Conceptio › Archive › Google Patents

Google Patentsopen access

Dynamic mass estimation methods for an integrated mobile manipulator robot — Boston Dynamics, Inc. (US20220305678A1)

Boston Dynamics, Inc. · Google Patents

Google Patents · Patents · License: Open Access

Open Source ↗

bostondynamics

patent, google patents, intellectual property, US20220305678A1, Boston Dynamics, Inc., Shervin Talebi, en, 2022

ABSTRACT

Abstract

A method of estimating one or more mass characteristics of a payload manipulated by a robot includes moving the payload using the robot, determining one or more accelerations of the payload while the payload is in motion, sensing, using one or more sensors of the robot, a wrench applied to the payload while the payload is in motion, and estimating the one or more mass characteristics of the payload based, at least in part, on the determined accelerations and the sensed wrench.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority under 35 U.S.C. Â§ 119(e) to U.S. Provisional Application Ser. No. 63/166,851, filed Mar. 26, 2021, titled, âDYNAMIC MASS ESTIMATION METHODS FOR AN INTEGRATED MOBILE MANIPULATOR ROBOT,â which is incorporated by reference in its entirety herein.

BACKGROUND

A robot is generally defined as a reprogrammable and multifunctional manipulator designed to move material, parts, tools, or specialized devices through variable programmed motions for a performance of tasks. Robots may be manipulators that are physically anchored (e.g., industrial robotic arms), mobile robots that move throughout an environment (e.g., using legs, wheels, or traction-based mechanisms), or some combination of a manipulator and a mobile robot. Robots are utilized in a variety of industries including, for example, manufacturing, warehouse logistics, transportation, hazardous environments, exploration, and healthcare.

SUMMARY

Some embodiments relate to a method of estimating one or more mass characteristics of a payload manipulated by a robot. The method comprises moving the payload using the robot, determining one or more accelerations of the payload while the payload is in motion, sensing (using one or more sensors of the robot) a wrench applied to the payload while the payload is in motion, and estimating the one or more mass characteristics of the payload based, at least in part, on the determined accelerations and the sensed wrench.

In one aspect, determining the one or more accelerations of the payload while the payload is in motion comprises determining the one or more accelerations of the payload while the payload is moved through an excitation routine, and sensing the wrench applied to the payload while the payload is in motion comprises sensing the wrench applied to the payload while the payload is moved through the excitation routine. In another aspect, determining the one or more accelerations of the payload comprises determining the one or more accelerations of the payload based, at least in part, on one or more motions of the robot. In another aspect, determining the one or more accelerations of the payload based, at least in part, on the one or more motions of the robot comprises determining one or more joint motions of a robotic arm of the robot, and determining one or more accelerations of the robotic arm based, at least in part, on the one or more determined joint motions and a kinematic model of the robotic arm. In another aspect, determining the one or more accelerations of the payload based, at least in part, on the one or more motions of the robot comprises determining the accelerations of the payload based, at least in part, on the one or more motions of a robotic arm of the robot and motion of a mobile base of the robot, wherein the robotic arm is operatively coupled to the mobile base.

In one aspect, sensing the wrench comprises sensing the wrench applied to the payload by an end effector of a robotic arm of the robot. In another aspect, sensing the wrench comprises sensing a wrench associated with a wrist of a robotic arm of the robot. In another aspect, sensing the wrench associated with the wrist of the robotic arm comprises sensing the wrench using a 6-axis force/torque sensor.

In one aspect, estimating the one or more mass characteristics of the payload comprises estimating one or more of a mass of the payload, a center of mass of the payload, and one or more moments of inertia of the payload. In another aspect, estimating the one or more mass characteristics of the payload comprises estimating at least ten mass characteristics, wherein the at least ten mass characteristics comprise one mass parameter, three center of mass parameters, and six moment of inertia parameters. In another aspect, estimating the one or more mass characteristics of the payload comprises estimating the one or more mass characteristics of the payload within a time period of less than 0.5 seconds. In another aspect, estimating the one or more mass characteristics of the payload comprises estimating the one or more mass characteristics of the payload based, at least in part, on one or more priors. In another aspect, estimating the one or more mass characteristics of the payload based, at least in part, on the one or more priors comprises estimating the one or more mass characteristics of the payload based, at least in part, on one or more physical dimensions of the payload.

Some embodiments relate to a method of planning a trajectory. The method comprises estimating one or more mass characteristics of a payload according to one or more of the techniques described herein, computing inverse dynamics of the payload based, at least in part, on the estimated one or more mass characteristics of the payload, and planning the trajectory based, at least in part, on the computed inverse dynamics. In one aspect, computing the inverse dynamics comprises computing one or more torques to be applied at one or more joints of a robotic arm of the robot. In another aspect, planning the trajectory comprises optimizing the trajectory based, at least in part, on the estimated one or more mass characteristics of the payload. In another aspect, optimizing the trajectory comprises optimizing one or more of a speed of the payload and an acceleration of the payload. In another aspect, optimizing the trajectory includes minimizing the applied wrench required to displace the payload.

Some embodiments relate to a robot comprising a robotic arm, one or more sensors, and a controller. The controller is configured to determine one or more accelerations of a payload manipulated by the robot while the payload is in motion, determine a wrench applied to the payload while the payload is in motion based on signals from the one or more sensors, and estimate one or more mass characteristics of the payload based, at least in part, on the determined accelerations and the determined wrench.

In one aspect, the controller is configured to determine the one or more accelerations of the payload while the payload is moved through an excitation routine, and the controller is configured to determine the wrench applied to the payload while the payload is moved through the excitation routine. In another aspect, the robot further comprises a mobile base, wherein the robotic arm is operatively coupled to the mobile base. In another aspect, the controller is further configured to determine one or more joint motions of the robotic arm, and determine one or more accelerations of the robotic arm based, at least in part, on the one or more determined joint motions and a kinematic model of the robotic arm. In another aspect, the robot further comprises an end effector operatively coupled to a distal portion of the robotic arm, wherein the controller is configured to determine a wrench applied to the payload by the end effector. In another aspect, the one or more sensors are configured to sense a wrench associated with a wrist of the robotic arm. In another aspect, the one or more sensors comprise a 6-axis force/torque sensor. In another aspect, the controller is configured to estimate one or more of a mass of the payload, a center of mass of the payload, and one or more moments of inertia of the payload. In another aspect, the controller is configured to estimate at least ten mass characteristics, wherein the at least ten mass characteristics comprise one mass parameter, three center of mass parameters, and six moment of inertia parameters.

Some embodiments relate to a method of manipulating an object using a robot. The method comprises planning a trajectory of the object, moving the object along the trajectory using the robot, estimating one or more mass characteristics of the object while the object is in motion along the trajectory, and modifying an operation of the robot based, at least in part, on the estimated one or more mass characteristics.

