DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant’s arguments filed on 12/04/2025 with respect to claim(s) 1, 3-5, 7-10, and 13-14 have been fully considered but are not persuasive or moot in view of new ground of rejection provided below which was necessitated based on Applicant’s amendments to the claims. The new ground of rejection for independent claim is based on Nikovski in view of Shimodaira, Kawada, Floyd-Jones, and further in view of Jha. Floyd-Jones teaches collision determination provided by sphere tree (See at least Abstract – “Collision detection useful in motion planning for robotics advantageously employs data structure representations of robots, persistent obstacles and transient obstacles in an environment in which a robot will operate. Data structures may take the form of hierarchical data structures ((e.g., octrees, sets of volumes or boxes (e.g. a tree of axis-aligned bounding boxes (AABBs), a tree of oriented (not axis-aligned) bounding boxes, or a tree of spheres)) or non-hierarchical data structures (e.g., Euclidean Distance Fields) Such can result in computational efficiency, reduce memory requirements, and lower power consumption…”, Para [0201] “Determining whether objects (e.g., robot, obstacles) represented as trees collide can be fast because of the inherent hierarchical nature of the data structure. The exemplary pseudocode provided immediately below illustrates a recursive algorithm for a testing operation to determine whether there is a collision between two sphere trees.”). Hence, it would have been obvious to an ordinary skill in the art to reach a conclusion that Nikovski in combination with Floyd-Jones, Shimodaira, Kawada, and Jha anticipates the teachings of claim 1.
The same reasoning applied to the independent claims also apply to their corresponding dependent claims.
Claim Objections
Claim 13 and 14 are objected to because of the following informalities: “The method in claim …” should read “The method of claim …” like the rest of the claims. Appropriate correction is required.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1, 3-5, 13, and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Nikovski et al. (US 2020/0230815 A1) (Hereinafter Nikovski), in view of Shimodaira (US 2016/0231726 A1), Kawada (JP2014155994A), Floyd-Jones et al. (US 20210178591 A1) (Hereinafter Floyd-Jones), and further in view of Jha et al. (US 20230294283 A1) (Hereinafter Jha).
Regarding Claim 1, Nikovski teaches a method for autonomous complex assembly based on a task-independent approach, comprising:
directing a robotic member on a first approach trajectory for placement of a grasped object (See at least Para [0040] “The robotic assembly 100 is configured to perform an insertion operation, e.g., insert a component 103 into a component 104, along an insertion line. As used herein, the insertion line is a trajectory of a motion of the wrist 102 defining a trajectory of the motion of the component 103….”, Fig 7), …
determining that placement along the first approach trajectory is blocked (See at least Para [0002] “…Anomaly detection-based automatic error recovery is helpful to maintain productivity requirements as well as the desired specifications for the assembled product.”, Para [0004] “…The automatic error recovery methods utilizing fault detection, also referred as anomaly detection, during the robotic insertion can be broadly divided into model-based and data-driven methods.”, Para [0026] “Some embodiments perform automatic error recovery by controlling the robotic arm based on the result of anomaly detection. For example, one embodiment stops the robotic arm in response to detecting the anomaly…”, Para [0042] “It is an object of an anomaly detection and control system 105 to detect anomaly of the insertion process performed by the robotic assembly 100 and to control the assembly based on results of the anomaly detection. It is another object to perform such an anomaly detection using the measurements 115 of the force sensor 110…”, discloses anomaly detection using measurements of the force sensor which is construed as determining blockage along placement trajectory); and
identifying an alternate approach trajectory defined by a second approach trajectory for placement of the grasped object based on a probability of success of the second approach trajectory (See at least Para [0011] “…It is another object of some embodiments to provide an automatic error recovery using data-driven model of the expected behavior of the force profile along the trajectory of the robot's end-effector while allowing for uncontrollable uncertainties introduced by process noise in the robotic assembly. For example, one embodiment discloses an automatic error recovery that takes advantage of a probabilistic functional model for the expected force profile along the trajectory of the robot during the normal (successful) insertion processes.”, Para [0015] “To that end, some embodiments perform automatic anomaly detection using a probabilistic relationship for a force experienced by the wrist of a robotic arm along a line of insertion as a probabilistic function of a