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WO2025203452A1 - Système robotisé, procédé de planification de fonctionnement et programme - Google Patents

Système robotisé, procédé de planification de fonctionnement et programme

Info

Publication number
WO2025203452A1
WO2025203452A1 PCT/JP2024/012717 JP2024012717W WO2025203452A1 WO 2025203452 A1 WO2025203452 A1 WO 2025203452A1 JP 2024012717 W JP2024012717 W JP 2024012717W WO 2025203452 A1 WO2025203452 A1 WO 2025203452A1
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WO
WIPO (PCT)
Prior art keywords
plan
model
robot
motion
tasks
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
PCT/JP2024/012717
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English (en)
Japanese (ja)
Inventor
真澄 一圓
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
NEC Corp
Original Assignee
NEC Corp
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Filing date
Publication date
Application filed by NEC Corp filed Critical NEC Corp
Priority to PCT/JP2024/012717 priority Critical patent/WO2025203452A1/fr
Publication of WO2025203452A1 publication Critical patent/WO2025203452A1/fr
Pending legal-status Critical Current
Anticipated expiration legal-status Critical

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Classifications

    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00—Programme-controlled manipulators
    • B25J9/16—Programme controls

Definitions

  • Patent Document 1 reduces the amount of calculation required by narrowing the scope of calculation when recalculating if any changes are made.
  • the initial motion plan which still requires a huge amount of calculation.
  • This disclosure has been made in light of the above circumstances, and one of its objectives is to provide technology that contributes to reducing calculation time across the entire generation of motion plans for robot control.
  • the storage medium can be a non-transitory medium such as a semiconductor memory, hard disk, magnetic recording medium, or optical recording medium.
  • the present disclosure can also be embodied as a computer program product.
  • This disclosure can contribute to reducing calculation time across the entire generation of motion plans for robot control.
  • FIG. 2 is a hardware configuration diagram of an example of a hardware configuration of a robot system according to the present disclosure.
  • FIG. 1(a) is a functional block diagram of a robot system 100 according to this embodiment.
  • the robot system 100 When a user provides a target state S1, the robot system 100 generates a motion plan S3 for the robot 600 using pre-stored data S2. The robot system 100 then generates a control instruction sequence S4 from the motion plan and supplies the control instruction sequence S4 to the robot 600.
  • the pre-stored data will be simply referred to as stored data S2.
  • the task motion planner 200 When generating the motion plan S3, the task motion planner 200 creates a model that abstracts the information necessary for generating the motion plan in a calculable form.
  • Information necessary for generating the motion plan includes, for example, the shape of the object, the task execution point, and the point of action.
  • creating an abstract model will also be referred to as modeling.
  • objects to be modeled include hands, obstacles, workpieces, etc.
  • Modeling is performed using the retained data S2.
  • the task motion planner 200 performs optimization calculations to generate the motion plan S3.
  • the initial model abstracts the state of each component so that it can be defined as a constraint equation for the optimization problem during the optimization calculations.
  • the motion plan is generated in a predetermined time unit.
  • the time unit for generating the motion plan will be referred to as the time step length.
  • the task motion planner 200 first creates an initial model from the retained data S2. Then, for example, by aggregating tasks in the initial model, the initial model is abstracted to create a first model. At this time, the time step length may be increased. A first motion plan is then generated using the first model. That is, as shown in Figure 1(b), instead of generating a motion plan to achieve a target state using an initial model with a low level of abstraction, the task motion planner 200 generates a first motion plan as an overall motion plan using a first model with a higher level of abstraction than the initial model. The generated first motion plan is then divided, and within each divided motion plan, the motion plan is recalculated using the pre-aggregation tasks included in that motion plan as the target task. The recalculated motion plans are then combined to generate a motion plan.
  • the abstraction unit 210 aggregates the tasks to be executed by the robot 600 from an initial model that abstracts the retained data S2, which is information necessary for generating an operation plan for the robot 600.
  • the retained data S2 includes environmental information such as the shapes of the hand, obstacles, and workpiece, the point of application of the hand, the working point of the task, the initial position of the workpiece, the target position of the workpiece, a map of the workspace, constraints, the degree of abstraction when creating the initial model, the time step length to be used, etc. As described above, the retained data S2 is set so that a motion plan with the desired accuracy can be obtained using the created initial model. Furthermore, this information is stored in advance in a storage device of the robot system 100. Note that this information may also be stored in an external storage device accessible by the robot system 100.
  • an initial model that abstracts this information is created in advance. Abstraction is the process of replacing information with less data. In other words, creating an initial model simplifies the complex object shapes and physical behaviors handled by the robot system 100, making them easier to handle.
