Work Machine Object Detection with Depth Manifolds for Movable Implements
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Solution Overview
Problem
Conventional object detection systems in work machines struggle to differentiate between work implements and external objects, leading to undesirable alerts and reduced situational awareness for operators.
Innovation Solution
A method and system that generate a multidimensional manifold representing the structure of a work implement within a perception field, allowing the system to disregard depth data corresponding to the implement, thereby separating it from external objects and reducing false positive detections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the perception system detects all objects in the field of view, then detection completeness is improved, but false positive alerts increase due to work implements being detected as obstacles
Solution Approach 1:
The perception field is segmented into multiple depth ranges, with each range corresponding to a specific work implement position. The system divides the detection space into zones (e.g., first depth range for rearmost position, second depth range for forward position) and applies different evaluation criteria to each segment, allowing work implements to be distinguished from actual obstacles based on their depth position.
Solution Approach 2:
The system pre-stores depth data representing the work implement at various positions in the field of view before operation. During detection, this pre-stored data is compared against current sensor data to identify and filter out work implement detections before they can trigger false alerts, enabling the system to anticipate and prevent false positives.
2Object-generated harmful factors
If the system filters out work implement detections, then false positive alerts are reduced, but situational awareness of actual external objects may be compromised
Solution Approach 1:
Different quality control measures are applied to different regions of the perception field. Work implement regions (identified through depth data matching) receive filtering treatment to eliminate false positives, while other regions maintain full detection sensitivity. This localized approach ensures that situational awareness is preserved for actual external objects while suppressing alerts only in areas where work implements are expected.
Data Source
AI summary
A work method and method provide intervention feedback based on spatially proximate objects in a work area. The work machine includes perception sensors and a work implement. During a calibration stage, the implement is moved to various positions across an available range of movement, and depth data are provided for each position and for each of various perception field portions with respect to objects within a perception field. For each position, a multidimensional manifold is created and stored comprising depth data associated with the work implement for the perception field portions including the work implement. During a machine operation stage, further perception inputs are used to determine current depth data for the perception field portions, and feedback signals are conditionally generated corresponding to an intervention event state which is determined while ignoring any objects identified as corresponding to the multidimensional manifold corresponding to a current position of the work implement.