# US 12,001,221 B2 · Methods for managing coordinated autonomous teams of under-canopy robotic systems for an agricultural field and devices > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,001,221 B2 - Document ID: US12001221B2 - Status: Granted - Kind: B2 - Application number: 17/219,471 - Filed: 2021-03-31 - Published: 2022-10-06 - Granted: 2024-06-04 - Earliest claimed: 2021-03-31 - Assignees: Earthsense, Inc. - Applicants: EarthSense, Inc. - Inventors: Girish Chowdhary; Chinmay Soman; Michael Hansen; Joseph Byrnes - Web page: https://patentgrep.com/patent/US12001221B2 ## CPC - G05D 1/0287: {involving a plurality of land vehicles, e.g. fleet or convoy travelling (traffic control systems for road vehicles G08G1/00, particularly anticollision systems G08G1/16)} - A01B 69/001: {Steering by means of optical assistance, e.g. television cameras (steering devices for road marking vehicles E01C23/163)} - A01B 69/008: {automatic} - A01C 7/00: Sowing - A01C 21/00: Methods of fertilising {, sowing or planting} - A01M 7/0089: {Regulating or controlling systems (the delivery being related to the movement of a vehicle B05B9/06)} - A01M 21/00: Apparatus for the destruction of unwanted vegetation, e.g. weeds (control of undesirable vegetation on roads or permanent ways of railways E01H11/00) - G01C 21/3885: {Transmission of map data to client devices; Reception of map data by client devices} - G01S 17/88: Lidar systems specially adapted for specific applications - G01S 17/89: for mapping or imaging - G05D 1/0088: {characterized by the autonomous decision making process, e.g. artificial intelligence, predefined behaviours (using knowledge based models G06N5/00)} - G05D 1/0212: {with means for defining a desired trajectory (involving a plurality of land vehicles G05D1/0287)} - G05D 1/227: Handing over between remote control and on-board control; Handing over between remote control arrangements - G05D 1/242: Means based on the reflection of waves generated by the vehicle (using passive navigation aids external to the vehicle G05D1/244; using signals provided by artificial sources external to the vehicle G05D1/247) - G05D 1/243: Means capturing signals occurring naturally from the environment, e.g. ambient optical, acoustic, gravitational or magnetic signals (using passive navigation aids external to the vehicle G05D1/244; using signals from positioning sensors located off-board the vehicle G05D1/249) - G05D 1/246: using environment maps, e.g. simultaneous localisation and mapping [SLAM] - G05D 1/646: Following a predefined trajectory, e.g. a line marked on the floor or a flight path - G05D 1/6482: {by dividing the whole area or space in sectors to be processed separately} - G05D 1/692: involving a plurality of disparate vehicles - G05D 1/6987: {by centralised control off-board any of the vehicles} - G06Q 50/02: Agriculture; Fishing; Forestry; Mining - H04W 4/024: Guidance services - H04W 4/20: Services signaling; Auxiliary data signalling, i.e. transmitting data via a non-traffic channel - G05D 2107/21: {Farming, e.g. fields, pastures or barns} - G05D 2109/18: Holonomic vehicles, e.g. with omni wheels - G05D 2111/10: Optical signals - G05D 2111/17: {Coherent light, e.g. laser signals} - G06N 3/006: based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO] - G06N 20/00: Machine learning - G05D: SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES - A01B: SOIL WORKING IN AGRICULTURE OR FORESTRY; PARTS, DETAILS, OR ACCESSORIES OF AGRICULTURAL MACHINES OR IMPLEMENTS, IN GENERAL (making or covering furrows or holes for sowing, planting or manuring A01C5/00; mowers convertible to soil working apparatus or capable of soil working A01D42/04; mowers combined with soil working implements A01D43/12; soil working for engineering purposes E01, E02, E21) - A01C: PLANTING; SOWING; FERTILISING (parts, details or accessories of agricultural machines or implements, in general A01B51/00 - A01B75/00) - A01M: CATCHING, TRAPPING OR SCARING OF ANIMALS (appliances for catching swarms or drone-catching A01K57/00; fishing A01K69/00 - {A01K99/00}; biocides, pest repellants or attractants A01N); APPARATUS FOR THE DESTRUCTION OF NOXIOUS ANIMALS OR NOXIOUS PLANTS - G01C: MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY (measuring liquid level G01F; radio navigation, determining distance or velocity by use of propagation effects, e.g. Doppler effects, propagation time, of radio waves, analogous arrangements using other waves G01S) - G01S: RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES - G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR - H04W: WIRELESS COMMUNICATION NETWORKS (broadcast communication H04H; communication systems using wireless links for non-selective communication, e.g. wireless extensions H04M1/72) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## Abstract A method, system and non-transitory computer readable medium includes obtaining an electronic map of an agricultural field. One or more assignment instructions for each of a plurality of robotic systems in an assigned team are generated to optimize execution of a selected agricultural task with respect to at least one parameter based on the obtained electronic map, a number of the robotic systems in the team, and at least one capability of each of the robotic systems in the team. The robotic systems in the team are managed based on wireless transmission of the generated assignment instructions to the robotic systems. ## Claims ### Claim 1 A method comprising: obtaining, by a computing device, an electronic map of an agricultural field; generating, by the computing device, one or more assignment instructions for each of a plurality of robotic systems in an assigned team for a selected agricultural task, based on the obtained electronic map, a number of the plurality of robotic systems in the assigned team, and at least one capability of each of the plurality of robotic systems in the assigned team; converting, by the computing device, motion data received from each of the plurality of robotic systems into one or more electrical signals, to control motion of each of the plurality of robotic systems; managing, by the computing device, the plurality of robotic systems in the assigned team based on wireless transmission of the generated one or more assignment instructions