Robotic Instrument Manipulation With Vision-Based Pose and Occlusion Sensing
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Solution Overview
Problem
Robotic manipulation of items in various environments is hindered by uncertainties in the environment and instrumentation, challenges in grasping and handling large or small items, and interactions with human collaborators, leading to inaccuracies and errors in robotic manipulation tasks.
Innovation Solution
A processor-implemented method and robotic manipulation system that uses sensors to acquire and process image data to identify non-occluded instruments, estimate their pose, and operate manipulators and effectors to perform tasks, incorporating vision-based motion planning and control to enhance the accuracy and robustness of robotic manipulation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If robotic systems are deployed to manipulate instruments in uncertain environments, then automation and productivity are improved, but reliability and accuracy deteriorate due to environmental uncertainties and instrumentation variability
Solution Approach 1:
The system continuously acquires sensor data from the environment and uses vision processing to identify instruments, estimate poses, and detect occlusions. This real-time feedback loop allows the robotic system to adapt to environmental uncertainties and maintain reliable manipulation despite variations in instrument positioning, occlusions, and environmental conditions
Solution Approach 2:
The system performs preliminary vision processing to identify non-occluded instruments and estimate their poses before manipulation begins. By pre-processing sensor data to determine instrument locations and orientations, the system prepares manipulation plans in advance, improving both automation and reliability by reducing reactive adjustments during execution
2Measurement precision
If vision-based approaches are used to identify instruments in cluttered environments, then measurement precision is improved, but device complexity increases due to additional sensors and processing requirements
Solution Approach 1:
The system extracts only the essential information needed for manipulation from the sensor data - specifically identifying non-occluded instruments and estimating their poses. By filtering out unnecessary data and focusing only on critical features, the system achieves high measurement precision without requiring overly complex processing systems
Solution Approach 2:
The vision processing system acts as an intermediary between the sensors and the robotic manipulator. It processes raw sensor data into meaningful instrument identifications and pose estimates, simplifying the overall system architecture by creating a dedicated processing layer that bridges perception and action
3Adaptability or versatility
If the robot attempts to grasp and handle diverse items including large and small objects, then adaptability is improved, but manufacturing precision and grasping accuracy deteriorate due to varying item sizes and shapes
Solution Approach 1:
The system dynamically adjusts its manipulation approach based on the identified instrument characteristics. By estimating poses and considering occlusion states for each specific instrument, the system adapts its grasping strategy to match the size, shape, and position of each object, maintaining precision across diverse item types
Solution Approach 2:
The system applies different manipulation strategies to different instruments based on their individual characteristics. By identifying each instrument's specific pose and occlusion state, the system tailors its grasping approach to the local requirements of each object rather than using a uniform approach, thereby maintaining precision across varied item types
4Measurement precision
If occlusion inference is performed to identify non-occluded instruments, then measurement precision is improved, but loss of time increases due to additional processing steps
Solution Approach 1:
The system performs occlusion inference only for instruments that are potentially visible in the sensor data, rather than analyzing every possible instrument in the environment. By applying occlusion detection selectively to relevant candidates, the system achieves accurate pose estimation without unnecessarily extending processing time
Data Source
AI summary
An approach relates to manipulation of tools or instruments in the performance of a task by a robot. In accordance with this approach, sensor data is acquired and processed to identify a subset of instruments initially susceptible to manipulation. The instruments are then manipulated in the performance of the task based on the processed sensor data.