In one aspect, planning the trajectory of the object comprises planning a first trajectory of the object, and modifying the operation of the robot comprises planning a second trajectory of the object different from the first trajectory of the object. In another aspect, planning the second trajectory comprises planning the second trajectory based, at least in part, on inverse dynamics computed using the estimated one or more mass characteristics. In another aspect, planning the second trajectory comprises planning the second trajectory to limit a wrench applied to the object by the robot within a predetermined range. In another aspect, modifying the operation of the robot comprises adjusting a motion of a robotic arm of the robot. In another aspect, adjusting the motion of the robotic arm comprises adjusting one or more torques applied at one or more joints of the robotic arm. In another aspect, modifying the operation of the robot comprises adjusting a motion of a mobile base of the robot. In another aspect, modifying the operation of the robot comprises adjusting a motion of a robotic arm of the robot and adjusting a motion of a mobile base of the robot, wherein the robotic arm is operatively coupled to the mobile base. In another aspect, estimating the one or more mass characteristics of the object comprises estimating one or more of a mass of the object, a center of mass of the object, and one or more moments of inertia of the object. In another aspect, estimating the one or more mass characteristics of the object comprises estimating at least ten mass characteristics, wherein the at least ten mass characteristics comprise one mass parameter, three center of mass parameters, and six moment of inertia parameters.

It should be appreciated that the foregoing concepts, and additional concepts discussed below, may be arranged in any suitable combination, as the present disclosure is not limited in this respect. Further, other advantages and novel features of the present disclosure will become apparent from the following detailed description of various non-limiting embodiments when considered in conjunction with the accompanying figures.

BRIEF DESCRIPTION OF DRAWINGS

The accompanying drawings are not intended to be drawn to scale. In the drawings, each identical or nearly identical component that is illustrated in various figures may be represented by a like numeral. For purposes of clarity, not every component may be labeled in every drawing. In the drawings:

FIG. 1A is a perspective view of one embodiment of a robot;

FIG. 1B is another perspective view of the robot of FIG. 1A ;

FIG. 2A depicts robots performing tasks in a warehouse environment;

FIG. 2B depicts a robot unloading boxes from a truck;

FIG. 2C depicts a robot building a pallet in a warehouse aisle;

FIG. 3 is a perspective view of one embodiment of a robot;

FIG. 4A is a schematic view illustrating forces and torques acting on a payload;

FIG. 4B illustrates how mass properties of an object may be determined from information relating to the forces, torques, and accelerations of the object;

FIG. 5 depicts a flowchart of one embodiment of a method of estimating payload mass characteristics;

FIG. 6 depicts a flowchart of one embodiment of a method of planning a trajectory; and

FIG. 7 depicts a flowchart of one embodiment of a method of manipulating an object.

DETAILED DESCRIPTION

Robots are typically configured to perform various tasks in an environment in which they are placed. Generally, these tasks include interacting with objects and/or the elements of the environment. Notably, robots are becoming popular in warehouse and logistics operations. Before the introduction of robots to such spaces, many operations were performed manually. For example, a person might manually unload boxes from a truck onto one end of a conveyor belt, and a second person at the opposite end of the conveyor belt might organize those boxes onto a pallet. The pallet may then be picked up by a forklift operated by a third person, who might drive to a storage area of the warehouse and drop the pallet for a fourth person to remove the individual boxes from the pallet and place them on shelves in the storage area. More recently, robotic solutions have been developed to automate many of these functions. Such robots may either be specialist robots (i.e., designed to perform a single task, or a small number of closely related tasks) or generalist robots (i.e., designed to perform a wide variety of tasks). To date, both specialist and generalist warehouse robots have been associated with significant limitations, as explained below.

A specialist robot may be designed to perform a single task, such as unloading boxes from a truck onto a conveyor belt. While such specialized robots may be efficient at performing their designated task, they may be unable to perform other, tangentially related tasks in any capacity. As such, either a person or a separate robot (e.g., another specialist robot designed for a different task) may be needed to perform the next task(s) in the sequence. As such, a warehouse may need to invest in multiple specialized robots to perform a sequence of tasks, or may need to rely on a hybrid operation in which there are frequent robot-to-human or human-to-robot handoffs of objects.

CROSS-REFERENCE TO RELATED APPLICATIONS

BACKGROUND

SUMMARY

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1A is a perspective view of one embodiment of a robot;

FIG. 1B is another perspective view of the robot of FIG. 1A ;

FIG. 2A depicts robots performing tasks in a warehouse environment;

FIG. 2B depicts a robot unloading boxes from a truck;

FIG. 2C depicts a robot building a pallet in a warehouse aisle;

FIG. 3 is a perspective view of one embodiment of a robot;

FIG. 4A is a schematic view illustrating forces and torques acting on a payload;

FIG. 4B illustrates how mass properties of an object may be determined from information relating to the forces, torques, and accelerations of the object;

FIG. 5 depicts a flowchart of one embodiment of a method of estimating payload mass characteristics;

FIG. 6 depicts a flowchart of one embodiment of a method of planning a trajectory; and

FIG. 7 depicts a flowchart of one embodiment of a method of manipulating an object.

DETAILED DESCRIPTION

In contrast, a generalist robot may be designed to perform a wide variety of tasks, and may be able to take a box through a large portion of the box's life cycle from the truck to the shelf (e.g., unloading, palletizing, transporting, depalletizing, storing). While such generalist robots may perform a variety of tasks, they may be unable to perform individual tasks with high enough efficiency or accuracy to warrant introduction into a highly streamlined warehouse operation. For example, while mounting an off-the-shelf robotic manipulator onto an off-the-shelf mobile robot might yield a system that could, in theory, accomplish many warehouse tasks, such a loosely integrated system may be incapable of performing complex or dynamic motions that require coordination between the manipulator and the mobile base, resulting in a combined system that is inefficient and inflexible. Typical operation of such a system within a warehouse environment may include the mobile base and the manipulator operating sequentially and (partially or entirely) independently of each other. For example, the mobile base may first drive toward a stack of boxes with the manipulator powered down. Upon reaching the stack of boxes, the mobile base may come to a stop, and the manipulator may power up and begin manipulating the boxes as the base remains stationary. After the manipulation task is completed, the manipulator may again power down, and the mobile base may drive to another destination to perform the next task. As should be appreciated from the foregoing, the mobile base and the manipulator in such systems are effectively two separate robots that have been joined together; accordingly, a controller associated with the manipulator may not be configured to share information with, pass commands to, or receive commands from a separate controller associated with the mobile base. As such, such a poorly integrated mobile manipulator robot may be forced to operate both its manipulator and its base at suboptimal speeds or through suboptimal trajectories, as the two separate controllers struggle to work together. Additionally, while there are limitations that arise from a purely engineering perspective, there are additional limitations that must be imposed to comply with safety regulations. For instance, if a safety regulation requires that a mobile manipulator must be able to be completely shut down within a certain period of time when a human enters a region within a certain distance of the robot, a loosely integrated mobile manipulator robot may not be able to act sufficiently quickly to ensure that both the manipulator and the mobile base (individually and in aggregate) do not a pose a threat to the human. To ensure that such loosely integrated systems operate within required safety constraints, such systems are forced to operate at even slower speeds or to execute even more conservative trajectories than those limited speeds and trajectories as already imposed by the engineering problem. As such, the speed and efficiency of generalist robots performing tasks in warehouse environments to date have been limited.