  • the abstraction unit 210 aggregates tasks to create a first model that further enhances the level of abstraction of the created initial model. That is, the abstraction unit 210 aggregates tasks to enhance the level of abstraction.
  • the abstraction unit 210 aggregates tasks according to predetermined rules. For example, multiple tasks whose inter-workpoint distances are within a predetermined threshold are aggregated into a single task.
  • the rules and thresholds are stored in the storage device.
  • information such as the required time and execution order of the tasks before aggregation within each aggregated task is also stored in the storage device.
  • FIG. 2(a) A specific example is shown in Figure 2(a).
  • tasks with work points 811 to 816 are used as an example.
  • tasks with work points 811 and 812 are aggregated into one task, which is designated as work point 821.
  • four tasks with work points 813 to 816 are aggregated into one task, which is designated as work point 822.
  • the abstraction unit 210 performs this processing based on the position information of the work points of each task, which is stored in advance in a storage device.
  • the plan generation unit 230 generates an operation plan from the first operation plan using an initial model.
  • the obtained first operation plan is divided in the time direction to obtain divided periods 700-1, 700-2, and 700-3.
  • the divided period will be represented by divided period 700.
  • the plan generation unit 230 recalculates an operation plan using the initial model based on that partial plan.
  • an operation plan for that divided period 700 is calculated.
  • the plan generation unit 230 limits the time range and target tasks and recalculates the operation plan at the abstraction level of the initial model. Finally, the recalculated operation plans are combined in chronological order for the original divided period 700 to generate an operation plan.
  • the partial plan for divided period 700-2 includes a portion of post-aggregation task B, which is executed by hand 1, and a portion of post-aggregation task E, which is executed by hand 2.
  • Post-aggregation task B within divided period 700-2 includes pre-aggregation tasks a-d
  • post-aggregation task E includes pre-aggregation tasks h-k.
  • the plan generation unit 230 recalculates the motion plan using the initial model for pre-aggregation tasks a-d and h-k included in this period.
  • the division method for the first motion plan is not limited to this.
  • the division should be made so that calculations can be performed in real time.
  • the division direction is also not limited to the time direction; for example, division may be made spatially.
  • the abstraction unit 210 aggregates tasks from a previously created initial model in accordance with predetermined rules to create a first model (step S1101).
  • the first generation unit 220 generates a first operation plan using the created first model (step S1102).
  • the plan generation unit 230 generates a motion plan from the first motion plan using the initial model (step S1103), outputs it to the robot controller 400, and ends the process.
  • the robot system 100 of this embodiment aggregates tasks to be executed by the robot 600 from an initial model created using pre-stored information, which is information necessary for planning the robot 600's motion, to create a first model that further abstracts the initial model.
  • a rough motion plan is then generated using the first model with a higher level of abstraction.
  • a detailed plan is recalculated using the initial model for the pre-aggregation tasks included in that partial plan.
  • the obtained detailed plans are combined to create a motion plan.
  • the robot controller 400 Based on the motion plan output from the task motion planner 200, the robot controller 400 creates a command sequence to control the robot 600 and operates the robot 600.
  • an overall motion plan is first generated using a model with a higher level of abstraction than the initial one. Then, the target range and target task are limited and a detailed motion plan is generated using the model with the initial level of abstraction.
  • the motion plan for the robot 600 is generated in two stages. In generating the motion plan at each stage, a mixed integer linear problem is solved with fewer constraints, so the amount of calculation at each stage can be reduced. This also shortens the calculation time for the overall motion plan.
  • this embodiment reduces the calculation time required for generating motion plans for robot control.
  • the robot system 100 of this embodiment is used to control a robot 600 that performs the task of welding multiple locations (welding locations: work points 630) on a vehicle body 620, as shown in FIG. 6(a). Multiple robots 600 may also work in coordination. A hand 601 is attached to the tip of each robot 600.
  • a case is shown in which two robots 600_#1 and 600_#2 are used.
  • the number of robots 600 is not limited to this. When there is no need to distinguish between the two, they are represented by robot 600.
  • the task motion planner 200 of this embodiment like the first embodiment, generates a motion plan from a given target state and information (retained data S2) required to generate a motion plan for the robot 600. That is, the task motion planner 200 first performs modeling, abstracting the target state S1 and retained data S2 into a form that can be handled mathematically. The retained data S2 is the same as in the first embodiment. The task motion planner 200 then formulates the created model using linear constraint equations that include integer variables. Then, under the constraint conditions expressed by the obtained constraint equations, it solves a mixed integer linear problem and outputs a solution that optimizes the objective function as a motion plan.
  • the robot controller 400 generates a control instruction sequence for controlling the robot 600 based on the received motion plan.