position of the wrist of the robotic arm along the line of insertion. During the operation of the robotic arm, at a current instance of time, the embodiments determine a probability of the current force conditioned on the current position of the wrist of the robotic arm according to the probabilistic function and determine a result of anomaly detection based on the probability of the current value of the force. In effect, the embodiments allow to perform more probabilistic detection in real time with practical computational requirements.”, Para [0055] “FIG. 1D shows a schematic of determining confidence intervals of values of the force according to some embodiments. For example, a position 163 has a probability distribution of the force 170 according to a probabilistic relationship 160. For a given confidence threshold 174, the probability distribution 170 defines an interval of normal, i.e., non-anomalous force values, In this example, the normal values of the force for position 163 are values between force 171 and force 172. For different positions, the confidence interval is different. For example, for position 161, the confidence interval is defined by force values 178 and 179. In combinations, multiple confidence intervals for a sequence of values of positions form a confidence corridor confined by boundaries 181 and 182. The confidence corridor defines allowed operating margin for the force as a function of position along the insertion line. In effect, the confidence interval allows detaching learning the probabilistic relationship from anomaly detection and simplifying anomaly detection for multiple position/force pairs, which can reduce the false anomaly detection rate.”, Para [0056] “FIG. 2 shows a block diagram of a method for determining a probabilistic relationship between the force and position of a wrist of a robotic arm along an insertion line according to some embodiments. For example, one embodiment fits a regression 202 to training data 201 for learning/estimating a probabilistic relationship between force experienced by the wrist of the robotic arm at different positions of the wrist of the robotic arm along the line of insertion.”) by: …
modeling a contact force at each of the contact configurations in the set of contact configurations (See at least Para [0005] “… These models can predict the profile of measured forces during different phases of the insertion process, and are used online to detect any deviation from the predicted force profile…”) …
iterating movement based on successive, alternate approach trajectories until placement is achieved (See at least Para [0056] “For example, one embodiment fits a regression 202 to training data 201 for learning/estimating a probabilistic relationship between force experienced by the wrist of the robotic arm at different positions of the wrist of the robotic arm along the line of insertion.”, Para [0045] “The system 105 also includes a memory 130 configured to store a probabilistic relationship for the force experienced by the wrist of the robotic arm along the insertion line as a probabilistic function of the position of the wrist of the robotic arm along the line of insertion. For example, the probabilistic function is learned online and/or offline from measurements of the operation repeatedly performed by one or multiple robotic arms having the configuration of the robotic arm under the control…”).
However, Nikovski does not explicitly spell out … the robotic member capable of actuated movement along 6 axes;
determining that the grasped object has a columnar property, and
forming a sphere tree defined by a surface-based representation of the grasped object including a set of similarly shaped cross sections oriented in an adjacency of 2- dimensional cross sections along a sweeping boundary: …
determining a set of contact configurations between the sphere tree representation of the grasped object and a placement object. wherein the set of contact configurations is calculated through a discretization of an uncertainty range of a relative position between the grasped object and the placement object:
…
selecting a contact configuration such that the respective contact force best matches a sensed force by the robotic member to estimate the relative position between the grasped object and the placement object, the second approach trajectory attaining an angle in which the grasped object is more closely aligned with a non-blocking insertion; and…
Shimodaira teaches … the robotic member capable of actuated movement along 6 axes (See at least Para [0062] “… That is, the robot 1 is a vertical multi-joint (six-axis) robot in which the base 11, the arm members 12, 13, 14, 15, 17, and 18, and the wrist 16 are coupled in this order from the proximal end side to the distal end side.”).
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to combine the system of Nikovski with the teachings of Shimodaira and include the feature of robotic member being capable of actuated movement along 6 axes, thereby providing enhanced flexibility and range of motion during autonomous robotic assembly operation of arbitrary part shapes.
