Auxiliary Sensor Integration for Point Cloud Haptic Rendering
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current robotic systems face challenges in providing a realistic and immersive virtual environment for human operators to effectively control robots in remote and hazardous environments, particularly in scenarios involving transparent objects and sparse point cloud data, which can lead to collisions and incomplete geometric information.
Innovation Solution
The integration of pre-touch sensing data and haptic feedback, combined with virtual haptic fixtures and 3D mapping techniques, allows for enhanced point cloud augmentation and collision avoidance, enabling human operators to accurately grasp and manipulate objects by providing critical geometrical information and preventing unintended contact.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If point cloud data from RGB-D cameras is used to represent transparent objects, then geometric information can be obtained, but the point cloud data remains sparse and incomplete for transparent objects
Solution Approach 1:
The patent combines data from multiple sensor types (RGB-D cameras, LIDAR, SONAR, RADAR) to create a comprehensive environmental model. By merging point cloud data with depth information and auxiliary sensor data, the system overcomes the sparsity and incompleteness of individual sensor inputs, particularly for transparent objects that are difficult for single sensors to detect accurately.
Solution Approach 2:
The patent introduces virtual haptic fixtures as an intermediary representation between the physical environment and the operator. These virtual fixtures are generated from sensor data and provide a tangible interface that mediates the interaction between the operator and the remote environment, allowing the operator to perceive and manipulate objects even when direct visual or tactile feedback is limited.
2Reliability
If virtual haptic fixtures are used to prevent HIP penetration, then collision avoidance is improved, but the system complexity increases
Solution Approach 1:
The patent implements virtual haptic fixtures that are pre-configured with penetration depth parameters and force field characteristics. By establishing these virtual constraints before physical contact occurs, the system prevents HIP penetration and potential collisions proactively, rather than reacting after contact is made. This preliminary action approach improves reliability while managing complexity through pre-computed parameters.
Solution Approach 2:
The system continuously monitors HIP position relative to virtual fixtures and provides real-time haptic feedback forces to prevent penetration. This closed-loop feedback mechanism detects potential violations of virtual boundaries and applies corrective forces, ensuring collision avoidance while maintaining a relatively simple underlying architecture through iterative correction rather than complex preventive modeling.
3Loss of information
If multiple sensor types are integrated to augment point cloud data, then data density and accuracy improve, but the device complexity increases
Solution Approach 1:
The patent creates a unified sensor fusion framework that processes data from multiple sensor types (RGB-D cameras, LIDAR, SONAR, RADAR) through a common point cloud generation and virtual fixture implementation architecture. This multi-functional approach allows the same software framework to handle diverse sensor inputs, reducing overall system complexity despite the variety of sensors employed.
Solution Approach 2:
The patent replaces complex mechanical sensor integration with software-based data fusion. Instead of physically coupling sensors in complex arrangements, the system uses computational algorithms to merge data from independently mounted sensors, creating a unified environmental model. This substitution of mechanical complexity with software processing simplifies the physical system while achieving comprehensive sensor integration.
Data Source
AI summary
Apparatus and methods for generating virtual environment displays based on a group of sensors are provided. A computing device can receive first data about an environment from a first group of one or more sensors. The computing device can model the environment as a virtual environment based on the first data. The computing device can determine whether to obtain additional data to model the environment. After determining to obtain additional data to model the environment, the computing device can: receive second data about the environment, and model the environment as the virtual environment based on at least the second data. The computing device can generate a display of the virtual environment.