  • the input device 310 accepts instructions from the user. In this embodiment, for example, it accepts input of a target state from the user.
  • the model creation unit 202 determines a goal logical formula from the goal state.
  • the goal logical formula is written, for example, in a temporal logic expression. Specifically, when a goal state is presented in which tasks a to c are to be performed, this goal state is expressed in temporal expression as " ⁇ (task a completion state) ⁇ (task b completion state) ⁇ (task c completion state)."
  • ⁇ is a logical operator that means that it will eventually become true
  • ⁇ is a logical AND.
  • the model creation unit 202 also abstracts tasks. Here, the execution status of the task, the execution procedure of the task in each robot 600, and the allocation of tasks to each robot 600 are abstracted. At this time, the model creation unit 202 uses object models stored as object model information.
  • the object models used are, for example, a model of the hand 601 of the robot 600, a model of the object to be worked on (workpiece), a working point 630, a model of an obstacle (for example, the car body 620 in Figure 6(a)), etc. These are represented by three-dimensional figures such as a rectangular parallelepiped, cube, sphere, cone, etc. and/or two-dimensional figures such as a polygon, ellipse, etc.
  • FIG. 7(b) An example of these modeling methods is shown in Figure 7(b).
  • task execution state models examples of hand-only tasks, such as welding, which are tasks performed by a hand alone, and workpiece handling tasks, such as picking up a workpiece, are shown. Also shown are task procedures and assignment models for two robots, 600_#1 and 600_#2.
  • the model creation unit 202 also models the dynamic characteristics of the robot 600.
  • the dynamic characteristics include a motion model of the hand 601 (model 691) and a collision avoidance model.
  • 694 is a model of an obstacle.
  • the motion plan generation unit 203 expresses (formulates) the created model, for example, as a linear mathematical expression including integer variables. It then uses this as a constraint equation to solve a mixed integer linear problem and generate a motion plan.
  • Figure 6(b) shows an example of a motion plan S3 generated by the motion plan generation unit 203.
  • the motion plan indicates, for example, the tasks to be executed for each robot 600 and for each time step, the position and posture of the hand 601, etc.
  • the task motion planner 200 of this embodiment first generates a motion plan (first motion plan) with a long time step length using a model (first model) with a higher level of abstraction than the initial model.
  • first motion plan a motion plan with a long time step length
  • first model a model with a higher level of abstraction than the initial model.
  • the time range and target task are limited, and motion plans are recalculated with the initial time step length using the initial model.
  • the recalculated motion plans are then combined to generate a motion plan to be output to the robot controller 400.
  • a high level of abstraction means that the amount of data (number of components) has been reduced by abstraction compared to the initial model.
  • the storage device 330 stores data necessary for the task motion planner 200 to generate a motion plan, as well as data generated when generating the motion plan. Note that the storage device 330 may be provided external to the task motion planner 200.
  • the initial model creation unit 240 creates an initial model.
  • the initial model is a model created by the model creation unit 202 of the task motion planner 201. That is, like the first embodiment, the initial model is information including tasks to be executed by the robot 600, and is a model created by abstracting the information necessary to generate a motion plan for the robot 600. Therefore, the function of the initial model creation unit 240 is the same as that of the model creation unit 202.
  • the initial model creation unit 240 stores data of the created initial model in the storage device 330.
  • the abstraction unit 210 performs a conversion process to create a first model from an initial model by aggregating tasks in accordance with predetermined creation rules.
  • the creation rules used when creating the first model are stored in advance in the storage device 330. For example, the user registers them in the storage device 330 via the input device 310.
  • aggregating tasks refers to the process of reducing the number of tasks that make up a model, as shown in Figures 2(a) and 2(b). For example, multiple tasks whose inter-working point distances are within a predetermined threshold may be aggregated into a single task, or tasks may be aggregated taking into account the balance of task execution times.
  • Figure 8(a) is an example of an initial model.
  • the initial model includes an initial time step length (initial TSL) of ⁇ t, a model 621 of the shape of the obstacle, a model 611 of the shape of the hand 601, a model 631 of the task's working point (execution point), and a model 641 showing the point of action on the hand 601.
  • the obstacle corresponds to the car body 620 shown in Figure 6(a), and its shape is modeled as a combination of multiple rectangular parallelepipeds and truncated pyramids, etc.
  • the shape of the hand 601 approximates a welding hand, and is modeled as multiple rectangular parallelepipeds.
  • Figure 8(b) is an example of a first model that increases the level of abstraction of the initial model.
  • the first model shows a first time step length (first TSL) of ⁇ T, a model 622 of the shape of the obstacle, a model 612 of the shape of the hand, a model 632 of the working point (execution point) of the task, and a model 641 showing the point of action on the hand.
  • the shape of the obstacle is modeled as a single rectangular parallelepiped.
  • the shape of the hand is modeled as a single rectangular parallelepiped.
