Robot Approach Control Using Semantic Segmentation and Depth Mapping
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Solution Overview
Problem
Existing robot control systems face challenges in enabling robots to autonomously approach designated objects efficiently across various environments without specific training, limiting their adaptability and effectiveness.
Innovation Solution
The implementation of a computer program that uses convolutional neural networks to perform semantic segmentation and depth mapping, allowing robots to iteratively navigate towards objects by selecting and executing movement actions based on image data from a camera, enabling efficient object approach in diverse settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a robot is trained to approach an object in a specific environment using machine learning, then the robot can perform the approach task in that environment, but the robot has little or no ability to provide the same function in a different environment
Solution Approach 1:
The patent segments the approach task into distinct phases: localization (identifying the target object), path planning (determining the approach path), and execution (performing the movement). This segmentation allows each phase to be independently optimized and recombined for different environments, resolving the contradiction between reliable task performance and environmental adaptability
Solution Approach 2:
The patent creates a universal approach system that can handle multiple object types and environments through a common framework. The system uses generic sensors (cameras, LIDAR), standardized processing algorithms, and adaptable parameters that can be configured for different scenarios without requiring complete retraining, enabling the robot to perform approach tasks across diverse environments
2Ease of operation
If remote control is used for robot approach operations, then human users can control the robot, but elderly or infirm users may be unable to provide such control and the robot may be required to operate outside of human vision
Solution Approach 1:
The patent implements self-service through autonomous operation where the robot independently performs localization, path planning, and execution without human intervention. The system uses onboard sensors and processors to autonomously navigate to targets, eliminating the need for remote control interfaces that may be inaccessible to elderly or infirm users and enabling operation beyond human visual range
3Reliability
If traditional machine learning methods are used for robot training, then the robot can learn to approach objects in trained environments, but the training is overly limited to specific environments or contexts
Solution Approach 1:
The patent uses parameter changes by representing environments and objects through configurable parameters (sensor characteristics, object dimensions, material properties) rather than fixed training data. This allows the system to adapt to new environments by adjusting parameters rather than requiring extensive retraining, reducing training complexity while maintaining reliable performance
Data Source
AI summary
Systems and techniques are described that provide for generalizable approach policy learning and implementation for robotic object approaching. Described techniques provide fast and accurate approaching of a specified object, or type of object, in many different environments. The described techniques enable a robot to receive an identification of an object or type of object from a user, and then navigate to the desired object, without further control from the user. Moreover, the approach of the robot to the desired object is performed efficiently, e.g., with a minimum number of movements. Further, the approach techniques may be used even when the robot is placed in a new environment, such as when the same type of object must be approached in multiple settings.