Floyd-Jones teaches …
determining that the grasped object has a columnar property (See at least Para [0055] “FIG. 14B-14E are isometric views of the portion of the robotic appendage of FIG. 14A, depicted as represented in successive levels of a sphere tree of depth 4, according to one illustrated implementation.” discloses sphere tree depth which is construed as columnar property), and
forming a sphere tree defined by a surface-based representation of the grasped object including a set of similarly shaped cross sections oriented in an adjacency of 2- dimensional cross sections along a sweeping boundary (See at least Para [0011] “… Generating a hierarchy of bounding volumes may include generating a k-ary sphere tree. Generating a hierarchy of bounding volumes may include generating a hierarchy of axis-aligned bounding boxes. Generating a hierarchy of bounding volumes may include generating a hierarchy of oriented bounding boxes. Generating the data structure representation of the set of persistent obstacles in the environment may include generating an octree that stores voxel occupancy information that represents the set of persistent obstacles in the environment. Generating the data structure representation of the robot may include generating a k-ary tree. Generating the data structure representation of the robot may include for each of a number of links of the robot, generating a respective k-ary tree. Generating the data structure representation of the robot may include for each of a number of links of the robot, generating a respective 8-ary tree. Generating the data structure representation of the robot may include for each of a number of links of the robot, generating a respective 8-ary tree with a tree depth equal to or greater than four. Generating the data structure representation of the robot may include for each of a number of links of the robot, generating a respective k-ary tree, where each node of the k-ary tree is a sphere that is identified as occupied if any portion of the respective sphere is occupied.”): …
determining a set of contact configurations between the sphere tree representation of
the grasped object and a placement object. wherein the set of contact configurations is calculated through a discretization of an uncertainty range of a relative position between the grasped object and the placement object (See at least Para [0097] “…Application
programs 238 may include instructions that cause the processor(s) 212 to perform one or more of: generating discretized representations of the environment 100 in which the robot will operate, including obstacles 106 and/or target objects 108 in the environment 100; generating planning graphs, motion plans or road maps including calling for or otherwise obtaining results of a collision assessment; and/or storing the determined plurality of planning graphs, motion plans, or road maps. Application programs 238 may additionally include one or more machine-readable instruction sets that cause the processor(s) 212 to perform other operations of perception (via sensors 282), planning graph construction, collision detection, and path search as described herein and in the references incorporated herein by reference.”, Para [0113] “The planning graph or road map 402 includes a number of nodes 406a, 406b, 406c, 406d, . . . 406n (eleven shown, only five called out, collectively 406) which represent respective states of the robot, the state which may include, but is not limited to, a particular configuration of the robot (which is the complete specification of a particular set of joint positions of the robot), pose, velocity and heading of the robot. One of the states may, for example, be or represent a start state 406a. One of the states may, for example, be or represent an end or goal state 406n. The planning graph or road map 402 includes a number of edges 408a, 408b, 408c, 408d, . . . 408n (eleven shown, only five called out, collectively 408). Each edge 408 of the planning graph or road map 402 represents a transition of the robot from one state to another state.”):
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to combine the system of Nikovski with the teachings of Floyd-Jones and include the feature of determining that the grasped object has a columnar property, forming a sphere tree defined by a surface-based representation of the grasped object including a set of similarly shaped cross sections oriented in an adjacency of 2- dimensional cross sections along a sweeping boundary, and determining a set of contact configurations between the sphere tree representation of the grasped object and a placement object. wherein the set of contact configurations is calculated through a discretization of an uncertainty range of a relative position between the grasped object and the placement object, thereby provide computational efficiency, reduce memory requirements, and lower power consumption (See at least Abstract – “Collision detection useful in motion planning for robotics advantageously employs data structure representations of robots, persistent obstacles and transient obstacles in an environment in which a robot will operate. Data structures may take the form of hierarchical data structures ((e.g., octrees, sets of volumes or boxes (e.g. a tree of axis-aligned bounding boxes (AABBs), a tree of oriented (not axis-aligned) bounding boxes, or a tree of spheres)) or non-hierarchical data structures (e.g., Euclidean Distance Fields) Such can result in computational efficiency, reduce memory requirements, and lower power consumption…”, Para [0176] “An octree is a hierarchical data structure to store voxel occupancy data. A tree structure allows collision detection to descend only as far into an inherent hierarchy of the trees' structure as needed, increasing computational efficiency of a collision detection process …”).
