Robot Haptic Photogrammetry for Richer Environment Models
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Solution Overview
Problem
Robots operating in environments face suboptimal performance due to incomplete environment models, which lack comprehensive sensory data, leading to inadequate interaction with objects and surfaces.
Innovation Solution
A robot system equipped with haptic sensors and processors that access and refine environment models using haptic and visual data, allowing for the identification and representation of objects and surfaces, enabling more accurate control and interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If robots use basic environment models without haptic data, then device complexity is reduced, but measurement precision and interaction accuracy deteriorate
Solution Approach 1:
The system performs haptic exploration actions beforehand to collect haptic data about objects and surfaces in the environment. This preliminary data collection enables the robot to build accurate haptic profiles and environment models before actual task execution, improving measurement precision without requiring complex real-time sensing during operations.
Solution Approach 2:
The system creates haptic profiles that are simplified representations or copies of complex physical objects and surfaces. These haptic profiles capture essential tactile characteristics (texture, compliance, temperature) in a processed format that can be stored and referenced, reducing the need for continuous complex sensing while maintaining accuracy.
2Loss of information
If robots collect comprehensive haptic data from all objects, then environment model completeness improves, but loss of time and data processing burden increases
Solution Approach 1:
The system applies different levels of haptic data collection to different regions and objects based on their importance and relevance to the robot's tasks. Critical objects receive detailed haptic profiling while less important areas use simplified or skipped sensing, reducing overall data collection time while maintaining model completeness for essential elements.
Solution Approach 2:
The system collects haptic data selectively rather than comprehensively from all objects. It focuses on gathering sufficient haptic information from key objects and surfaces needed for task execution, avoiding unnecessary data collection from irrelevant elements, thus reducing time loss while maintaining adequate environment model completeness.
3Adaptability or versatility
If robots use only visual data for environment modeling, then device complexity is minimized, but adaptability and interaction capability deteriorate
Solution Approach 1:
The system merges visual data from cameras with haptic data from tactile sensors to create a unified environment model. This combination allows the robot to leverage the strengths of both modalities - visual recognition for object identification and haptic sensing for texture, compliance, and contact force information - thereby improving adaptability and interaction capability.
Solution Approach 2:
The haptic sensor system is designed to perform multiple functions: identifying object material properties, determining surface texture, measuring contact forces, and detecting object compliance. This multi-functionality enables versatile object interaction using a single integrated sensing approach, improving adaptability without proportionally increasing device complexity.
Data Source
AI summary
Robots, robot systems, and methods for operating the same based on environment models including haptic data are described. An environment model which includes representations of objects in an environment is accessed, and a robot system is controlled based on the environment model. The environment model incudes haptic data, which provides more effective control of the robot. The environment model is populated based on visual profiles, haptic profiles, and/or other data profiles for objects or features retrieved from respective databases. Identification of objects or features can be based on cross-referencing between visual and haptic profiles, to populate the environment model with data not directly collected by a robot which is populating the model, or data not directly collected from the actual objects or features in the environment.