  • the abstraction unit 210 may also perform these abstractions. That is, it performs a conversion process on the modeling target to reduce the number of constraint equations, and creates a first model. These processes reduce the number of model components, and the number of constraint equations in the mixed integer linear problem to be solved when generating an action plan. This reduces the amount of calculation.
  • the abstraction unit 210 may store information about the aggregated tasks, along with their required times, execution order, etc., as aggregated task information in the storage device 330.
  • the abstraction unit 210 may also store other conversion information between the first model and the initial model, such as conversion information related to the shape and information related to the time step length, in the storage device 330.
  • the abstraction unit 210 may simplify the shape of the workpiece to be worked on.
  • the workpieces may also be aggregated.
  • workpiece aggregation can be achieved by combining a group of workpieces whose initial positions and target positions are close to each other and whose initial postures and target postures are similar, in the same way as task aggregation.
  • the shape surrounding the multiple workpieces aggregated into one is defined as the workpiece shape after aggregation.
  • the first generation unit 220 generates a first operation plan using the created first model.
  • the motion plan generation unit 203 of the task motion planner 201 formulates an initial model and uses it as a constraint equation to solve a mixed integer linear problem and calculate a motion plan.
  • the function of the first generation unit 220 in this embodiment is basically the same as that of the motion plan generation unit 203.
  • a first model is formulated and used as a constraint equation to generate a first motion plan.
  • the plan generation unit 230 generates an operation plan from the first operation plan using an initial model.
  • the first operation plan is divided into multiple parts in the time direction, each of which is a partial plan. Then, for each partial plan, a detailed plan is recalculated using the initial model. Finally, the detailed plans are combined to generate an operation plan.
  • the dividing unit 231 divides the first operation plan in the time direction to generate partial plans. Furthermore, the second generation unit 232 generates a detailed plan for each partial plan using the initial model. Here, the second generation unit 232 generates, as a detailed plan, an operation plan for executing tasks included in the partial plan within the time range of the partial plan. The combining unit 233 combines the detailed plans to generate an operation plan.
  • task group a to f of the initial model are aggregated into two tasks (tasks A and B).
  • Task A is an aggregate of task groups a and b
  • task B is an aggregate of task groups c to f.
  • Figure 9(b) shows a first operation plan 710 generated using the first model that aggregates task groups a to f as described above.
  • first operation plan 710 task A is executed between ⁇ T and 2 ⁇ T, and task B is executed between 3 ⁇ T and 6 ⁇ T.
  • the dividing unit 231 divides this first operation plan 710 by a predetermined time interval ⁇ D to generate partial plans 720 (720a and 720b).
  • a predetermined time interval ⁇ D is set to 3 ⁇ T
  • the first operation plan 710 is divided into two partial plans 720.
  • the second generation unit 232 recalculates the motion plan using the initial model for each partial plan 720.
  • the second generation unit 232 generates a motion plan with an initial time step length ⁇ t to execute tasks a and b within the time range (0 to 3 ⁇ T) of partial plan 720a, resulting in detailed plan 730a.
  • the second generation unit 232 generates a motion plan with an initial time step length ⁇ t to execute tasks c to f within the time range (3 ⁇ T to 6 ⁇ T) of partial plan 720b, resulting in detailed plan 730b.
  • the second generation unit 232 generates these detailed plans 730a and 730b using information related to the conversion stored in the storage device 330. Note that unless a distinction is particularly required, they will be represented by detailed plan 730.
  • the combining unit 233 combines detailed plans 730a and 730b to generate operation plan 740.
  • the combining unit 233 maintains the execution order of the partial plans 720 that were the basis for generating each detailed plan 730.
  • detailed plan 730b is combined after detailed plan 730a.
  • the combining unit 233 sets the configuration so that the final state of the object in the previous detailed plan 730 becomes the initial state of the next detailed plan 730.
  • the state of the object is, for example, the position, posture, and whether or not it is attached to the hand.
  • the storage device 330 of this embodiment stores retained data S2, an initial model 331, creation rules 332, aggregated task information 333, and conversion information 334.
  • the creation rules 332 are rules that the abstraction unit 210 follows when creating a first model from the initial model 331.
  • the aggregated task information 333 is information on tasks aggregated by the abstraction unit 210.
  • the conversion information 334 is information on shapes and the like that are converted when creating the first model from the initial model 331.
  • retained data S2 includes abstract state specification information, constraint information, operating limit information, task information, abstract model information, map information, etc.
  • the abstract state specification information is information that specifies the abstract state that needs to be defined for each robot 600 in order to assign a task. For example, the state of an object according to the type and content of the task is displayed in the form of an abstract state.