Jha teaches …
selecting a contact configuration such that the respective contact force best matches a sensed force by the robotic member to estimate the relative position between the grasped object and the placement object (See at least Para [0017] “Further, some embodiments of the
present invention provide a computer-implemented manipulation method for performing desired manipulation by reorienting an object on a workbench by a manipulator of a robotic system using external contacts of the object with a part of the workbench. The method uses a processor coupled with a memory storing instructions implementing the method, wherein the instructions, when executed by the processor, carry out at steps of the method, including: acquiring measurement data from vision sensors and force sensors arranged on the robotic system; determining an input-output relation for the object based on a nonlinear static model representing input-output relationships between contact forces and movements of the object on the workbench; representing interaction between the object and the manipulator using complementarity constraints to capture the contact state between the object and the manipulator; formulating a representation for frictional stability of the object based on the non-linear static model at the external contacts with the workbench; formulating a bilevel optimization problem so as to maximize the frictional stability over a position trajectory of the object being manipulated on the workbench; estimating, using the frictional stability, uncertainty value in physical parameters to be compensated by performing the bilevel optimization problem; solving the bilevel optimization problem using a non-linear optimization solver and generating control data with respect to a sequence of the contact forces being applied to the object by using the manipulator; and transmitting the control data that instruct the manipulator to perform the reorienting the object on the workbench according to the sequence of the contact forces for obtaining a target position of the object”), and
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective
filing date of the claimed invention to combine the system of Nikovski with the teachings of Jha and include the feature of selecting a contact configuration such that the respective contact force best matches a sensed force by the robotic member to estimate the relative position between the grasped object and the placement object, thereby enable precise calculation for accurate object alignment during assembly (See at least Para [0011] “To that end, the present disclosure proposes an optimization method for robust trajectory optimization during manipulation. To solve the underlying manipulation problem, a bilevel trajectory optimization method for generating trajectory in the presence of uncertain parameters is proposed…”).
Kawada teaches … the second approach trajectory attaining an angle in which the grasped
object is more closely aligned with a non-blocking insertion (See at least Para [0006] “Such an object is achieved by the present invention described below. Application Example 1 In a robot according to the present invention, an arm connected body having a plurality of arms rotatably connected, a force sensor connected to the arm connected body, and the force sensor are mounted A robot body including a hand gripping a gripped object, and a robot control device controlling an operation of the robot body, the robot control device pressing an insertion portion of the gripped object gripped by the hand A first scan for moving the grasped object along the pressing surface while pressing against a surface includes a pressing direction of the insertion portion and a gripping axis perpendicular to a direction for gripping the gripped object. An angle setting unit configured to change an angle θ to be formed and to detect a reaction force by the force sensor at the time of the first scan, and to set the angle θ based on a detection result of the force sensor; Pressing of the insert Is set to a value set by the angle setting unit, the gripping object while pressing the insertion portion of the gripping object gripped by the hand against the insertion object A second scan for moving the object along the insertion target, and in the second scan, the force sensor detects a reaction force, and the insertion point is detected based on the detection result of the force sensor And an insertion operation control unit for inserting the insertion portion of the object to be inserted into the insertion position.”, Para [0008] “Application Example 2 In the robot according to the present invention, the angle setting unit obtains the frictional force between the insertion portion and the pressing surface based on the detection result of the force sensor, and the frictional force is obtained. Preferably, the angle θ is set based on An appropriate angle θ can be set for grasped objects of various shapes, which makes it possible to always stably detect the position of the insertion point even when grasping objects having different shapes continuously.); and …
Therefore, it would have been obvious to one of the ordinary skill in the art before the
effective filing date of the claimed invention to combine the system of Nikovski with the teachings of Kawada and include the feature of second approach trajectory attaining an angle in which the grasped object is more closely aligned with a non-blocking insertion, thereby provide precise calculation for object alignment for assembly (See at least Para [0015] “… By performing two-dimensional analysis on information obtained by a plurality of scans, it is possible to specify a position with higher accuracy. Thereby, the position of the insertion point can be specified more reliably.”).
