Sensor Coverage Field Alignment for Narrow-Aisle Obstacle Detection
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Solution Overview
Problem
Material handling vehicles (MHVs) face challenges in efficiently detecting obstacles within narrow aisles due to limitations in sensor coverage fields, particularly when deviating from the intended path or using non-zero steering angles, leading to incomplete or unnecessary obstacle detection.
Innovation Solution
A system comprising a sensor and a controller that generates, receives, and transforms coverage fields based on the material handling vehicle's position relative to a guidance system, ensuring accurate obstacle detection by adjusting the sensor's field of view to match the vehicle's actual path and orientation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the sensor coverage field is fixed and does not adjust to vehicle position, then the device complexity is reduced, but the obstacle detection accuracy deteriorates when the vehicle deviates from the intended path
Solution Approach 1:
The coverage field is transformed dynamically based on the vehicle's actual position and orientation relative to the guidance system. The controller continuously adjusts the coverage field parameters (position, orientation, shape) to match the vehicle's current state, enabling accurate obstacle detection even when the vehicle deviates from the intended path. This dynamic adaptation resolves the contradiction by making the detection system responsive to changing conditions without requiring complex manual reconfiguration.
Solution Approach 2:
The system uses feedback from the vehicle's position characteristics (actual position, orientation, steering angle) to adjust the coverage field. The controller receives position data, compares it with the intended path, and transforms the coverage field accordingly. This feedback loop ensures that the coverage field remains aligned with the vehicle's actual trajectory, maintaining high detection accuracy while using a systematic approach to manage the transformation complexity.
2Reliability
If the sensor coverage field covers a wide area to detect all potential obstacles, then the obstacle detection coverage is improved, but false detection of extraneous objects increases
Solution Approach 1:
The coverage field is locally adapted to the vehicle's actual position and orientation, concentrating detection resources on the relevant area of interest. By transforming the coverage field to match the vehicle's current trajectory and steering angle, the system focuses detection on the actual path while reducing coverage of extraneous areas. This local quality approach ensures reliable obstacle detection on the path without detecting objects outside the relevant zone, eliminating false positives.
3Measurement precision
If the coverage field is transformed continuously based on vehicle position, then the obstacle detection accuracy is improved, but the processing time and computational load increase
Solution Approach 1:
The coverage field transformation is performed periodically at appropriate intervals during vehicle operation, rather than continuously without interruption. The controller transforms the coverage field based on updated position characteristics when available, balancing the need for accurate alignment with the computational time required. This periodic approach maintains sufficient detection accuracy while managing processing time and computational load effectively.
Data Source
AI summary
Systems and methods for generating a coverage field for obstacle detection. The system includes a sensor to detect obstacles. The system further includes a material handling vehicle guided by a guidance system and a controller. The controller is configured to: generate a coverage field for the sensor, receive position characteristics of the material handling vehicle, determine a position of the material handling vehicle relative to the guidance system based on the position characteristics, and transform the coverage field based on the determined position of the material handling vehicle.