Mobile Robot Grasping With Semantic Navigation and Pose Estimation
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Solution Overview
Problem
Existing robotic systems lack flexibility and efficiency in grasping tasks due to limited object recognition, navigation, and grasping planning, particularly in dynamic environments, and do not effectively integrate mobile platforms for improved grasping capabilities.
Innovation Solution
A robotic system integrating a mobile platform with a semantic navigation system, object recognition and pose estimation, and a mobile grasping controller to enhance grasping success rates by combining semantic information, object pose estimation, and grasp planning, allowing simultaneous motion of the robotic arm and mobile platform for efficient grasping.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a robotic arm is mounted on a mobile platform to improve grasping flexibility, then the adaptability and versatility of the system is improved, but the device complexity increases due to the need for coordinated control of multiple components
Solution Approach 1:
The patent combines the robotic arm and mobile platform into an integrated mobile robotic system, where the robotic arm is mounted on the mobile platform. This merging allows the system to simultaneously achieve navigation capabilities and grasping functionality, improving adaptability while managing complexity through unified system architecture.
Solution Approach 2:
The mobile robotic system is designed to perform multiple functions: navigation to target locations, object recognition and pose estimation, and grasping operations. The robotic arm can grasp various types of objects, and the system can operate in different environments, enhancing versatility through multi-functionality.
2Manufacturing precision
If semantic information and object pose estimation are integrated to improve grasping accuracy, then the manufacturing precision of grasping operations is improved, but the loss of time increases due to additional processing requirements
Solution Approach 1:
The system performs semantic segmentation and pose estimation of the target object before the robotic arm executes the grasping operation. By preparing the grasp plan in advance based on preprocessed image data and estimated pose, the system achieves high grasping accuracy while minimizing the time required during the actual grasping execution.
Solution Approach 2:
The patent replaces traditional mechanical positioning methods with vision-based pose estimation and semantic segmentation. Using image processing algorithms to determine object pose and generate grasp plans reduces the need for complex mechanical measurement and positioning systems, improving accuracy while managing processing time through computational efficiency.
3Productivity
If the robotic arm and mobile platform move simultaneously to reach the grasping position to improve productivity, then the productivity of the system is improved, but the device complexity increases due to coordinated motion control requirements
Solution Approach 1:
The system employs dynamic coordinated control where the mobile platform and robotic arm move simultaneously and adaptively. The control system adjusts the motion of both components in real-time based on the target position and grasp requirements, allowing efficient reaching of the grasping position while managing control complexity through dynamic coordination algorithms.
Solution Approach 2:
The system uses feedback from the semantic navigation system and object recognition to coordinate the motion of the mobile platform and robotic arm. By continuously monitoring the system state and adjusting commands based on feedback, the system achieves efficient simultaneous movement while managing control complexity through closed-loop control.
Data Source
AI summary
A robot and method for autonomously moving and grasping objects are provided. The robot includes a robotic arm for grasping a target object; a mobile platform for moving the robotic arm to the target object's location; a semantic navigation system for navigating the mobile platform to the target object's location; a first camera for shooting the external environment during navigation; a second camera for obtaining relative images of the environment for the robotic arm; an object recognition and pose estimation system for performing semantic segmentation and pose estimation of the target object; an automatic docking coordination controller for obtaining the best mobile grasping path and pose for the robotic arm and the mobile platform; a mobile grasping controller for controlling the movement of the robotic arm through the object recognition and pose estimation system; and a mobile platform controller for controlling the motion of the robot.