Regarding Claim 3, Nikovski teaches all the elements of claim 1. Nikovski further teaches the method of claim 1 wherein the robotic member has an end-effector at a distal end configured for compressively grasping the grasped object (See at least Fig 1A item 102, Para [0041] “The robotic assembly 100 also includes a force sensor 110 operatively connected to the wrist of the robotic arm to measure the force tensor experienced by the end-effector of the robot during a manipulation and insertion operation…”, Para [0039] “In some embodiments, the robotic arm 105 includes a wrist 102 for ensuring multiple degrees of freedom of moving the component 103. In some implementations, the wrist 102 has a gripper 106 for holding the mobile component 103.”).
Regarding Claim 4, Nikovski teaches all the elements of claim 3. Nikovski further teaches the method of claim 3 wherein determining blockage is based on at least one of force and torque sensing encountered by the end effector (See at least Fig 1A item 110, Para [0041] “The robotic assembly 100 also includes a force sensor 110 operatively connected to the wrist of the robotic arm to measure the force sensor experienced by the end-effector of the robot during a manipulation and insertion operation…”, Para [0042] “It is an object of an anomaly detection and control system 105 to detect anomaly of the insertion process performed by the robotic assembly 100 and to control the assembly based on results of the anomaly detection. It is another object to perform such an anomaly detection using the measurements 115 of the force sensor 110…”).
Regarding Claim 5, Nikovski teaches all the elements of claim 1.
Although, Nikovski teaches actuator receiving command during robotic assembly (See at least Para [0050] “… the recovery controller 150 can submit a command 155 to the actuators of the robotic assembly 100.”), he does not explicitly spell out the method of claim 1 wherein the robotic member is attached to and driven by a 6-axis robotic actuator.
Shimodaira teaches the method of claim 1 wherein the robotic member is attached to and driven by a 6-axis robotic actuator (See at least Para [0122] “The joints 1200 couples the links 1210, couples a body section and the link 1210, couples the hand 1220 and the link 1210 to be turnable (turnable within a predetermined range). In the example shown in the figure, the robot 1100 is a six-axis arm including six joints.”, Para [0123] “The joints 1200 and the hand 1220 include, for example, an actuator (not shown in FIG. 13) for causing the joints 1200 and the hand 1220 to operate. The actuator includes, for example, a servo motor and an encoder…”).
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to combine the system of Nikovski with the teachings of Shimodaira and include the feature of the robotic member being attached to and driven by a 6-axis robotic actuator, thereby providing enhanced flexibility and range of motion during autonomous robotic assembly operation of arbitrary part shapes.
Regarding Claim 13, modified Nikovski teaches all the elements of claim 1.
However, Nikovski does not explicitly spell out the method in claim 1, wherein the second
approach trajectory is identified based on a path that directs the grasped object from the selected contact configuration and a goal configuration corresponding to the placement of the grasped object.
Jha teaches the method in claim 12, wherein the second approach trajectory is identified based
on a path that directs the grasped object from the selected contact configuration and a goal configuration corresponding to the placement of the grasped object (See at least Para [0017] “… formulating a representation for frictional stability of the object based on the non-linear static model at the external contacts with the workbench; formulating a bilevel optimization problem so as to maximize the frictional stability over a position trajectory of the object being manipulated on the workbench; estimating, using the frictional stability, uncertainty value in physical parameters to be compensated by performing the bilevel optimization problem; solving the bilevel optimization problem using a non-linear optimization solver and generating control data with respect to a sequence of the contact forces being applied to the object by using the manipulator; and transmitting the control data that instruct the manipulator to perform the reorienting the object on the workbench according to the sequence of the contact forces for obtaining a target position of the object”).