  • Constraint information is information that indicates the constraints when executing a task. For example, it includes the positional relationship between the robot 600 and obstacles, the definition of the above-mentioned in-execution and unexecuted states, etc.
  • FIGS 10(a) to 10(c) Examples of constraint information are shown in Figures 10(a) to 10(c).
  • Figure 10(a) an example of constraints is shown for a case where the shapes of the hand, obstacle, and working area are represented by multiple two-dimensional rectangles.
  • a constraint equation either Equation 1 or Equation 2, shown in Figure 10(b) is set.
  • the constraint equation is set so that the object does not pass through the area corresponding to the obstacle.
  • constraints are defined between adjacent times (t and t+1) for all time steps.
  • the motion limit information is information about the motion limits of the robot 600. For example, the maximum speed and acceleration of the hand 601, requirements for obstacle avoidance, etc.
  • Abstract model information is information about a model that abstracts dynamics in the workspace. Dynamics here refers to items whose values can change in the model, such as the state of an object. A model that abstracts dynamics in the workspace is also called an abstract model. Abstract model information may be registered in association with information indicating the type or content of the task.
  • Map information is information that shows a map of the workspace. More specifically, it is information that shows the positions of objects within the workspace.
  • Fig. 11 shows the processing flow of the motion plan generation process of this embodiment.
  • the task motion planner 200 receives instructions from the user and starts the motion plan generation process.
  • the instructions from the user may be, for example, the setting (input) of a target state.
  • the initial model creation unit 240 creates an initial model using the above method (step S2101).
  • the abstraction unit 210 creates a first model using the above method (step S2102).
  • the abstraction unit 210 aggregates tasks and sets the first time step length to be longer than the initial time step length.
  • the shape of the object may be simplified compared to the initial model. Work may also be aggregated.
  • the first generation unit 220 generates a first operation plan 710 using the first model (step S2103).
  • the first operation plan 710 is generated as a task sequence and motion sequence that specifies the execution timing, execution order, execution allocation, hand position, posture, etc. of the aggregated tasks using the first time step length. Note that these sequences are generated for each cooperating robot 600.
  • the dividing unit 231 divides the first operation plan 710 by a predetermined time interval ⁇ D (step S2104).
  • the first operation plan 710 is divided into N pieces (N is an integer greater than or equal to 1) by the time interval ⁇ D.
  • each divided first operation plan 710 is called a partial plan 720, and in processing order, it is called the nth partial plan 720.
  • n is an integer greater than or equal to 1 and less than or equal to N.
  • the second generation unit 232 generates a detailed plan 730 for each partial plan 720 using the initial model. That is, for each n from 1 to N, the nth detailed plan 730 is repeatedly generated based on the nth partial plan 720 (steps S2105 to S2108).
  • the first operation plan 710 is divided in the time direction, and for each partial plan 720 after division, an initial model is used to generate a detailed operation plan 730.
  • an initial model is used to generate a detailed operation plan 730.
  • the dividing unit 231 divides the first operation plan 710 by a predetermined time interval ⁇ D.
  • the time interval for division is not limited to this.
  • the division may be performed according to the ratio of the execution times of the tasks before aggregation.
  • tasks with execution order constraints are divided so that they are in the same group. Specifically, as shown in Figure 12(b), if there is an execution order constraint such that task e must be executed after task d, the tasks are divided so that the two are not separated. As shown in Figure 12(b), the position of division line 721 is moved to 721c and 721d to divide the tasks.
  • Figures 13(a) and 13(b) show an example in which there are two robots 600.
  • task groups a and b in the initial model are designated as task A
  • task group c to f are designated as task B
  • task group g to i are designated as task C.
  • the task motion planner 200 creates a second model that is more abstract than the first model (aggregating tasks). Then, the second model is used to generate a second motion plan. Next, the second motion plan is divided into specified ranges, and a first detailed plan is generated for each divided range using the first model.
  • tasks 821 and 822 are aggregated in the second model. Then, the operation plan for the aggregated tasks is generated as a second operation plan. After that, operation plans for individual tasks 821 and 822 within the divided range are generated as first detailed plans. Then, each first detailed plan is divided into specified ranges, and for each divided range, a detailed plan 730 is generated using the initial model, and these are combined using the above method.
  • first detailed plans may be combined using the above method to generate the first operation plan 710.
  • subsequent processing is the same as in the above embodiments and variations.
  • control target of the robot system 100 is the robot 600
  • the control target is not limited to this.
  • the control target may be the operation of various types of transportation means such as an automobile or an airplane.
  • FIG. 14 is a diagram showing an example of the hardware configuration of the robot system 100.
  • the robot system 100 includes a CPU (Central Processing Unit) 191, a main storage device (memory) 192, an auxiliary storage device 193, and an interface (I/F) 194. These are connected to each other via a bus so as to be able to communicate with each other.
