Pallet State Estimation Using Vision and Geometric Models
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Solution Overview
Problem
Current robotic systems struggle to efficiently palletize and depalletize heterogeneous items due to variations in item size, weight, and type, leading to instability and inefficiency in handling and storage.
Innovation Solution
A robotic system that combines geometric data and sensor data to estimate the state of a pallet or stack of items, allowing for precise planning and execution of item placement and removal, thereby enhancing stability and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If robotic systems are used to palletize heterogeneous items, then productivity is improved, but stability and reliability deteriorate due to variations in item size, weight, and type
Solution Approach 1:
The system performs preliminary actions by using vision systems to capture images and geometric models to predict the state of items on the pallet before actual placement. This allows the robotic system to plan and anticipate stability issues in advance, adjusting placement strategies proactively to maintain reliability while achieving high productivity.
2Measurement precision
If geometric data and sensor data are combined to estimate pallet state, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system introduces a geometric model as an intermediary that bridges sensor data and pallet state estimation. The geometric model serves as a mediator that translates and integrates complex sensor readings into meaningful spatial relationships and item positions, improving measurement precision while managing system complexity through this intermediate representation layer.
3Reliability
If human workers manually stack items based on judgment and intuition, then stability is improved, but productivity deteriorates
Solution Approach 1:
The system implements feedback mechanisms where vision systems continuously monitor the pallet state, geometric models predict stability outcomes, and robotic systems adjust placement decisions in real-time based on this feedback loop. This allows automated systems to achieve human-level stability judgment while maintaining high productivity, as the feedback enables adaptive decision-making without manual intervention.
Data Source
AI summary
A robotic system is disclosed. The system includes a communication interface that receives, from a sensor(s) deployed in a workspace, sensor data indicative of a current state of the workspace, the workspace comprising a pallet or other receptacle and a plurality of items stacked on or in the receptacle. The system includes one or more processors that control a robotic arm to place a first set of items on or in, or remove the first set of items from, the pallet or other receptacle, update a geometric model based on the first set of items placed on or in a receptacle, use the geometric model in combination with the sensor data to estimate a stack of one or more items on or in the receptacle, and use the estimated state to generate or update a plan to control the robotic arm to place a second set of items.