In view of the above, the inventors have recognized and appreciated that a highly integrated mobile manipulator robot with system-level mechanical design and holistic control strategies between the manipulator and the mobile base may be associated with certain benefits in warehouse and/or logistics operations. Such an integrated mobile manipulator robot may be able to perform complex and/or dynamic motions that are unable to be achieved by conventional, loosely integrated mobile manipulator systems. As a result, this type of robot may be well suited to perform a variety of different tasks (e.g., within a warehouse environment) with speed, agility, and efficiency.

Furthermore, the inventors have recognized and appreciated that even more complex and/or dynamic motions may be achievable if certain mass properties of a payload (e.g., of an object manipulated by the robotic arm) can be estimated. Without wishing to be bound by theory, the speed at which a payload may be moved (and/or the degree to which a payload may be accelerated) by a robotic arm before the payload separates from the robotic arm may depend, at least in part, on the mass properties of the payload. Accordingly, a payload may be moved at greater speeds and/or accelerations if certain mass information of the payload is known. Robots that employ some conventional mass estimation methods require the robot to stop moving to keep the payload stationary while the mass properties of the payload are measured. The inventors have recognized that requiring the robot to stop moving while mass properties of a payload are measured increases the amount of time needed to move the payload compared to scenarios in which mass estimation is performed âon-the-flyâ without requiring the motion of the robot to be stopped. To this end, some embodiments relate to a âdynamicâ mass estimation technique in which mass properties of a payload are estimated as the payload is moved in an accelerated state. Accordingly, these dynamic mass estimation methods (and the associated path planning and trajectory optimization that are thereby enabled) may be associated with increased speed and/or efficiency for certain manipulation tasks, as described in greater detail below.

Example Robot Overview

In this section, an overview of some components of one embodiment of a highly integrated mobile manipulator robot configured to perform a variety of tasks is provided to explain the interactions and interdependencies of various subsystems of the robot. Each of the various subsystems, as well as control strategies for operating the subsystems, are described in further detail in the following sections.

FIGS. 1A and 1B are perspective views of one embodiment of a robot 100 . The robot 100 includes a mobile base 110 and a robotic arm 130 . The mobile base 110 includes an omnidirectional drive system that enables the mobile base to translate in any direction within a horizontal plane as well as rotate about a vertical axis perpendicular to the plane. Each wheel 112 of the mobile base 110 is independently steerable and independently drivable. The mobile base 110 additionally includes a number of distance sensors 116 that assist the robot 100 in safely moving about its environment. The robotic arm 130 is a 6 degree of freedom (6-DOF) robotic arm including three pitch joints and a 3-DOF wrist. An end effector 150 is disposed at the distal end of the robotic arm 130 . The robotic arm 130 is operatively coupled to the mobile base 110 via a turntable 120 , which is configured to rotate relative to the mobile base 110 . In addition to the robotic arm 130 , a perception mast 140 is also coupled to the turntable 120 , such that rotation of the turntable 120 relative to the mobile base 110 rotates both the robotic arm 130 and the perception mast 140 . The robotic arm 130 is kinematically constrained to avoid collision with the perception mast 140 . The perception mast 140 is additionally configured to rotate relative to the turntable 120 , and includes a number of perception modules 142 configured to gather information about one or more objects in the robot's environment. The integrated structure and system-level design of the robot 100 enable fast and efficient operation in a number of different applications, some of which are provided below as examples.

FIG. 2A depicts

robots

10 a , 10 b , and 10 c performing different tasks within a warehouse environment. A first robot 10 a is inside a truck (or a container), moving boxes 11 from a stack within the truck onto a conveyor belt 12 (this particular task will be discussed in greater detail below in reference to FIG. 2B ). At the opposite end of the conveyor belt 12 , a second robot 10 b organizes the boxes 11 onto a pallet 13 . In a separate area of the warehouse, a third robot 10 c picks boxes from shelving to build an order on a pallet (this particular task will be discussed in greater detail below in reference to FIG. 2C ). It should be appreciated that the

robots

10 a , 10 b , and 10 c are different instances of the same robot (or of highly similar robots). Accordingly, the robots described herein may be understood as specialized multi-purpose robots, in that they are designed to perform specific tasks accurately and efficiently, but are not limited to only one or a small number of specific tasks.

FIG. 2B depicts a robot 20 a unloading boxes 21 from a truck 29 and placing them on a conveyor belt 22 . In this box picking application (as well as in other box picking applications), the robot 20 a will repetitiously pick a box, rotate, place the box, and rotate back to pick the next box. Although robot 20 a of FIG. 2B is a different embodiment from robot 100 of FIGS. 1A and 1B , referring to the components of robot 100 identified in FIGS. 1A and 1B will ease explanation of the operation of the robot 20 a in FIG. 2B . During operation, the perception mast of robot 20 a (analogous to the perception mast 140 of robot 100 of FIGS. 1A and 1B ) may be configured to rotate independent of rotation of the turntable (analogous to the turntable 120 ) on which it is mounted to enable the perception modules (akin to perception modules 142 ) mounted on the perception mast to capture images of the environment that enable the robot 20 a to plan its next movement while simultaneously executing a current movement. For example, while the robot 20 a is picking a first box from the stack of boxes in the truck 29 , the perception modules on the perception mast may point at and gather information about the location where the first box is to be placed (e.g., the conveyor belt 22 ). Then, after the turntable rotates and while the robot 20 a is placing the first box on the conveyor belt, the perception mast may rotate (relative to the turntable) such that the perception modules on the perception mast point at the stack of boxes and gather information about the stack of boxes, which is used to determine the second box to be picked. As the turntable rotates back to allow the robot to pick the second box, the perception mast may gather updated information about the area surrounding the conveyor belt. In this way, the robot 20 a may parallelize tasks which may otherwise have been performed sequentially, thus enabling faster and more efficient operation.

