PATENT CLAIM ANALYSIS

Application Number: 15917219
Application Type: Utility
Filing Date: 2018-03
Publication Date: 2018-07
Patent Classification: ["700", "254000"]

Abstract:
A mechanism-parametric-calibration method for a robotic arm system is provided, including: controlling the robotic arm to perform a plurality of actions so that one end of the robotic arm moves toward corresponding predictive positioning-points; determining a predictive relative-displacement between each two of the predictive positioning-points; after each of the actions is performed, sensing three-dimensional positioning information of the end of the robotic arm; determining, according to the three-dimensional positioning information, a measured relative-displacement moved by the end of the robotic-arm when each two of the actions are performed; deriving an equation corresponding to the robotic arm from the predictive relative-displacements and the measured relative-displacements; and utilizing a feasible algorithm to find the solution of the equation. When an ambient temperature changes or a stress variation on the robotic arm exceeds a predetermined range, re-obtaining the set of mechanism parametric deviations corresponds to a current robot configuration.

Claim (Index 1):
A mechanism-parametric-calibration method for a robotic arm system, wherein the robotic arm system comprises a robotic arm and a measuring instrument, and the mechanism-parametric-calibration method comprises:\n controlling, according to n mechanism parameter sets, the robotic arm performing n actions so that an end of the robotic arm moves toward n corresponding predictive positioning-points; determining a predictive relative-displacement equation of each two of the n predictive positioning-points; sensing, using the measuring instrument, three-dimensional measured positioning-points corresponding to the end of the robotic arm after the robotic arm performs each of the n actions; determining, according to the n three-dimensional measured positioning-points, a measured relative-displacement moved by the end of the robotic arm when the robotic arm performs each two of the n actions; deriving an optimization equation corresponding to the robotic arm from the predictive relative-displacement equations and the measured relative-displacements; obtaining, by the optimization equation, a set of mechanism parametric deviations of the robotic arm; and calibrating, by the set of mechanism parametric deviations, the n mechanism parameter sets of the robotic arm; wherein the optimization equation for a specific status of the robotic arm is presented as: \u03a6 rConfig = min \u0394 \ue89e \ue89e S \ue89e \u2211 i = 1 n - 1 \ue89e \u2211 j = i + 1 n \ue89e ( \u0394 \ue89e \ue89e M i , j - G \ue8a0 ( S i , S j , \u0394 \ue89e \ue89e S \ue8a0 ( rConfig ) ) ) 2 ; and wherein \u0394M i,j  is the measured relative-displacement, G(S i ,S j ,\u0394S(rConfig)) is the predictive relative-displacement equation, S i  and S j  are the mechanism parameter sets, and \u0394S(rConfig) is the set of mechanism parametric deviations related to the specific status of the robotic arm; and wherein when an ambient temperature of the robotic arm changes or a stress variation on the robotic arm exceeds a predetermined range, re-obtaining the set of mechanism parametric deviations corresponds to a current robot configuration of the robotic arm by the optimization equation.

Metadata:
- Claim Count in Document: 19.0
- Percentile: 90.0
- Lexical Diversity: 1.9875
- Patent Class: 700.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: True
- Related Applications: ['15213736', '11491255', '15516522', '14314970', '11195724']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3391198461059794
- 35 USC 102 Novelty (BERT): 0.679735190331393
- Combined Prediction Score: 0.3731813805285208
- Mean Citation Score: 255.40995200000003
- Max Citation Score: 677.1442
- Similarity Product: 669.4935417516232

Labels:
- Claim Label 101: 1
- Claim Label 102: 1
- Claim Label 103: 1
- Claim Label 112: 0
- Combined Label: 1
- Label 101 Adjusted: 1

Dataset: test