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective
filing date of the claimed invention to combine the system of Nikovski with the teachings of Jha and include the feature of the second approach trajectory being identified based on a path that directs the grasped object from the selected contact configuration and a goal configuration corresponding to the placement of the grasped object, thereby enable precise calculation for accurate object alignment during assembly (See at least Para [0011] “To that end, the present disclosure proposes an optimization method for robust trajectory optimization during manipulation. To solve the underlying manipulation problem, a bilevel trajectory optimization method for generating trajectory in the presence of uncertain parameters is proposed…”).
Regarding Claim 14, modified Nikovski teaches all the elements of claim 13. Nikovski further teaches the method in claim 13, further comprising:
attempting to move the grasped object along the second approach trajectory (See at least Para [0010] “…With the recent improvements in sensing technologies, the force experienced by the robot while attempting the desired assembly could be measured…”, Para [0011] “ It is an object of some embodiments to provide a system and a method for detecting anomalous attempts of robotic assembly, in real-time, from measurements of a force sensor mounted on the robot. For example, the force sensor can be mounted on the wrist of the robot to make measurements of the force experienced by the robot performing an insertion of a component to assemble a product. It is another object of some embodiments to provide an automatic error recovery using data-driven model of the expected behavior of the force profile along the trajectory of the robot's end-effector while allowing for uncontrollable uncertainties introduced by process noise in the robotic assembly. For example, one embodiment discloses an automatic error recovery that takes advantage of a probabilistic functional model for the expected force profile along the trajectory of the robot during the normal (successful) insertion processes.”); and
determining whether the selected contact configuration accurately estimates the relative
position between the grasped object and the placement object (See at least Para [0011] “It is an object of some embodiments to provide a system and a method for detecting anomalous attempts of robotic assembly, in real-time, from measurements of a force sensor mounted on the robot. For example, the force sensor can be mounted on the wrist of the robot to make measurements of the force experienced by the robot performing an insertion of a component to assemble a product. It is another object of some embodiments to provide an automatic error recovery using data-driven model of the expected behavior of the force profile along the trajectory of the robot's end-effector while allowing for uncontrollable uncertainties introduced by process noise in the robotic assembly. For example, one embodiment discloses an automatic error recovery that takes advantage of a probabilistic functional model for the expected force profile along the trajectory of the robot during the normal (successful) insertion processes.”).
Claim(s) 7 is rejected under 35 U.S.C. 103 as being unpatentable over Nikovski et al. (US 2020/0230815 A1) (Hereinafter Nikovski), in view of Shimodaira (US 2016/0231726 A1), Kawada (JP2014155994A), Floyd-Jones et al. (US 20210178591 A1) (Hereinafter Floyd-Jones), Jha et al. (US 20230294283 A1) (Hereinafter Jha), and further in view of Yu et al. (G. Yu, D. Wang, Y. Zhang and X. Zhang, "Six degree-of-freedom haptic simulation of sharp geometric features using a hybrid sphere-tree model," 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, Vilamoura-Algarve, Portugal, 2012, pp. 3314-3319) (Hereinafter Yu).
Regarding Claim 7, modified Nikovski teaches all the elements of claim 1.
However, Nikovski does not explicitly spell out the method of claim 1 further comprising:
identifying a cross-section boundary of each cross section in the set of similarly shaped cross sections; and
scaling each cross-section for alignment with a tangent to a periphery of the respective similarly shaped cross section.
Yu teaches the method of claim 6 further comprising:
identifying a cross-section boundary of each cross section in the set of similarly shaped cross
sections (See at least Fig 1 sharp splined shaft and hole (right), shows identifying a cross-section boundary, Fig 2 The spheres at the surface of sharp features. As the probing track, the dashed line shows that it becomes smoother when moving across the sharp corner/edge consisted of bigger spheres (left), Page 3314 Col 1 “Abstract— …We propose a configuration-based optimization method using a hybrid sphere-tree model to compute constraint-based collision response. Based on the variance of dihedral angle between adjacent triangles, an original triangle mesh of the simulated object is segmented into a hybrid sphere-tree model, i.e. a hierarchical sphere-tree for global shape and several linear-lists of spheres for local areas with sharp features. In each local area with sharp features, we first identify those spheres which radius is larger than a pre-defined perceptual threshold. Then these spheres are divided into a linear list of smaller spheres by a splitting method. The experiment results on a sphere-cube interaction and a spline-shaped peg-hole interaction validate that the proposed method can simulate a subtle force direction change when sliding contact occurs across the sharp edges…”); and
scaling each cross-section for alignment with a tangent to a periphery of the respective similarly shaped cross section (See at least Page 3316 Col 2 Para 4 Line 36 -39 “The two sideway sub-spheres could be tangent with the side of chord length symmetrically (Fig. 7(d)). In this way, all the generated sub-spheres could be effectively restricted not bigger than the mesh surface…”).
