AGV Path Control Using 3D Cargo Shape and Obstacle Distance
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Solution Overview
Problem
Conventional automated guided vehicles (AGVs) face challenges in stably moving loaded cargo due to the need for manual adjustment of paths based on cargo shape and size, lacking an efficient method to dynamically adapt movement control to varying cargo dimensions and environmental obstacles.
Innovation Solution
An information processing apparatus that acquires three-dimensional cargo shape information and environmental data to determine control values for the AGV, ensuring it maintains a safe distance from obstacles, using sensors like Time-of-Flight cameras and depth mapping to calculate optimal movement paths.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual path setting is used to maintain suitable distance from obstacles, then movement stability is improved, but operation efficiency deteriorates due to repeated manual adjustments for different cargo
Solution Approach 1:
The AGV performs self-measurement of cargo dimensions using onboard sensors and automatically calculates its own movement path, eliminating the need for manual path setting. The system serves itself by acquiring cargo shape information, determining vehicle contour, and computing avoidance paths autonomously
Solution Approach 2:
The system performs preliminary measurement of cargo dimensions before movement begins, and pre-calculates the movement path based on the vehicle's contour including cargo. This advance preparation eliminates the need for repeated manual adjustments during operation
2Ease of operation
If fixed movement paths are used, then operation simplicity is improved, but adaptability deteriorates when cargo shape or environment changes
Solution Approach 1:
The movement path is dynamically determined based on the actual cargo dimensions and environmental obstacles. The system calculates the vehicle's contour including cargo and generates avoidance paths that adapt to each specific cargo configuration and environmental condition, rather than following fixed predetermined paths
Solution Approach 2:
The automatic path determination system handles multiple functions: measuring cargo dimensions, calculating vehicle contour, detecting environmental obstacles, and generating avoidance paths. This universal system replaces multiple specialized manual operations for different cargo types and environments
3Measurement precision
If cargo dimensions are measured manually, then measurement accuracy is improved, but time consumption increases
Solution Approach 1:
The system replaces manual mechanical measurement with optical sensors (cameras, laser scanners) that automatically capture cargo dimensions. The sensor-based measurement system maintains accuracy while eliminating the time-consuming manual measurement process
Solution Approach 2:
The system creates a digital copy or model of the cargo's three-dimensional shape using sensor data. This virtual representation is then used to calculate the vehicle's contour and determine movement paths, replacing physical manual measurement with digital modeling
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables stable and safe movement control of AGVs loaded with varying cargo, reducing manual intervention and preventing collisions with obstacles by dynamically adjusting movement paths based on real-time cargo and environmental data.
Implementation Method 1
using sensors like Time-of-Flight cameras and depth mapping to calculate optimal movement paths
Data Source
AI summary
An information processing apparatus for determining control values for controlling a position of a vehicle for conveying a cargo includes an acquisition unit configured to acquire first information for identifying a three-dimensional shape of the cargo based on a captured first image of the cargo, and second information for identifying, based on a captured second image of an environment where the vehicle moves, a distance between an object in the environment and the vehicle, and a determination unit configured to, based on the first information and the second information, determine the control values for preventing the cargo and the object from coming closer than a predetermined distance.