Work Machine Proximity Control via Dynamic Thresholds
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Solution Overview
Problem
Existing work machines, such as hydraulic excavators, face inefficiencies in work efficiency due to frequent control interventions when operating near intrusion prohibition regions, leading to potential collisions and decreased productivity.
Innovation Solution
A work machine equipped with posture sensors, actuators, and a controller that computes proximity to intrusion prohibition regions and adjusts operating area limiting control to prevent intrusions, using history data to alter proximity thresholds and reduce unnecessary deceleration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If operating area limiting control is executed frequently to prevent intrusion into prohibition regions, then safety is improved, but work efficiency deteriorates due to frequent deceleration
Solution Approach 1:
The degree-of-proximity threshold is made dynamic rather than fixed. The controller automatically adjusts the threshold based on history data about the degree of proximity, allowing the safety margin to adapt to the actual work conditions. This resolves the contradiction by enabling frequent control intervention when necessary for safety while reducing unnecessary intervention when the work pattern is stable and predictable
Solution Approach 2:
The system implements feedback control by storing history data about the degree of proximity and using this information to alter the degree-of-proximity threshold. The controller continuously monitors the proximity between the work device and prohibition regions, learns from past patterns, and adjusts the threshold accordingly. This feedback mechanism ensures safety is maintained while avoiding excessive control intervention that would reduce work efficiency
2Productivity
If the degree-of-proximity threshold is set low to reduce control intervention, then work efficiency is improved, but the risk of intrusion into prohibition regions increases
Solution Approach 1:
The threshold is dynamically adjusted based on learned work patterns rather than being set to a conservative fixed value. When the system learns that certain work patterns are safe and repeatable, it can temporarily lower the threshold or extend the time without intervention, improving efficiency. Conversely, when potential risks are detected in the history data, the threshold is raised to ensure safety
Solution Approach 2:
The system performs self-learning and self-adjustment of the degree-of-proximity threshold based on its own operation history. By analyzing stored history data about proximity patterns, the controller automatically optimizes the threshold setting without requiring external intervention or manual tuning, balancing safety and efficiency adaptively
Data Source
AI summary
A work machine includes a plurality of actuators that drive a work device; a posture sensor that senses postural data about the work device; and a controller having a degree-of-proximity calculating section that computes a degree of proximity that is an index value indicating proximity between an intrusion prohibition region and the work device on the basis of positional data about the intrusion prohibition region and the postural data. A command section executes, when the proximity specified by the degree of proximity is closer than proximity specified by a degree-of-proximity threshold, operating area limiting control to decelerate at least one of the plurality of actuators such that an intrusion of the work device into the intrusion prohibition region is prevented. History of the data about the degree of proximity calculated at the degree-of-proximity calculating section is stored and the degree-of-proximity threshold is altered on the basis of the history data.