to each of the plurality of robotic systems; receiving, by the computing device, issue data from a first robotic system of the plurality of robotic systems, wherein the issue data is related to an operational error in the first robotic system; and updating, by the computing device, navigational instructions to control the motion of one or more other robotic systems from the plurality of robotic systems based on task performance parameters, the issue data, and the historical data of the agricultural field associated with the issue data. ### Claim 2 (depends on 1) The method as set forth in claim 1, wherein the generating the one or more assignment instructions further comprises: tessellating, by the computing device, the obtained electronic map into sections for each of the robotic systems based on the obtained electronic map, the number of the plurality of robotic systems in the assigned team, the task performance parameters, and the at least one capability of each of the plurality of robotic systems in the assigned team, wherein the task performance parameters comprises at least one of a completion time limit threshold, a cost threshold, designated completion objective related to the selected agricultural task, and wherein the designated completion objective is a percentage of the selected agricultural task; wherein each of the plurality of robotic systems navigates autonomously based on the tessellating and fused data obtained from two or more LIDAR systems or cameras during the selected agricultural task. ### Claim 3 (depends on 2) The method as set forth in claim 2, further comprising: tessellating, by the computing device, the obtained electronic map into sections for each of the plurality of robotic systems based on the obtained electronic map, the number of the plurality of robotic systems in the assigned team, and the at least one capability of each of the plurality of robotic systems in the assigned team; and adjusting, by the computing device, the tessellation of the obtained electronic map based on the received issue data. ### Claim 4 (depends on 1) The method as set forth in claim 1, wherein the managing the plurality of robotic systems in the assigned team further comprises: predictively adjusting, by the computing device, the one or more assignment instructions for the one or more other robotic systems from the plurality of robotic systems in the assigned team based on the issue data and historical data about the agricultural field associated with the issue data; and managing, by the computing device, the plurality of robotic systems in the assigned team based on the adjusted one or more assignment instructions. ### Claim 5 (depends on 1) The method as set forth in claim 1, wherein the at least one capability comprises a scouting imaging system, a sprayer, a weeding system, or a planter. ### Claim 6 (depends on 1) The method as set forth in claim 1, wherein the one or more assignment instructions further comprise navigation instructions, task performance instructions related to the at least one capability of each of the plurality of robotic systems in the assigned team, and maintenance instructions. ### Claim 7 (depends on 1) The method as set forth in claim 1, wherein the issue data is related to progress of the first robotic system in obtaining and providing scouting data and at least one of an operational error indication and condition with the first robotic system. ### Claim 8 (depends on 1) The method as set forth in claim 1, wherein managing the robotic systems in the team further comprises: monitoring the plurality of robotic systems, to identify one or more issues, based on at least one of scouting data and sensor data received from the plurality of robotic systems, and wherein the sensor data comprises data received from an Inertial Measurement Unit (IMU), and wherein the IMU is installed on each of the plurality of robotic systems. ### Claim 9 (depends on 8) The method as claimed in claim 8, wherein the sensor data comprises data corresponding to specific force, angular rate, orientation, or a combination thereof, for each of the plurality of robotic systems as measured by the IMU. ### Claim 10 An agricultural management system, the system comprising: a memory comprising programmed instructions stored thereon and one or more processors configured to be capable of executing the stored programmed instructions to: obtain an electronic map of an agricultural field; generate one or more assignment instructions for each of a plurality of robotic systems in an assigned team for the selected agricultural task based on the obtained electronic map, a number of the robotic systems in the team, issue data, and at least one capability of each of the robotic systems in the team; convert motion data received from each of the plurality of robotic systems into one or more electrical signals, to control motion of each of the plurality of robotic systems; manage the plurality of robotic systems in the assigned team, based on wireless transmission of the generated assignment instructions to the plurality of robotic systems, receive issue data from a first robotic system of the plurality of robotic systems, wherein the issue data is related to an operational error in the first robotic system; and update navigational instructions to control the motion of one or more other robotic systems from the plurality of robotic systems based on task performance parameters, the issue data, and the historical data of the agricultural field associated with the issue data. ### Claim 11 (depends on 10) The system as set forth in claim 10, wherein to generate the one or more assignment instructions, the one or more processors are further configured to be capable of executing the stored programmed instructions to: tessellate the obtained electronic map into sections for each of the plurality of robotic systems based on the obtained electronic map, the number of the plurality of robotic systems in the assigned team, the task performance parameters, and the at least one capability of each of the plurality of robotic systems in the assigned team, wherein the task performance parameters comprises at least one of a completion time limit threshold, a cost threshold, designated completion objective related to the selected agricultural task, and