# US 12,759,133 B2 · System and method for manipulating an omnidirectional cart using an armed robot > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,759,133 B2 - Document ID: US12759133B2 - Status: Granted - Kind: B2 - Application number: 18/983,979 - Filed: 2024-12-17 - Published: 2026-06-18 - Granted: 2026-10-13 - Earliest claimed: 2024-12-17 - Assignees: Toyota Motor Engineering & Manufacturing North America, Inc.; Toyota Jidosha Kabushiki Kaisha - Applicants: Toyota Motor Engineering & Manufacturing North America, Inc. - Inventors: Bardh Hoxha; Georgios Fainekos; Hideki Okamoto; Danil V. Prokhorov; Mitchell Black - Web page: https://patentgrep.com/patent/US12759133B2 ## CPC - B25J 5/007: {mounted on wheels} - B25J 9/161: {Hardware, e.g. neural networks, fuzzy logic, interfaces, processor} - B25J 9/1664: {characterised by motion, path, trajectory planning} - B25J 9/1679: {characterised by the tasks executed} - B25J 11/008: {Manipulators for service tasks} - B25J: MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES ({manipulators specially adapted for use in surgery A61B34/70; manipulators used in cleaning hollow articles B08B9/04}; manipulators associated with rolling mills B21B39/20; manipulators associated with forging machines B21J13/10; {manipulators associated with picking-up and placing mechanisms B23P19/007}; means for holding wheels or parts thereof B60B30/00; {vehicles with ground-engaging propulsion means, e.g. walking members B62D57/02, B62D57/032; devices for picking-up and depositing articles or materials between conveyors B65G47/90, B65G47/91; manipulators with gripping or holding means for transferring packages B65H67/065}; cranes B66C; {manipulators used in the protection or supervision of pipe-line installations F17D5/00; walking equipment adapted for nuclear steam-generators F22B37/006}; manipulators specially adapted for, or associated with, nuclear reactors G21C; {apparatus used for handling wafers during manufacture or treatment of semiconductor H10P72/50}) ## Abstract Systems and methods for manipulating an omnidirectional cart using an armed mobile robot are described herein. In one example, a system includes a processor and a memory in communication with the processor. The memory includes instructions that, when executed by the processor, cause the processor to determine a control input for controlling a mobile robot with at least two arms configured to manipulate an omnidirectional cart. The control input is based on the trajectory and the state of the mobile robot when the at least two arms are manipulating the omnidirectional cart. Once the control input is determined, the system then controls the movement of the mobile robot using the control input. ## Claims ### Claim 1 A system comprising a memory having instructions that, when executed by a processor, cause the processor to: determine a control input for controlling a mobile robot with at least two arms configured to manipulate an omnidirectional cart, the control input being based on a trajectory and a state of the mobile robot when the at least two arms are manipulating the omnidirectional cart, wherein the state was determined using a robot-cart model that models interconnected dynamics between the omnidirectional cart and the mobile robot; and control a movement of the mobile robot using the control input. ### Claim 2 (depends on 1) The system of claim 1, wherein the memory further comprises instructions that, when executed by the processor, cause the processor to determine the trajectory using a closed-loop rapidly-exploring random tree planner. ### Claim 3 (depends on 1) The system of claim 1, wherein the memory further comprises instructions that, when executed by the processor, cause the processor to determine the trajectory using at least one of a neural network and a reinforcement learning agent. ### Claim 4 (depends on 1) The system of claim 1, wherein the memory further comprises instructions that, when executed by the processor, cause the processor to: determine a predicted control input using a model predictive control methodology that uses the trajectory and the state as inputs; and apply a predictive risk-aware control barrier function to the predicted control input to determine the control input. ### Claim 5 (depends on 4) The system of claim 4, wherein applying the predictive risk-aware control barrier function is implemented via a quadratic program. ### Claim 6 (depends on 4) The system of claim 4, wherein the predictive risk-aware control barrier function applies one or more constraints comprising at least one of an obstacle avoidance, a collision avoidance, and a speed limit. ### Claim 7 (depends on 1) The system of claim 1, wherein the state of the mobile robot includes at least one of a location, heading, and velocity. ### Claim 8 A method comprising: determining a control input for controlling a mobile robot with at least two arms configured to manipulate an omnidirectional cart, the control input being based on a trajectory and a state of the mobile robot when the at least two arms are manipulating the omnidirectional cart, wherein the state was determined using a robot-cart model that models interconnected dynamics between the omnidirectional cart and the mobile robot; and controlling a movement of the mobile robot using the control input. ### Claim 9 (depends on 8) The method of claim 8, further comprising determining the trajectory using a closed-loop rapidly-exploring random tree planner. ### Claim 10 (depends on 8) The method of claim 8, further comprising determining the trajectory using at least one of a neural network and a reinforcement learning agent. ### Claim 11 (depends on 8) The method of claim 8, further comprising: determining a predicted control input using a model predictive control methodology that uses the trajectory and the state as inputs; and applying a predictive risk-aware control barrier function to the predicted control input to determine the control input. ### Claim 12 (depends on 11) The method of claim 11, wherein applying the predictive risk-aware control barrier function is implemented via a quadratic program. ### Claim 13 (depends on 11) The method of claim 11, wherein the predictive risk-aware control barrier function applies one or more constraints comprising at least one of an obstacle avoidance, a collision avoidance, and a speed limit. ### Claim 14 (depends on 8) The method of claim 8, wherein the state of the mobile robot includes at least one of a location, heading, and velocity. ### Claim 15 A non-transitory computer-readable medium having instructions that, when executed by a processor, cause the processor to: determine a control input for controlling a mobile robot with at least two arms configured to manipulate an omnidirectional cart, the control input being based on a trajectory and a state of the mobile robot when the at least two arms are manipulating the omnidirectional cart, wherein the state was determined using a robot-cart model that models interconnected dynamics between the omnidirectional cart and the mobile robot; and control a movement of the mobile robot using the control input. ### Claim 16 (depends on 15) The non-transitory computer-readable medium of claim 15, further comprising instructions that, when executed by the processor, cause the processor to determine the trajectory using a closed-loop rapidly-exploring random tree planner. ### Claim 17 (depends on 15) The non-transitory computer-readable medium of claim 15, further comprising instructions that, when executed by the processor, cause the processor to determine the trajectory using at least one of a neural network and a reinforcement learning agent.