Robot Gripping Pose Registration Using Pattern-Based 3D Rotation
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Solution Overview
Problem
Existing techniques are inefficient for registering multiple gripping poses of a robot hand relative to a gripping target and fail to apply appropriate gripping patterns for complex shapes.
Innovation Solution
A gripping pose registration apparatus that allows users to set a gripping pose of a robot hand three-dimensionally and designate a type of gripping pattern, automatically generating a group of gripping poses through rotation or translation in a tool coordinate system, enabling collective registration and application of patterns for various shapes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple gripping poses are registered manually using existing techniques, then gripping position registration can be completed, but the processing time becomes excessively long
Solution Approach 1:
The system performs preliminary action by automatically generating multiple gripping poses based on a single user-designated pose and predefined patterns. Instead of requiring users to manually register each pose individually, the system pre-generates a group of poses (e.g., 5-10 poses) by applying transformations such as rotation around the gripping target, translation along coordinate axes, or mirroring operations to the initial pose, thereby significantly reducing registration time while maintaining precision.
Solution Approach 2:
The system uses copying by creating multiple variations of the user-designated gripping pose through automated transformations. Each generated pose is a copied and modified version of the original pose, applying operations like rotation by specific angles (e.g., 30°, 45°, 60°), translation by fixed distances, or mirroring across planes. This copying approach allows rapid generation of diverse gripping poses without requiring manual input for each one.
2Productivity
If primitive shape fitting is used to determine gripping positions, then some gripping targets can be processed automatically, but complex-shaped targets cannot be fit to appropriate primitive shapes
Solution Approach 1:
The system achieves universality by making the gripping pose generation method applicable to any gripping target regardless of its shape. Instead of relying on shape-specific primitive fitting, the system uses a universal approach where users can designate a gripping pose for any target, and the system automatically generates additional poses through pattern-based transformations. This multi-functional capability allows the same generation process to work for simple geometric shapes, complex organic shapes, and irregular objects alike.
Solution Approach 2:
The system applies parameter changes by transforming the initial gripping pose through systematic modifications of positional and orientational parameters. The generation process varies parameters such as rotation angles (e.g., 0°, 30°, 60°, 90°), translation distances, and mirroring operations to create diverse gripping poses from a single input pose. This parameter-based approach bypasses the limitations of shape fitting by directly manipulating pose parameters rather than relying on geometric matching.
3Measurement precision
If users manually set each gripping pose individually, then precise control over each pose is achieved, but the complexity of operation increases significantly
Solution Approach 1:
The system implements self-service by automatically generating multiple gripping poses without requiring continuous user intervention. After the user designates one initial gripping pose and selects a generation pattern, the system autonomously generates the remaining poses by applying predefined transformations. This self-service mechanism reduces operational complexity from requiring multiple manual pose registrations to a single user action, while the system handles the repetitive generation task automatically.
Solution Approach 2:
The system applies segmentation by dividing the gripping pose registration task into two distinct phases: (1) user designation of a single reference pose, and (2) automated generation of multiple poses through pattern application. This segmentation separates the creative input (user-defined reference pose) from the repetitive execution (generating variations), thereby reducing operational complexity while maintaining precision through the structured generation process.
Data Source
AI summary
The rotation center is calculated (step S11). A gripping pattern is determined (step S12). The rotation axis is set to X-axis (step S13) of a tool coordinate system in response to the gripping pattern being a fan-shaped pattern, to Y-axis (step S14) in response to being a cylinder pattern, and to Z-axis (step S15) in response to being a circle pattern. A start angle θ_start is set as a rotation angle θ (step S16). The pose of a tool is calculated (step S17). The current pose is registered (step S18). The angle θ is changed by a predetermined angle of Δθ (step S19). Steps S17 to S19 are repeated until the angle θ reaches an end angle θ_end (step S20).