Robotic Front Loading With Adaptive Grabber Pads for Large Objects
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Solution Overview
Problem
Existing robotic systems face challenges in effectively capturing and containing diverse objects, such as large, deformable, or small objects, due to their geometry and mobility limitations, leading to inefficiencies in object isolation, pickup, and deposition.
Innovation Solution
A robotic system equipped with grabber pad arms, a shovel, and a control system that employs reinforcement learning and rules-based strategies to determine optimal object isolation, pickup, and deposition strategies, using sensor data and object attributes to adapt to various object types and environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a robot uses a simple containment area to capture objects, then the device complexity is reduced, but the ability to securely capture diverse objects (large, deformable, small) deteriorates
Solution Approach 1:
The containment area employs movable grabber pad arms with adjustable positions and orientations, allowing the structure to dynamically adapt to different object geometries. The grabber pads can extend, retract, and reposition to securely capture large, deformable, or small objects without requiring a complex fixed structure.
Solution Approach 2:
The system changes physical parameters of the containment area by adjusting grabber pad positions, arm extensions, and pressure forces based on detected object attributes. This allows the same simple structure to effectively capture diverse objects by modifying its configuration parameters rather than requiring multiple specialized structures.
2Device complexity
If the robot uses a single unified pickup strategy for all objects, then the control system complexity is reduced, but the effectiveness of capturing diverse object types deteriorates
Solution Approach 1:
The control system applies different pickup strategies tailored to specific object types detected by sensors. Instead of a single unified approach, the system selects from multiple strategies (e.g., gentle capture for small objects, compression for deformable objects, secure positioning for large objects) based on local object characteristics, improving capture reliability without requiring overly complex global control.
Solution Approach 2:
The control system dynamically selects and adjusts pickup strategies based on real-time sensor data and object attributes. This adaptive approach allows the robot to respond to diverse object types with appropriate strategies, maintaining reliability while keeping the control architecture manageable through rule-based or reinforcement learning frameworks.
3Speed
If the robot captures small lightweight objects with initial contact, then the capture speed is improved, but the objects scatter or roll away deteriorating capture reliability
Solution Approach 1:
Before making initial contact with small lightweight objects, the robot uses sensors to detect object properties and pre-positions the grabber pads with appropriate force and orientation. This preliminary preparation ensures that when capture occurs, the objects are immediately secured without scattering or rolling, maintaining both speed and reliability.
Solution Approach 2:
The system adjusts capture parameters such as grabber pad force, contact point, and closure speed based on detected object characteristics. For small lightweight objects, the robot applies gentler, more precisely controlled forces that prevent scattering while maintaining rapid capture, resolving the contradiction between speed and reliability.
4Productivity
If the robot deposits objects quickly without organization, then the productivity is improved, but the objects become dispersed and disorganized deteriorating the quality of deposition
Solution Approach 1:
The deposition process is segmented into different handling modes based on object type and destination. The robot can perform rapid dumping for homogeneous groups of objects while switching to organized placement for valuable or sensitive items. This segmentation allows high productivity for routine tasks while maintaining organization when needed, without requiring the system to be slow for all operations.
Data Source
AI summary
A method and system are herein disclosed wherein a robot handles objects that are large, unwieldy, highly-deformable, or otherwise difficult to contain and carry. The robot is operated to navigate an environment and detect and classify objects using a sensing system. The robot determines the type, size and location of objects and classifies the objects based on detected attributes. Grabber pad arms and grabber pads move other objects out of the way and move the target object onto the shovel to be carried. The robot maneuvers objects into and out of a containment area comprising the shovel and grabber pad arms following a process optimized for the type of object to be transported. Large, unwieldy, highly deformable, or otherwise difficult to maneuver objects may be managed by the method disclosed herein.