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective
filing date of the claimed invention to combine the system of Nikovski with the teachings of Yu and include the feature of identifying a cross-section boundary of each cross section in the set of similarly shaped cross sections and scaling each cross-section for alignment with a tangent to a periphery of the respective similarly shaped cross section, thereby enable precise calculation for object alignment for assembly.
Claim(s) 8 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Nikovski et al. (US 2020/0230815 A1) (Hereinafter Nikovski) in view of Shimodaira (US 2016/0231726 A1), Kawada (JP2014155994A), Floyd-Jones et al. (US 20210178591 A1) (Hereinafter Floyd-Jones), Jha et al. (US 20230294283 A1) (Hereinafter Jha), and further in view of Takeuchi (US 2019/0232492 A1).
Regarding Claim 8, modified Nikovski teaches all the elements of claim 1. Nikovski further teaches
… determining an uncertainty in a pose of the placement object resulting from a
misalignment between the grasped object and the placement object (See at least Para [0016] “…In effect, this allows probabilistic anomaly detection, which is more accurate, because it considers uncertainties of measurements and insertion process.”, Para [0051] “Anomaly detection based on probabilistic relationship between force and position along the insertion line allows to reduce dimensionality of anomaly detection problem from 12 or 18 dimensions to just two dimensions. The dimensionality reduction allows to perform data-driven anomaly detection in real time. In addition, the probabilistic nature of the relationship is more accurate representation of a practical robotic assembly process than deterministic relationship. The probabilistic relationship allows to consider uncertainties of the anomaly detection process to increase the accuracy of the fault detection.”); and
computing recovery motions based on the uncertainty for defining the second approach
trajectory and the uncertainty (See at least Para [0016] “…In effect, this allows probabilistic anomaly detection, which is more accurate, because it considers uncertainties of measurements and insertion process.”, Para [0050] “The system 105 also includes a recovery controller 150 configured to control the robotic arm based on the result 145 of anomaly detection. For example, the recovery controller 150 can submit a command 155 to the actuators of the robotic assembly 100…”).
However, Nikovski does not explicitly spell out the method of claim 1 further comprising:
actuating the robotic member for moving the grasped object along an approach trajectory until a contact against a placement object is determined; …
Takeuchi teaches the method of claim 1 further comprising:
actuating the robotic member for moving the grasped object along an approach trajectory until a contact against a placement object is determined (See at least Para [0095] “Contacting is an operation of moving in a designated direction and stopping when receiving reaction force.”, Para [0096] “The category of the contacting operation includes a contacting object. As shown in FIG. 8A, in the contacting object, a workpiece WKa held by the end effector 140 is moved in a designated direction DD and the end effector 140 is stopped when the reaction force is measured by the force detector 130…”); …
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective
filing date of the claimed invention to combine the system of Nikovski with the teachings of Takeuchi and include the feature of actuating the robotic member for moving the grasped object along an approach trajectory until a contact against a placement object is determined in order to determine an uncertainty in a pose of the placement object resulting from a misalignment between the grasped object and the placement object in order to compute recovery motions based on the uncertainty for defining the second approach trajectory and the uncertainty, thereby enable precise calculation for object alignment for assembly.
