assigning each of the multiplicity of robots to different individual tasks corresponding to different individual objectives of a problem based upon the particular locomotive model of the corresponding robots;

deploying the multi-robot set into a confined geographic area;

surveilling each robot to ensure that each robot assigned to a corresponding one of the different tasks is achieving the assigned one of the tasks; and

, responsive to one of the robots in the multi-robot set failing to complete a corresponding assigned one of the tasks, selecting a different one of the robots in the multi-robot set with a locomotive model considered compatible with the corresponding assigned one of the tasks to complete the corresponding assigned one of the tasks and assigning to the one of the robots failing to complete the corresponding assigned one of the tasks, a different task assigned to the different one of the robots and training weaker performing ones of the robots in the set by stronger performing ones of the robots in set so that tasks of the problem are re-assigned more effectively to the robots in the set.

12. The computer program product of claim 11, wherein the method further comprises modifying the locomotive model of the one of the robots with the locomotive model of the selected different one of the robots.

13. The computer program product of claim 11, wherein the robots of the multi-robot set are communicatively linked to one another over a wireless computer communications network and each of the robots surveils at least one other of the robots in the multi-robot set to ensure that the surveilled other robot assigned to a corresponding one of the different tasks is achieving the assigned one of the tasks.

14. The computer program product of claim 11, wherein the method further comprises responding to one of the robots in the multi-robot set failing to complete a corresponding assigned one of the tasks by re-assigning each of the multiplicity of robots to the different individual tasks corresponding to the different individual objectives of a problem based upon the particular locomotive model of the corresponding robots and in consideration of the inability of the one of the robots in the multi-robot set failing to complete the corresponding assigned one of the tasks.

15. The computer program product of claim 11, wherein each of the multiplicity of robots is assigned to different individual tasks corresponding to different individual objectives of a problem based upon the particular locomotive model of the corresponding robots utilizing a genetic algorithm in consideration of a population of a candidate set of possible assignments, and wherein ones of the multiplicity of robots are repeatedly re-assigned over time to other different individual tasks corresponding to the different individual objectives of the problem utilizing different iterations of the population of the candidate set of possible assignments.