Also of note in FIG. 2B is that the robot 20 a is working alongside humans (e.g., workers

27 a and 27 b ). Given that the robot 20 a is configured to perform many tasks that have traditionally been performed by humans, the robot 20 a is designed to have a small footprint, both to enable access to areas designed to be accessed by humans, and to minimize the size of a safety zone around the robot into which humans are prevented from entering.

FIG. 2C depicts a robot 30 a performing an order building task, in which the robot 30 a places boxes 31 onto a pallet 33 . In FIG. 2C , the pallet 33 is disposed on top of an autonomous mobile robot (AMR) 34 , but it should be appreciated that the capabilities of the robot 30 a described in this example apply to building pallets not associated with an AMR. In this task, the robot 30 a picks boxes 31 disposed above, below, or within shelving 35 of the warehouse and places the boxes on the pallet 33 . Certain box positions and orientations relative to the shelving may suggest different box picking strategies. For example, a box located on a low shelf may simply be picked by the robot by grasping a top surface of the box with the end effector of the robotic arm (thereby executing a âtop pickâ). However, if the box to be picked is on top of a stack of boxes, and there is limited clearance between the top of the box and the bottom of a horizontal divider of the shelving, the robot may opt to pick the box by grasping a side surface (thereby executing a âface pickâ).

To pick some boxes within a constrained environment, the robot may need to carefully adjust the orientation of its arm to avoid contacting other boxes or the surrounding shelving. For example, in a typical âkeyhole problemâ, the robot may only be able to access a target box by navigating its arm through a small space or confined area (akin to a keyhole) defined by other boxes or the surrounding shelving. In such scenarios, coordination between the mobile base and the arm of the robot may be beneficial. For instance, being able to translate the base in any direction allows the robot to position itself as close as possible to the shelving, effectively extending the length of its arm (compared to conventional robots without omnidirectional drive which may be unable to navigate arbitrarily close to the shelving). Additionally, being able to translate the base backwards allows the robot to withdraw its arm from the shelving after picking the box without having to adjust joint angles (or minimizing the degree to which joint angles are adjusted), thereby enabling a simple solution to many keyhole problems.

Of course, it should be appreciated that the tasks depicted in FIGS. 2A-2C are but a few examples of applications in which an integrated mobile manipulator robot may be used, and the present disclosure is not limited to robots configured to perform only these specific tasks. For example, the robots described herein may be suited to perform tasks including, but not limited to, removing objects from a truck or container, placing objects on a conveyor belt, removing objects from a conveyor belt, organizing objects into a stack, organizing objects on a pallet, placing objects on a shelf, organizing objects on a shelf, removing objects from a shelf, picking objects from the top (e.g., performing a âtop pickâ), picking objects from a side (e.g., performing a âface pickâ), coordinating with other mobile manipulator robots, coordinating with other warehouse robots (e.g., coordinating with AMRs), coordinating with humans, and many other tasks.

Example Robotic Arm

FIG. 3 is a perspective view of a robot 400 designed in accordance with some embodiments. The robot 400 includes a mobile base 410 and a turntable 420 rotatably coupled to the mobile base. A robotic arm 430 is operatively coupled to the turntable 420 , as is a perception mast 440 . The perception mast 440 includes an actuator 444 configured to enable rotation of the perception mast 440 relative to the turntable 420 and/or the mobile base 410 , so that a direction of the perception modules 442 of the perception mast may be independently controlled.

The robotic arm 430 of FIG. 3 is a 6-DOF robotic arm. When considered in conjunction with the turntable 420 (which is configured to yaw relative to the mobile base about a vertical axis parallel to the Z axis), the arm/turntable system may be considered a 7-DOF system. The 6-DOF robotic arm 430 includes three

pitch joints

432 , 434 , and 436 , and a 3- DOF wrist 438 which, in some embodiments, may be a spherical 3-DOF wrist. Starting at the turntable 420 , the robotic arm 430 includes a turntable offset 422 which is fixed relative to the turntable 420 . A distal portion of the turntable offset 422 is rotatably coupled to a proximal portion of a first link 433 at a first joint 432 . A distal portion of the first link 433 is rotatably coupled to a proximal portion of a second link 435 at a second joint 434 . A distal portion of the second link 435 is rotatably coupled to a proximal portion of a third link 437 at a third joint 436 . The first, second, and

third joints

432 , 434 , and 436 are associated with first, second, and

third axes

432 a , 434 a , and 436 a , respectively. The first, second, and

third joints

432 , 434 , and 436 are additionally associated with first, second, and third actuators (not labeled) which are configured to rotate a link about an axis. Generally, the nth actuator is configured to rotate the nth link about the nth axis associated with the nth joint. Specifically, the first actuator is configured to rotate the first link 433 about the first axis 432 a associated with the first joint 432 , the second actuator is configured to rotate the second link 435 about the second axis 434 a associated with the second joint 434 , and the third actuator is configured to rotate the third link 437 about the third axis 436 a associated with the third joint 436 . In the embodiment shown in FIG. 3 , the first, second, and

third axes

432 a , 434 a , and 436 a are parallel (and, in this case, are all parallel to the X axis). In the embodiment shown in FIG. 3 , the first, second, and

third joints

432 , 434 , and 436 are all pitch joints.

In some embodiments, a robotic arm of a highly integrated mobile manipulator robot may include a different number of degrees of freedom than the robotic arms discussed above. Additionally, a robotic arm need not be limited to a robotic arm with three pitch joints and a 3-DOF wrist. It should be appreciated that a robotic arm of a highly integrated mobile manipulator robot may include any suitable number of joints of any suitable type, whether revolute or prismatic. Revolute joints need not be oriented as pitch joints, but rather may be pitch, roll, yaw, or any other suitable type of joint.

Returning to FIG. 3 , the robotic arm 430 includes a wrist 438 . As noted above, the wrist 438 is a 3-DOF wrist, and in some embodiments may be a spherical 3-DOF wrist. The wrist 438 is coupled to a distal portion of the third link 437 . The wrist 438 includes three actuators configured to rotate an end effector 450 coupled to a distal portion of the wrist 438 about three mutually perpendicular axes. Specifically, the wrist may include a first wrist actuator configured to rotate the end effector relative to a distal link of the arm (e.g., the third link 437 ) about a first wrist axis, a second wrist actuator configured to rotate the end effector relative to the distal link about a second wrist axis, and a third wrist actuator configured to rotate the end effector relative to the distal link about a third wrist axis. The first, second, and third wrist axes may be mutually perpendicular. In embodiments in which the wrist is a spherical wrist, the first, second, and third wrist axes may intersect.

