Sensor Fusion Empty-Container Detection for Robotic Picking
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Solution Overview
Problem
In logistic operations, especially in warehouse environments, robotic picking devices require manual confirmation to determine if a container is empty, leading to inefficiencies and production halts due to the need for human intervention.
Innovation Solution
A system and method utilizing a sensor fusion module that processes weight, depth, and color data from multiple sensors to autonomously determine if a container is empty, including perturbation techniques to confirm the presence of items and generate confidence scores for accurate determination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual confirmation is used to determine container emptiness, then detection reliability is improved, but productivity deteriorates due to production halts and time consumption
Solution Approach 1:
The system enables the robotic picking device to automatically detect container emptiness using multiple sensors (weight, depth, color) and sensor fusion algorithms, eliminating the need for manual confirmation. The device serves itself by autonomously determining when containers are empty and managing container changes without human intervention.
Solution Approach 2:
The patent replaces the manual mechanical inspection process with an automated sensing system. Multiple sensors (weight sensors, depth sensors, color sensors) and computational algorithms substitute for human visual and physical inspection, enabling continuous automated operation.
2Productivity
If multiple sensors and sensor fusion are used to autonomously detect container emptiness, then productivity is improved by eliminating manual intervention, but device complexity increases
Solution Approach 1:
The patent combines multiple sensing modalities (weight, depth, color) into a unified sensor fusion module. This merging approach allows the system to leverage complementary information from different sensors, improving detection reliability while managing complexity through integrated processing.
Solution Approach 2:
The sensor fusion module serves multiple functions: it processes data from different sensor types, determines container emptiness, generates confidence scores, and triggers autonomous container management. This multi-functionality consolidates what would otherwise require separate systems into a single integrated component.
3Measurement precision
If perturbation techniques are used to confirm container contents, then measurement precision is improved, but loss of time increases due to additional verification steps
Solution Approach 1:
The system performs perturbation actions (shaking, tilting the container) proactively to redistribute contents before final detection. This preliminary action ensures that items are positioned in a way that maximizes sensor detection accuracy, preventing false negatives without requiring repeated inspection cycles.
Solution Approach 2:
The system uses feedback from confidence scores to determine whether perturbation is necessary. When confidence is low, perturbation is triggered to improve measurement precision. This feedback-based approach ensures perturbation is applied only when needed, minimizing time loss while maintaining high measurement precision.
Data Source
AI summary
Devices, systems, and methods for determining whether a container is empty in the context of robotic picking solutions. The system includes a plurality of sensors configured to gather container data regarding a container at a first location, wherein the container data includes at least two of weight data related to the container, depth data related to the container, and color sensor data related to the container, and a processor configured to execute instructions stored on a memory to provide a sensor fusion module configured to process the received container data to determine whether the container is empty.