wherein the designated completion objective is a percentage of the selected agricultural task; wherein each of the plurality of robotic systems navigates autonomously based on the tessellating and obtained fused data from two or more LIDAR systems or cameras during the selected agricultural task. ### Claim 12 (depends on 10) The system as set forth in claim 10, wherein the one or more processors are further configured to be capable of executing the stored programmed instructions to: tessellate the obtained electronic map into sections for each of the plurality of robotic systems based on the obtained electronic map, the number of the plurality of robotic systems in the assigned team, and the at least one capability of each of the plurality of robotic systems in the assigned team; and adjust the tessellation of the obtained electronic map based on the received issue data. ### Claim 13 (depends on 10) The system as set forth in claim 10, wherein to manage the plurality of robotic systems in the assigned team, the one or more processors are further configured to be capable of executing the stored programmed instructions to: predictively adjust the one or more assignment instructions for the one or more other robotic systems from the plurality of robotic systems in the assigned team based on the issue data and historical data about the agricultural field associated with the issue data; and manage the plurality of robotic systems in the assigned team based on the adjusted one or more assignment instructions. ### Claim 14 (depends on 10) The system as set forth in claim 10, wherein the at least one capability comprises a scouting imaging system, a sprayer, a weeding system, or a planter. ### Claim 15 (depends on 10) The system as set forth in claim 10, wherein the one or more assignment instructions further comprise navigation instructions, task performance instructions related to the at least one capability of each of the plurality of robotic systems in the assigned team, and maintenance instructions. ### Claim 16 (depends on 14) The non-transitory computer readable medium as set forth in claim 14, wherein generate the one or more assignment instructions, the executable code when executed by the one or more processors further causes the one or more processors to: tessellate the obtained electronic map into sections for each of the plurality of robotic systems based on the obtained electronic map, the number of the plurality of robotic systems in the assigned team, the task performance parameters, and the at least one capability of each of the plurality of robotic systems in the assigned team, wherein the task performance parameters comprises at least one of a completion time limit threshold, a cost threshold, designated completion objective related to the selected agricultural task, and wherein the designated completion objective is a percentage of the selected agricultural task; wherein each of the plurality of robotic systems navigates autonomously based on the tessellating and fused data obtained from two or more LIDAR systems or cameras during the selected agricultural task. ### Claim 17 (depends on 15) The non-transitory computer readable medium as set forth in claim 15, wherein the one or more processors are further configured to be capable of executing the stored programmed instructions to: tessellate the obtained electronic map into sections for each of the plurality of robotic systems based on the obtained electronic map, the number of the plurality of robotic systems in the assigned team, and the at least one capability of each of the plurality of robotic systems in the assigned team; and adjust the tessellation of the obtained electronic map based on the issue data. ### Claim 18 (depends on 14) The non-transitory computer readable medium as set forth in claim 14, wherein to manage the plurality of robotic systems in the assigned team, the executable code when executed by the one or more processors further causes the one or more processors to: predictively adjust the one or more assignment instructions for the one or more other robotic systems from the plurality of robotic systems in the assigned team, based on the issue data and historical data about the agricultural field associated with the issue data; and manage the robotic systems in the team based on the adjusted one or more assignment instructions. ### Claim 19 (depends on 14) The non-transitory computer readable medium as set forth in claim 14, wherein the at least one capability comprises a scouting imaging system, a sprayer, a weeding system, or a planter. ### Claim 20 (depends on 14) The non-transitory computer readable medium as set forth in claim 14, wherein the one or more assignment instructions further comprise navigation instructions, task performance instructions related to the at least one capability of each of the plurality of robotic systems in the assigned team, and maintenance instructions. ### Claim 21 A non-transitory computer readable medium having stored thereon instructions comprising executable code which when executed by one or more processors, causes the one or more processors to: obtain an electronic map of an agricultural field; generate one or more assignment instructions for each of a plurality of robotic systems in an assigned team for the selected agricultural task based on the obtained electronic map, a number of the plurality of robotic systems in the assigned team, and at least one capability of each of the plurality of robotic systems in the assigned team; convert motion data received from each of the plurality of robotic systems into one or more electrical signals, to control motion of each of the plurality of robotic systems; manage the plurality of robotic systems in the assigned team based on wireless transmission of the generated assignment instructions to the plurality of robotic systems, receive issue data from a first robotic system of the plurality of robotic systems, wherein the issue data is related to an operational error in the first robotic system; and update navigational instructions to control the motion of one or more other robotic systems from the plurality of robotic systems based on task performance parameters, the issue data, and the historical data of the agricultural field associated with the issue data.