In the embodiment of FIG. 3 , the end effector 450 is a vacuum-based end effector. In embodiments in which the end effector is a vacuum-based end effector, the end effector may include multiple vacuum assemblies that attach to an object by applying a suction force through a suction cup. The vacuum assemblies may be individually addressable, such that a controller may adjust a level of suction of each vacuum assembly independently. For example, each vacuum assembly may include a sensor (such as a pressure sensor) to determine a grip quality between the vacuum assembly and the object being grasped. If it is determined that some vacuum assemblies are insufficiently attached to the object (e.g., due to a poor suction cup seal), those vacuum assemblies may be turned off such that the total vacuum pressure of the end effector may be distributed among only the vacuum assemblies with a good seal, reducing the amount of vacuum pressure that is wasted.

In some embodiments, an end effector may be associated with one or more sensors. For example, a force/torque sensor may measure forces and/or torques (e.g., wrenches) applied to the end effector. Alternatively or additionally, a sensor may measure wrenches applied to a wrist of the robotic arm by the end effector (and, for example, an object grasped by the end effector) as the object is manipulated. Signals from these (or other) sensors may be used during mass estimation and/or path planning operations, as will be explained below. In some embodiments, sensors associated with an end effector may include an integrated force/torque sensor, such as a 6-axis force/torque sensor. In some embodiments, separate sensors (e.g., separate force and torque sensors) may be employed. Some embodiments may include only force sensors (e.g., uniaxial force sensors, or multi-axis force sensors), and some embodiments may include only torque sensors. In some embodiments, an end effector may be associated with a custom sensing arrangement. For example, one or more sensors (e.g., one or more uniaxial sensors) may be arranged to enable sensing of forces and/or torques along multiple axes. An end effector (or another portion of the robotic arm) may additionally include any appropriate number or configuration of cameras, distance sensors, pressure sensors, light sensors, or any other suitable sensors, whether related to sensing characteristics of the payload or otherwise, as the disclosure is not limited in this regard.

As noted briefly above, the inventors have recognized and appreciated that knowledge of certain mass propert

CLAIMS

Claims ( 27 )

1 . A method of estimating one or more mass characteristics of a payload manipulated by a robot, the method comprising:

moving the payload using the robot; determining one or more accelerations of the payload while the payload is in motion; sensing, using one or more sensors of the robot, a wrench applied to the payload while the payload is in motion; and estimating the one or more mass characteristics of the payload based, at least in part, on the determined accelerations and the sensed wrench.

2 . The method of claim 1 , wherein:

determining the one or more accelerations of the payload while the payload is in motion comprises determining the one or more accelerations of the payload while the payload is moved through an excitation routine; and sensing the wrench applied to the payload while the payload is in motion comprises sensing the wrench applied to the payload while the payload is moved through the excitation routine.

3 . The method of claim 1 , wherein determining the one or more accelerations of the payload comprises determining the one or more accelerations of the payload based, at least in part, on one or more motions of the robot.

4 . The method of claim 3 , wherein determining the one or more accelerations of the payload based, at least in part, on the one or more motions of the robot comprises:

determining one or more joint motions of a robotic arm of the robot; and determining one or more accelerations of the robotic arm based, at least in part, on the one or more determined joint motions and a kinematic model of the robotic arm.

5 . The method of claim 3 , wherein determining the one or more accelerations of the payload based, at least in part, on the one or more motions of the robot comprises determining the accelerations of the payload based, at least in part, on the one or more motions of a robotic arm of the robot and motion of a mobile base of the robot, wherein the robotic arm is operatively coupled to the mobile base.

6 . The method of claim 1 , wherein sensing the wrench comprises sensing the wrench applied to the payload by an end effector of a robotic arm of the robot.

7 . The method of claim 1 , wherein sensing the wrench comprises sensing a wrench associated with a wrist of a robotic arm of the robot.

8 . The method of claim 7 , wherein sensing the wrench associated with the wrist of the robotic arm comprises sensing the wrench using a 6-axis force/torque sensor.

9 . The method of claim 1 , wherein estimating the one or more mass characteristics of the payload comprises estimating one or more of a mass of the payload, a center of mass of the payload, and one or more moments of inertia of the payload.

10 . The method of claim 1 , wherein estimating the one or more mass characteristics of the payload comprises estimating at least ten mass characteristics, wherein the at least ten mass characteristics comprise one mass parameter, three center of mass parameters, and six moment of inertia parameters.

11 . The method of claim 1 , wherein estimating the one or more mass characteristics of the payload comprises estimating the one or more mass characteristics of the payload within a time period of less than 0.5 seconds.

12 . The method of claim 1 , wherein estimating the one or more mass characteristics of the payload comprises estimating the one or more mass characteristics of the payload based, at least in part, on one or more priors.

13 . The method of claim 12 , wherein estimating the one or more mass characteristics of the payload based, at least in part, on the one or more priors comprises estimating the one or more mass characteristics of the payload based, at least in part, on one or more physical dimensions of the payload.

14 . A method of planning a trajectory, the method comprising:

estimating one or more mass characteristics of a payload according to the method of claim 1 ; computing inverse dynamics of the payload based, at least in part, on the estimated one or more mass characteristics of the payload; and planning the trajectory based, at least in part, on the computed inverse dynamics.

15 . The method of claim 14 , wherein computing the inverse dynamics comprises computing one or more torques to be applied at one or more joints of a robotic arm of the robot.

16 . The method of claim 14 , wherein planning the trajectory comprises optimizing the trajectory based, at least in part, on the estimated one or more mass characteristics of the payload.

17 . The method of claim 16 , wherein optimizing the trajectory comprises optimizing one or more of a speed of the payload and an acceleration of the payload.

18 . The method of claim 16 , wherein optimizing the trajectory includes minimizing the applied wrench required to displace the payload.

19 . A robot comprising:

a robotic arm; one or more sensors; and a controller configured to:

determine one or more accelerations of a payload manipulated by the robot while the payload is in motion;

determine a wrench applied to the payload while the payload is in motion based on signals from the one or more sensors; and

estimate one or more mass characteristics of the payload based, at least in part, on the determined accelerations and the determined wrench.

20 . The robot of claim 19 , wherein:

the controller is configured to determine the one or more accelerations of the payload while the payload is moved through an excitation routine; and the controller is configured to determine the wrench applied to the payload while the payload is moved through the excitation routine.

21 . The robot of claim 19 , further comprising a mobile base, wherein the robotic arm is operatively coupled to the mobile base.

22 . The robot of claim 19 , wherein the controller is further configured to:

determine one or more joint motions of the robotic arm; and determine one or more accelerations of the robotic arm based, at least in part, on the one or more determined joint motions and a kinematic model of the robotic arm.

23 . The robot of claim 19 , further comprising an end effector operatively coupled to a distal portion of the robotic arm, wherein the controller is configured to determine a wrench applied to the payload by the end effector.

24 . The robot of claim 19 , wherein the one or more sensors are configured to sense a wrench associated with a wrist of the robotic arm.

