Robotic Packing Error Detection With Dynamic Placement Control
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Solution Overview
Problem
Traditional robotic packing systems lack adaptability to real-time conditions and deviations, requiring pre-determined sequences and poses, which leads to inefficiencies and increased costs due to the need for sequence buffers and human intervention to handle errors and uncertainties.
Innovation Solution
A robotic system that dynamically derives object placement locations based on real-time conditions, using a discretization mechanism to transform continuous space into digital information, allowing for flexible and accurate placement without pre-defined sequences, and eliminating the need for sequence buffers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional robotic packing systems use pre-determined sequences and poses, then control precision is maintained, but adaptability to real-time conditions deteriorates
Solution Approach 1:
The system transitions from static pre-determined sequences to dynamic real-time derivation of placement locations. The robotic system continuously receives object data, derives placement locations on-the-fly, and adjusts packing sequences dynamically based on current conditions, eliminating the need for predetermined poses while maintaining precision through active control.
Solution Approach 2:
The system implements a feedback loop where the robotic system receives real-time object data from sensors, derives placement locations based on current packing state, executes placement, and uses this information to continuously update subsequent placement decisions. This closed-loop control maintains precision while enabling adaptability to changing conditions.
2Device complexity
If traditional systems eliminate sequence buffers, then device complexity is reduced, but reliability of packing sequence deteriorates
Solution Approach 1:
The robotic system autonomously derives placement locations and determines packing sequences without requiring external sequence buffers or human intervention. The system self-manages the packing sequence by continuously processing object data and generating placement instructions in real-time, eliminating the need for buffer storage while maintaining sequence reliability through automated control logic.
Solution Approach 2:
The system replaces the mechanical sequence buffer mechanism with a digital/computational approach. Instead of physically storing sequences in buffers, the system uses software-based real-time derivation of placement locations based on current packing state, substituting mechanical complexity with computational intelligence to maintain reliability.
3Reliability
If traditional robotic systems require human intervention for error handling, then reliability is improved, but productivity deteriorates
Solution Approach 1:
The robotic system autonomously detects and handles errors without human intervention. When deviations or uncertainties occur, the system automatically adjusts placement locations and sequences based on real-time object data, maintaining reliability through self-correcting control logic while preserving productivity by eliminating stops for human intervention.
Solution Approach 2:
The system maintains continuous packing operations without interruption for error handling. By implementing real-time error detection and automatic adjustment of placement locations, the system ensures uninterrupted productive action while maintaining reliability through continuous monitoring and adaptive control, eliminating the need to stop for human intervention.
4Manufacturing precision
If traditional systems use predetermined packing plans, then manufacturing precision is maintained, but adaptability to uncertainties deteriorates
Solution Approach 1:
The system replaces static predetermined packing plans with dynamic real-time derivation of placement locations. The robotic system continuously adapts placement decisions based on current object characteristics and packing state, maintaining precision through active control while enabling full adaptability to uncertainties in object size, shape, and arrival sequence.
Solution Approach 2:
The system dynamically changes placement location parameters based on real-time object data rather than following fixed predetermined plans. By adjusting placement coordinates, orientations, and sequences in real-time based on measured object parameters, the system maintains manufacturing precision while adapting to uncertainties in the packing process.
Data Source
AI summary
A method for operating a robotic system includes determining a discretized object model based on source sensor data; comparing the discretized object model to a packing plan or to master data; determining a discretized platform model based on destination sensor data; determining height measures based on the destination sensor data; comparing the discretized platform model and/or the height measures to an expected platform model and/or expected height measures; and determining one or more errors by (i) determining at least one source matching error by identifying one or more disparities between (a) the discretized object model and (b) the packing plan or the master data or (ii) determining at least one destination matching error by identifying one or more disparities between (a) the discretized platform model or the height measures and (b) the expected platform model or the expected height measures, respectively.