  • CPU Central Processing Unit
  • main storage device memory
  • I/F interface
  • the CPU 191 for example, loads programs stored in the auxiliary storage device 193 into the main storage device 192 and executes them to realize the above functions and to provide overall control of the entire device.
  • processors such as an MPU (Micro Processing Unit), may be used instead of the CPU 191.
  • the auxiliary storage device 193 may be, for example, a ROM (Read Only Memory), HDD (Hard Disk Drive), or SSD (Solid State Drive).
  • the auxiliary storage device 193 stores various programs executed by the device.
  • the auxiliary storage device 193 may also include storage media such as a flexible disk, hard disk, optical disk, CD-ROM, CD-R, magnetic tape, non-volatile memory card, or DVD.
  • the storage device 330 may be constructed on the auxiliary storage device 193, for example.
  • the programs stored in the auxiliary storage device 193 can be provided as program products recorded on non-transitory computer-readable recording media.
  • the auxiliary storage device 193 can be used for medium- to long-term storage of various programs recorded on non-transitory computer-readable recording media.
  • I/F 194 is an interface for electrically connecting the robot system 100 to other devices. Examples of other devices include the input device 310 and sensors. It may also be an external storage device. For example, it may be an interface compliant with USB (Universal Serial Bus) or SATA (Serial AT Attachment).
  • the robot system 100 may also be connected to a communication device via I/F 194. By connecting to a communication device, the robot system 100 can input and output signals and data via the communication device, either wired or wirelessly.
  • each device The above functions of each device are realized by the CPU 191 loading programs stored in the auxiliary storage device 193 into the main storage device 192 and executing them.
  • task motion planner 200 and robot controller 400 that make up the robot system 100 may each be realized by independent hardware.
  • the program that realizes each of the above functions can be recorded on a computer-readable storage medium.
  • the storage medium can be a non-transitory medium such as a semiconductor memory, hard disk, magnetic recording medium, or optical recording medium.
  • the present disclosure can also be embodied as a computer program product.
  • the robot system includes an abstraction unit that aggregates tasks from an initial model created by abstracting information including tasks to be executed by the robot and necessary for generating an operation plan for the robot, and creates a first model by aggregating the tasks; a first generation unit that generates a first operation plan using the first model; and a plan generating unit that generates an operation plan from the first operation plan using the initial model.
  • an abstraction unit that aggregates tasks from an initial model created by abstracting information including tasks to be executed by the robot and necessary for generating an operation plan for the robot, and creates a first model by aggregating the tasks; a first generation unit that generates a first operation plan using the first model; and a plan generating unit that generates an operation plan from the first operation plan using the initial model.
  • the robot system includes a division unit that divides the first operation plan to generate partial plans; a second generation unit that generates, for each of the partial plans, a detailed plan from the partial plan by using the initial model; It is preferable to provide a combining unit that combines the detailed plans to generate the operation plan.
  • Appendix 3 3.
  • the robot system according to claim 2 It is desirable that the division unit divides the first operation plan in the time direction to generate the partial plans.
  • (Appendix 4) 4 The robot system according to claim 3, It is desirable that the dividing unit divides the first operation plan in accordance with a ratio of the execution times of the tasks included in the first operation plan.
  • the initial model further includes an object model that abstracts a shape of an object in a workspace of the robot, and a time step length that is a time unit for generating the motion plan; It is desirable that the abstraction unit creates the first model by performing at least one of increasing the level of abstraction of the object model compared to the initial model and making the time step length longer than the time step length of the initial model. (Appendix 8) 8.
  • the device further comprises a robot controller that generates an instruction sequence for controlling the robot based on the motion plan.
  • the motion planning method executed by a computer mounted on a robot system includes: generating a first model by aggregating the tasks from an initial model created by abstracting information including tasks to be executed by the robot and necessary for generating an operation plan for the robot; generating a first motion plan using the first model; A motion plan is generated from the first motion plan using the initial model.
  • the program is a step of creating a first model by aggregating the tasks from an initial model created by abstracting information including tasks to be executed by the robot, the initial model being information necessary for generating an operation plan for the robot; generating a first motion plan using the first model; A procedure of generating an operation plan from the first operation plan using the initial model is executed. (Appendix 11) 9.
  • the robot further comprises an initial model creation unit that creates the initial model from pre-stored information necessary for generating an operation plan for the robot.
  • an initial model creation unit that creates the initial model from pre-stored information necessary for generating an operation plan for the robot.

