Multi-Resolution Grasp Planning for Free-Form Object Manipulation
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Solution Overview
Problem
Robots face challenges in reliably and efficiently manipulating free-form objects in dynamic and semi-structured environments due to the complexity of object shapes and variability in environments, which affects their ability to grasp and interact with objects effectively.
Innovation Solution
An enhanced environmental analysis and robotic end effector control process that integrates sensor data from imaging, range, and inertial sensors to generate maps and semantic labels, enabling the robot to determine suitable grasping configurations through a mission planner and end effector controller, using neural networks and spatial programming for efficient and adaptable object manipulation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If robots use traditional object manipulation methods, then the system structure is simple, but the reliability of manipulating free-form objects in dynamic environments deteriorates
Solution Approach 1:
The patent segments the object manipulation task into multiple resolution levels (coarse to fine) and separates environmental analysis from execution planning. The multi-resolution approach divides the configuration space into hierarchical levels, allowing robots to first identify coarse grasping regions and then refine to precise contact points, improving reliability without requiring complete environmental understanding at once.
Solution Approach 2:
The patent performs preliminary environmental analysis to generate maps and identify object characteristics before execution planning. The system pre-processes sensor data to create occupancy maps, surface maps, and object models, then uses this pre-analyzed information to guide the multi-resolution planning process, ensuring reliable manipulation by preparing all necessary information in advance.
2Reliability
If robots perform detailed environmental analysis to grasp free-form objects, then the reliability of object manipulation improves, but the time and computational resources required increase
Solution Approach 1:
The patent implements a dynamic multi-resolution planning process that adapts the level of detail based on task requirements and environmental uncertainty. The system dynamically adjusts resolution levels during planning, starting with coarse analysis for quick orientation and progressively refining to fine details only where necessary for successful grasping, reducing overall computation time while maintaining reliability.
Solution Approach 2:
The patent applies partial analysis by focusing computational resources on critical regions of interest rather than analyzing the entire environment at maximum resolution. The multi-resolution approach performs detailed analysis only on relevant object surfaces and grasping configurations, while using lower resolution for surrounding areas, thus achieving reliable grasping with reduced computational overhead and time loss.
3Manufacturing precision
If robots use high-resolution analysis for all objects, then the precision of grasping configurations improves, but the computational complexity and processing time increase
Solution Approach 1:
The patent adds a resolution dimension to the planning process, creating a multi-resolution configuration space that extends beyond traditional single-resolution analysis. This dimensional extension allows the system to navigate from coarse to fine resolutions systematically, achieving precise grasping configurations by progressively refining solutions across multiple scales rather than requiring full high-resolution analysis from the start.
Solution Approach 2:
The patent performs preliminary coarse-resolution analysis to identify feasible grasping regions and object characteristics before conducting fine-resolution analysis. This preliminary step filters out invalid configurations early, reducing the computational burden of detailed analysis to only promising candidates, thus achieving high precision grasping configurations with managed computational complexity.
4Adaptability or versatility
If robots adapt to dynamic environments with moving objects, then the adaptability improves, but the difficulty of environmental analysis and manipulation increases
Solution Approach 1:
The patent implements dynamic environmental analysis that continuously updates occupancy maps and object models as new sensor data arrives. The multi-resolution planning process adapts to environmental changes by re-evaluating grasping configurations at appropriate resolution levels, allowing the system to maintain adaptability to moving objects and dynamic conditions while managing analysis difficulty through hierarchical processing.
Data Source
AI summary
Systems, apparatuses and methods may provide for controlling one or more end effectors by generating a semantic labelled image based on image data, wherein the semantic labelled image is to identify a shape of an object and a semantic label of the object, associating a first set of actions with the object, and generating a plan based on an intersection of the first set of actions and a second set of actions to satisfy a command from a user through actuation of one or more end effectors, wherein the second set of actions are to be associated with the command