25 . The robot of claim 19 , wherein the one or more sensors comprise a 6-axis force/torque sensor.

26 . The robot of claim 19 , wherein the controller is configured to estimate one or more of a mass of the payload, a center of mass of the payload, and one or more moments of inertia of the payload.

27 . The robot of claim 19 , wherein the controller is configured to estimate at least ten mass characteristics, wherein the at least ten mass characteristics comprise one mass parameter, three center of mass parameters, and six moment of inertia parameters.

US17/699,523

2021-03-26

2022-03-21

Dynamic mass estimation methods for an integrated mobile manipulator robot

Active

2043-01-21

US12240105B2

( en )

Priority Applications (2)

Application Number

Priority Date

Filing Date

Title

US17/699,523

US12240105B2

( en )

2021-03-26

2022-03-21

Dynamic mass estimation methods for an integrated mobile manipulator robot

US19/040,953

US20250170731A1

( en )

2021-03-26

2025-01-30

Dynamic mass estimation methods for an integrated mobile manipulator robot

Applications Claiming Priority (2)

Application Number

Priority Date

Filing Date

Title

US202163166851P

2021-03-26

US17/699,523

US12240105B2

( en )

2021-03-26

2022-03-21

Dynamic mass estimation methods for an integrated mobile manipulator robot

Related Child Applications (1)

Application Number

Title

Priority Date

Filing Date

US19/040,953

Continuation

US20250170731A1

( en )

2021-03-26

2025-01-30

Dynamic mass estimation methods for an integrated mobile manipulator robot

Publications (2)

Publication Number

Publication Date

US20220305678A1

true

US20220305678A1 ( en )

2022-09-29

US12240105B2

US12240105B2 ( en )

2025-03-04

Family

ID=81326775

Family Applications (2)

Application Number

Title

Priority Date

Filing Date

US17/699,523

Active

2043-01-21

US12240105B2

( en )

2021-03-26

2022-03-21

Dynamic mass estimation methods for an integrated mobile manipulator robot

US19/040,953

Pending

US20250170731A1

( en )

2021-03-26

2025-01-30

Dynamic mass estimation methods for an integrated mobile manipulator robot

Family Applications After (1)

Application Number

Title

Priority Date

Filing Date

US19/040,953

Pending

US20250170731A1

( en )

2021-03-26

2025-01-30

Dynamic mass estimation methods for an integrated mobile manipulator robot

Country Status (7)

Country

Link

( 2 )

US12240105B2

( en )

( 1 )

EP4313504A1

( en )

( 1 )

KR20230162961A

( en )

( 1 )

CN117460601A

( en )

( 1 )

AU2022241986B2

( en )

( 1 )

CA3214609A1

( en )

( 1 )

WO2022203980A1

( en )

Cited By (13)

* Cited by examiner, â Cited by third party

Publication number

Priority date

Publication date

Assignee

Title

US20210239512A1

( en )

2018-06-07

2021-08-05

Antolini Bilance S.R.L.

Method for handling and checking products in general or raw material in blocks or slabs

CN115771753A

( en )

2022-12-15

2023-03-10

æ¢å¡æ¼å¾·ï¼åäº¬ï¼æºå¨äººç§ææéå¬å¸

Palletizing power failure processing method, equipment and storage medium

US20230321825A1

( en )

2022-03-25

2023-10-12

Dexterity, Inc.

Detection of heavy objects in robotic induction

USD1013003S1

( en )

2021-03-26

2024-01-30

Boston Dynamics, Inc.

Robotic device

USD1013001S1

( en )

2022-08-10

2024-01-30

Boston Dynamics, Inc.

Robotic device

USD1013004S1

( en )

2021-03-26

2024-01-30

Boston Dynamics, Inc.

Robotic device

USD1018621S1

( en )

2022-08-10

2024-03-19

Boston Dynamics, Inc.

Robotic device

USD1033501S1

( en )

2022-08-10

2024-07-02

Boston Dynamics, Inc.

Robotic device

USD1034728S1

( en )

2022-08-10

2024-07-09

Boston Dynamics, Inc.

Robotic device

USD1034729S1

( en )

2022-08-10

2024-07-09

Boston Dynamics, Inc.

Robotic device

EP4497552A1

( en )

2023-07-25

2025-01-29

Hand Held Products, Inc.

Robotic system and method of controlling thereof

US12440980B1

( en )

2023-02-28

2025-10-14

Agility Robotics, Inc.

Robotic manipulation with bimanual and nonprehensile aspects and related technology

US12632028B2

( en )

2021-04-23

2026-05-19

Fanuc Corporation

Numerical control device and numerical control system

Families Citing this family (6)

* Cited by examiner, â Cited by third party

Publication number

Priority date

Publication date

Assignee

Title

JP2023142080A

( en )

2022-03-24

2023-10-05

ã¢ã¤ã·ã³ã·ããæ ªå¼ä¼ç¤¾

Object detection method and detection device

US12306033B2

( en )

2022-04-15

2025-05-20

Invia Robotics, Inc.

Systems and methods for deriving weight and quantity based on activations of robot actuators used for object retrieval

WO2024114921A1

( en )

2022-12-02

2024-06-06

Abb Schweiz Ag

Method and system for load-aware optimization of a trajectory for an industrial robot

TWI881710B

( en )

2024-02-07

2025-04-21

å°éé»åå·¥æ¥è¡ä»½æéå¬å¸

Method of payload estimation and robot arm system and electronic device using the same

WO2026009958A1

( en )

2024-07-03

2026-01-08

ããã½ããã¯ï¼©ï½ããã¸ã¡ã³ãæ ªå¼ä¼ç¤¾

Picking method and picking control device

WO2026052191A1

( en )

2024-09-06

2026-03-12

Universal Robots A/S

Manipulation of a workpiece using a robotic arm

Citations (12)

* Cited by examiner, â Cited by third party

Publication number

Priority date

Publication date

Assignee

Title

US20070142823A1

( en )

2005-12-20

2007-06-21

Intuitive Surgical Inc.

Control system for reducing internally generated frictional and inertial resistance to manual positioning of a surgical manipulator

US20140230581A1

( en )

2011-09-20

2014-08-21

Ishida Co., Ltd.

Mass measurement device

US9242380B2

( en )

2013-02-25

2016-01-26

Panasonic Intellectual Property Management Co., Ltd.

Robot, robot control apparatus, robot control method, and robot control program

RU2575184C1

( en )

2014-10-30

2016-02-20

Method to determine body inertia tensor

US20160178430A1

( en )

2014-12-17

2016-06-23

Norgren Automation Solutions, Llc

Apparatus and method for detecting multiple workpieces

US20180370021A1

( en )

2017-06-27

2018-12-27

Massachusetts Institute Of Technology

Wearable robotic systems for supporting a load

US20190321977A1

( en )

2018-04-23

2019-10-24

General Electric Company

Architecture and methods for robotic mobile manipluation system

US20200094401A1

( en )

2018-09-21

2020-03-26

Beijing Jingdong Shangke Information Technology Co., Ltd.