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  • Robotics (AREA)
  • Mechanical Engineering (AREA)
  • Manipulator (AREA)
  • Numerical Control (AREA)

Abstract

La présente invention contribue à réduire le temps de calcul sur la totalité de la génération d'un plan de fonctionnement pour la commande de robot. Ce système robotisé comprend : une unité d'abstraction qui crée un premier modèle par agrégation de tâches à partir d'un modèle initial créé par abstraction d'informations nécessaires pour générer un plan de fonctionnement pour un robot, les informations comprenant une tâche à exécuter par le robot ; une première unité de génération pour générer un premier plan de fonctionnement à l'aide du premier modèle ; et une unité de génération de plan pour générer un plan de fonctionnement à l'aide du modèle initial à partir du premier plan de fonctionnement.
PCT/JP2024/012717 2024-03-28 2024-03-28 Système robotisé, procédé de planification de fonctionnement et programme Pending WO2025203452A1 (fr)

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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20070208442A1 (en) * 2006-02-27 2007-09-06 Perrone Paul J General purpose robotics operating system
US20220043455A1 (en) * 2020-08-07 2022-02-10 X Development Llc Preparing robotic operating environments for execution of robotic control plans
JP2022067006A (ja) * 2020-10-19 2022-05-02 オムロン株式会社 動作計画装置、動作計画方法、及び動作計画プログラム
KR20220067718A (ko) * 2020-11-18 2022-05-25 한국과학기술연구원 로봇의 자율적인 조작 서비스를 위한 소프트웨어 아키텍쳐 및 장치
WO2022107207A1 (fr) * 2020-11-17 2022-05-27 日本電気株式会社 Dispositif de collecte d'informations, procédé de collecte d'informations et support de stockage

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20070208442A1 (en) * 2006-02-27 2007-09-06 Perrone Paul J General purpose robotics operating system
US20220043455A1 (en) * 2020-08-07 2022-02-10 X Development Llc Preparing robotic operating environments for execution of robotic control plans
JP2022067006A (ja) * 2020-10-19 2022-05-02 オムロン株式会社 動作計画装置、動作計画方法、及び動作計画プログラム
WO2022107207A1 (fr) * 2020-11-17 2022-05-27 日本電気株式会社 Dispositif de collecte d'informations, procédé de collecte d'informations et support de stockage
KR20220067718A (ko) * 2020-11-18 2022-05-25 한국과학기술연구원 로봇의 자율적인 조작 서비스를 위한 소프트웨어 아키텍쳐 및 장치

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