Regarding Claim 10, Nikovski teaches all the elements of claim 8. Nikovski further teaches the method of claim 8 wherein the placement object has a receptacle having a periphery based on a cross section of the grasped object, the receptacle adapted to receive the grasped object (See at least Fig 1A items 103, 104 , Para [0040] “The robotic assembly 100 is configured to perform an insertion operation, e.g., insert a component 103 into a component 104, along an insertion line…”).
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Nikovski et al. (US 2020/0230815 A1) (Hereinafter Nikovski) in view of Shimodaira (US 2016/0231726 A1), Kawada (JP2014155994A), Floyd-Jones et al. (US 20210178591 A1) (Hereinafter Floyd-Jones), Jha et al. (US 20230294283 A1) (Hereinafter Jha), Takeuchi (US 2019/0232492 A1) and further in view of Rozo et al. (US2021122037A1) (Hereinafter Rozo).
Regarding Claim 9, modified Nikovski teaches all the elements of claim 8. Nikovski further teaches the method of claim 8 further comprising:
sensing an insertion force and torque based on a force/torque sensor on the robotic member (See at least Fig 1A item 110, Para [0041] “The robotic assembly 100 also includes a force sensor 110 operatively connected to the wrist of the robotic arm to measure the force tensor experienced by the end-effector of the robot during a manipulation and insertion operation…”); and …
However, Nikovski does not explicitly spell out … searching a candidate set of uncertainty for identifying a best prediction from a correspondence to the sensed insertion force and torque; and
computing the second approach trajectory based on a mapping of the sensed insertion force and torque to the candidate set.
Rozo teaches … searching a candidate set of uncertainty for identifying a best prediction from a correspondence to the sensed insertion force and torque (See at least Para [0014] “Example 3 is the method according to Example 2, comprising determining a search direction for the mapped updated candidate evaluation point by modifying the gradient of the acquisition function at the mapped updated candidate evaluation point by a multiple of the search direction at the candidate evaluation point mapped to the tangent space of the parameter space at the mapped updated candidate evaluation point by parallel transport.”, Para [0112] “The objective function may be evaluated in course of the Bayesian optimization by means of sensor signals acquired by various types of sensors, including for example any type of force or torque sensor, e.g., attached at the robot end-effector, or by using joint torque sensors of the robot.”, Para [0007] “…in each iteration, updating a candidate evaluation point using a search direction in the tangent space of the parameter space at the candidate evaluation point; mapping the updated candidate evaluation point from the tangent space to the parameter space; and using the mapped updated candidate evaluation point as evaluation point for a next iteration until a stop criterion is fulfilled and controlling the robot in accordance with a control parameter value found in the Bayesian optimization.”); and
computing the second approach trajectory based on a mapping of the sensed insertion force and torque to the candidate set (See at least Para [0007] “…in each iteration, updating a candidate evaluation point using a search direction in the tangent space of the parameter space at the candidate evaluation point; mapping the updated candidate evaluation point from the tangent space to the parameter space; and using the mapped updated candidate evaluation point as evaluation point for a next iteration until a stop criterion is fulfilled and controlling the robot in accordance with a control parameter value found in the Bayesian optimization.”, Para [0112] “The objective function may be evaluated in course of the Bayesian optimization by means of sensor signals acquired by various types of sensors, including for example any type of force or torque sensor, e.g., attached at the robot end-effector, or by using joint torque sensors of the robot.”, Para [0015] “Example 4 is the method according to any one of Examples 1 to 3, comprising mapping the updated candidate evaluation point from the tangent space to the parameter space using the exponential map of the tangent space at the candidate evaluation point.”).
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective
filing date of the claimed invention to combine the system of Nikovski with the teachings of Rozo and include the feature of searching a candidate set of uncertainty for identifying a best prediction from a correspondence to the sensed insertion force and torque and computing the second approach trajectory based on a mapping of the sensed insertion force and torque to the candidate set, thereby enable precise calculation for object alignment for assembly.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Merlhiot (US 2010/0030531 A1) teaches system for detecting collision between rigid or deformable polyhedral objects
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/SHAHEDA HOQUE/Examiner, Art Unit 3658
/Ramon A. Mercado/Supervisory Patent Examiner, Art Unit 3658