System and method for automatic learning of product manipulation

US20200130961A1

( en )

2018-10-30

2020-04-30

Mujin, Inc.

Robotic system with automated package registration mechanism and methods of operating the same

US20200306964A1

( en )

2019-03-27

2020-10-01

Boston Dynamics, Inc.

Palletizing Boxes

US20210291371A1

( en )

2020-03-18

2021-09-23

Kabushiki Kaisha Yaskawa Denki

Robot control apparatus, robot control system, and robot control method

US20220371195A1

( en )

2019-11-19

2022-11-24

Google Llc

Optimization of Motion Paths of a Robot Using Vision Data

Family Cites Families (50)

* Cited by examiner, â Cited by third party

Publication number

Priority date

Publication date

Assignee

Title

US4789295A

( en )

1985-02-26

1988-12-06

International Business Machines Corp.

Article manipulator for robot

DE19719748C2

( en )

1997-05-09

2001-03-29

Fraunhofer Ges Forschung

Device for handling piece goods, in particular parcels, for loading and unloading a loading space, and method for unloading a loading space

DE10018385A1

( en )

2000-04-13

2001-10-18

Abb Patent Gmbh

Method and device for the automatic loading and unloading of piece goods

US7684896B2

( en )

2001-06-29

2010-03-23

Honda Motor Co., Ltd.

System and method of estimating joint loads using an approach of closed form dynamics

NZ533533A

( en )

2004-06-11

2006-11-30

Avalon Engineering Ltd

Deployment method and apparatus for moving stacked articles in varying orientations

US7153085B2

( en )

2004-12-22

2006-12-26

Aidco International, Inc.

Multi-modal package handling tool and system

US20060182607A1

( en )

2005-01-18

2006-08-17

Clark Jason A

Method and apparatus for depalletizing bagged products

AT507339B1

( en )

2008-09-26

2011-06-15

Stiwa Holding Gmbh

METHOD AND APPARATUS FOR RECORDING AND / OR PROCESSING OBJECTS

US9875911B2

( en )

2009-09-23

2018-01-23

STATS ChipPAC Pte. Ltd.

Semiconductor device and method of forming interposer with opening to contain semiconductor die

US9004846B2

( en )

2011-02-11

2015-04-14

The Procter & Gamble Company

Gripper assembly for moving device

US8965563B2

( en )

2011-04-04

2015-02-24

Palo Alto Research Incorporated

High throughput parcel handling

US20130017053A1

( en )

2011-07-11

2013-01-17

Axium, Inc.

Depalletizing Tool

US9451810B2

( en )

2011-11-18

2016-09-27

Nike, Inc.

Automated identification of shoe parts

US9481530B2

( en )

2011-12-20

2016-11-01

Ludwig BrandmÃ¼ller

Depalletizing system and removal unit

US10216865B1

( en )

2012-03-06

2019-02-26

Vecna Robotics, Inc.

Monitoring one or more articles on a support surface

JP5975685B2

( en )

2012-03-09

2016-08-23

ãã¤ãã³æ ªå¼ä¼ç¤¾

Information processing apparatus and information processing method

FR2995596B1

( en )

2012-09-17

2014-09-05

Materials Technologies

PALLET PROCESS AND DEVICE FOR IMPLEMENTING THE METHOD

JP6273084B2

( en )

2012-09-20

2018-01-31

æ ªå¼ä¼ç¤¾å®å·é»æ©

Robot system and workpiece transfer method

JP5642759B2

( en )

2012-10-31

2014-12-17

ãã¡ããã¯æ ªå¼ä¼ç¤¾

Article take-out device and article take-out method

FR3000915B1

( en )

2013-01-17

2015-03-13

Amg Ind

GRIPPING DEVICE AND ASSOCIATED METHOD

WO2014113762A1

( en )

2013-01-20

2014-07-24

Wynright Corporation

Automated truck unloader for unloading/unpacking product from trailers and containers

US9393686B1

( en )

2013-03-15

2016-07-19

Industrial Perception, Inc.

Moveable apparatuses having robotic manipulators and conveyors to facilitate object movement

CN105473474B

( en )

2013-05-17

2018-01-23

å ç¹å©æ ¼å°ç¹æ»é¨æéè´£ä»»å¬å¸

robot carton unloader

US9487361B2

( en )

2013-05-17

2016-11-08

Intelligrated Headquarters Llc

Robotic carton unloader

US9315345B2

( en )

2013-08-28

2016-04-19

Intelligrated Headquarters Llc

Robotic carton unloader

US9785911B2

( en )

2013-07-25

2017-10-10

I AM Robotics, LLC

System and method for piece-picking or put-away with a mobile manipulation robot

US9493316B2

( en )

2013-07-30

2016-11-15

Intelligrated Headquarters Llc

Robotic carton unloader

US10005627B2

( en )

2013-08-28

2018-06-26

Intelligrated Headquarters, Llc

Carton unloader with self-aligning interface

JP5945968B2

( en )

2013-09-03

2016-07-05

æ ªå¼ä¼ç¤¾å®å·é»æ©

Robot hand, robot system, and article depalletizing method

CA2923474C

( en )

2013-09-09

2022-06-21

Dematic Corp.

Autonomous mobile picking

SG2013069893A

( en )

2013-09-13

2015-04-29

Jcs Echigo Pte Ltd

Material handling system and method

JP5897532B2

( en )

2013-11-05

2016-03-30

ãã¡ããã¯æ ªå¼ä¼ç¤¾

Apparatus and method for picking up articles placed in three-dimensional space by robot

CA3114789C

( en )

2014-01-22

2021-12-07

Symbotic Canada Ulc

Vision-assisted robotized depalletizer

US9623569B2

( en )

2014-03-31

2017-04-18

Intelligrated Headquarters, Llc

Autonomous truck loader and unloader

CA2887261C

( en )

2014-04-11

2020-09-22

Axium Inc.

Vision-assisted system and method for picking of rubber bales in a bin

CA2951151A1

( en )

2014-06-04

2015-12-10

Intelligrated Headquarters Llc

Truck unloader visualization

US9272417B2

( en )

2014-07-16

2016-03-01

Google Inc.

Real-time determination of object metrics for trajectory planning

EP3186175B1

( en )

2014-08-27

2021-05-26

Intelligrated Headquarters LLC

Robotic carton unloader

US9688489B1

( en )

2015-03-30

2017-06-27

X Development Llc

Modular dock for facilities integration

US10124489B2

( en )

2016-02-26

2018-11-13

Kinema Systems Inc.

Locating, separating, and picking boxes with a sensor-guided robot

US10071856B2

( en )

2016-07-28

2018-09-11

X Development Llc

Inventory management

US10122995B2

( en )

2016-09-22

2018-11-06

X Development Llc

Systems and methods for generating and displaying a 3D model of items in a warehouse

US10369702B2

( en )

2016-10-17

2019-08-06

Raytheon Company

Automated work piece moment of inertia (MOI) identification system and method for same

CN106647282B

( en )

2017-01-19

2020-01-03

åäº¬å·¥ä¸å¤§å¦

Six-degree-of-freedom robot trajectory planning method considering tail end motion error

CN107199569B

( en )

2017-06-22

2020-01-21

åä¸ç§æå¤§å¦

A joint robot trajectory planning method based on the balanced distribution of joint energy

JP7051045B2

( en )

2017-11-08

2022-04-11

ãªã ãã³æ ªå¼ä¼ç¤¾

Mobile manipulators, control methods and programs for mobile manipulators

US10766149B2

( en )

2018-03-23

2020-09-08

Amazon Technologies, Inc.

Optimization-based spring lattice deformation model for soft materials

CN112368116B

( en )

2018-06-15

2025-03-21

ä¼å²æºå¨äººå¬å¸

Estimation of the payload attached to the robot arm

US11027435B2

( en )

2018-12-04

2021-06-08

Raytheon Company

Automated work piece testing system and method for same

EP3736090A1

( en )

2019-05-10

2020-11-11

Franka Emika GmbH

Joint velocity and joint acceleration estimation for robots

2022

2022-03-21

AU2022241986A

patent/AU2022241986B2/en

active

Active

2022-03-21

KR1020237036823A

patent/KR20230162961A/en

active

Pending

2022-03-21

US17/699,523

patent/US12240105B2/en

active

Active

2022-03-21

CA3214609A

patent/CA3214609A1/en

active

Pending

2022-03-21

PCT/US2022/021076

patent/WO2022203980A1/en

not_active

Ceased

2022-03-21

EP22715843.3A

patent/EP4313504A1/en

active

Pending

2022-03-21

CN202280035950.0A

patent/CN117460601A/en

active

Pending

2025

2025-01-30

US19/040,953

patent/US20250170731A1/en

active

Pending

Patent Citations (12)

* Cited by examiner, â Cited by third party

Publication number

Priority date

Publication date

Assignee

Title

US20070142823A1

( en )

2005-12-20

2007-06-21

Intuitive Surgical Inc.

Control system for reducing internally generated frictional and inertial resistance to manual positioning of a surgical manipulator

US20140230581A1

( en )

2011-09-20

2014-08-21

Ishida Co., Ltd.

Mass measurement device

US9242380B2

( en )

2013-02-25

2016-01-26

Panasonic Intellectual Property Management Co., Ltd.

Robot, robot control apparatus, robot control method, and robot control program

RU2575184C1

( en )

2014-10-30

2016-02-20

Method to determine body inertia tensor

US20160178430A1

( en )

2014-12-17

2016-06-23

Norgren Automation Solutions, Llc

Apparatus and method for detecting multiple workpieces

US20180370021A1

( en )

2017-06-27

2018-12-27

Massachusetts Institute Of Technology

Wearable robotic systems for supporting a load

US20190321977A1

( en )

2018-04-23

2019-10-24

General Electric Company

Architecture and methods for robotic mobile manipluation system

US20200094401A1

( en )

2018-09-21

2020-03-26

Beijing Jingdong Shangke Information Technology Co., Ltd.

System and method for automatic learning of product manipulation

US20200130961A1

( en )

2018-10-30

2020-04-30

Mujin, Inc.

Robotic system with automated package registration mechanism and methods of operating the same

US20200306964A1

( en )

2019-03-27

2020-10-01

Boston Dynamics, Inc.

Palletizing Boxes

US20220371195A1

( en )

2019-11-19

2022-11-24

Google Llc

Optimization of Motion Paths of a Robot Using Vision Data

US20210291371A1

( en )

2020-03-18

2021-09-23

Kabushiki Kaisha Yaskawa Denki

Robot control apparatus, robot control system, and robot control method

Non-Patent Citations (1)

* Cited by examiner, â Cited by third party

Title

ALEKSANDROVA M YU, RU 2575184 C1 (RU 2014143855 A) translation, METHOD TO DETERMINE BODY INERTIA TENSOR, 2014-10-30, FEDERAL'NOE GOSUDARSTVENNOE BJUDZHETNOE UCHREZHDENIE (Year: 2014)

Cited By (16)

* Cited by examiner, â Cited by third party

Publication number

Priority date

Publication date

Assignee

Title

US20210239512A1

( en )

2018-06-07

2021-08-05

Antolini Bilance S.R.L.

Method for handling and checking products in general or raw material in blocks or slabs

USD1013003S1

( en )

2021-03-26

2024-01-30

Boston Dynamics, Inc.

Robotic device

USD1013002S1

( en )

2021-03-26

2024-01-30

Boston Dynamics, Inc.

Robotic device

USD1013004S1

( en )

2021-03-26

2024-01-30

Boston Dynamics, Inc.

Robotic device

US12632028B2

( en )

2021-04-23

2026-05-19

Fanuc Corporation

Numerical control device and numerical control system

US20230321825A1

( en )

2022-03-25

2023-10-12

Dexterity, Inc.

Detection of heavy objects in robotic induction

US12544920B2

( en )

2022-03-25

2026-02-10

Dexterity, Inc.

Detection of heavy objects in robotic induction

USD1033501S1

( en )

2022-08-10

2024-07-02

Boston Dynamics, Inc.

Robotic device

USD1018621S1

( en )

2022-08-10

2024-03-19

Boston Dynamics, Inc.

Robotic device

USD1034728S1

( en )

2022-08-10

2024-07-09

Boston Dynamics, Inc.

Robotic device

USD1034729S1

( en )

2022-08-10

2024-07-09

Boston Dynamics, Inc.

Robotic device

USD1013001S1

( en )

2022-08-10

2024-01-30

Boston Dynamics, Inc.

Robotic device

CN115771753A

( en )

2022-12-15

2023-03-10

æ¢å¡æ¼å¾·ï¼åäº¬ï¼æºå¨äººç§ææéå¬å¸

Palletizing power failure processing method, equipment and storage medium

US12440980B1

( en )

2023-02-28

2025-10-14

Agility Robotics, Inc.

Robotic manipulation with bimanual and nonprehensile aspects and related technology

<tr itemprop="forwardReferences" it

Dynamic mass estimation methods for an integrated mobile manipulator robot — Boston Dynamics, Inc. (US20220305678A1)

Related documents