On-Board AI Control for Heavy Machinery Tool Maneuvering
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Heavy machinery operations require skilled operators to manage both vehicle driving and tool maneuvering, leading to operator fatigue and inefficiencies, as existing systems lack effective automation for task prediction and execution.
Innovation Solution
An on-board artificial intelligence module, incorporating a neural network with long-short term memory layers, analyzes operator data to predict tasks and issue instructions for tool maneuvering, reducing operator workload and optimizing engine speed, while learning to perform tasks autonomously.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If skilled operators manually manage both vehicle driving and tool maneuvering, then task execution quality is maintained, but operator fatigue increases and operational efficiency decreases
Solution Approach 1:
The system segments the operator's tasks by separating vehicle driving control from tool maneuvering control. The AI module autonomously handles tool maneuvering while the operator focuses on vehicle driving, dividing the complex operation into manageable segments that reduce cognitive load and fatigue.
Solution Approach 2:
The AI module enables the heavy machinery to perform tool maneuvering tasks autonomously without continuous human intervention. The system learns from operator actions and independently executes tool control decisions, allowing the machinery to serve itself in routine operations while improving productivity.
2Productivity
If automation is introduced to reduce operator workload, then operational efficiency improves, but system complexity increases
Solution Approach 1:
The AI module acts as an intermediary between the operator and the heavy machinery's tool control systems. It processes sensor data, predicts operator intentions, and generates control commands, serving as a intelligent mediator that bridges human intent and machine execution while managing system complexity.
Solution Approach 2:
The system performs preliminary actions by continuously learning from operator behavior patterns and pre-computing optimal tool maneuvering strategies. The AI module anticipates operator needs and prepares control commands in advance, enabling smooth autonomous execution without adding operational complexity during actual tasks.
3Power
If cloud-based AI processing is used, then computational power is sufficient for complex tasks, but system response time and reliability decrease due to network dependency
Solution Approach 1:
The patent extracts the AI module from cloud-based processing and embeds it directly on the heavy machinery's onboard computer. This extraction eliminates network dependency, ensuring the system maintains full computational capabilities for complex AI tasks while achieving immediate local processing and enhanced reliability without external connectivity.
Data Source
AI summary
A system of this disclosure includes an artificial intelligence module, which may include a neural network or a decision tree architecture, configured to analyze data indicative of the manner in which an operator performs tasks using a heavy machine. The artificial intelligence module is further configured to provide instructions pertaining to the control of at least some components of the heavy machine. As such, the heavy machine is operated in whole or in part based on the direction of the artificial intelligence module, which reduces reliance on a human operator. The artificial intelligence module is highly efficient, and in particular the artificial intelligence module is trained relatively quickly. Further, the artificial intelligence module may be embodied on the heavy machinery itself, as opposed to on a cloud-based system or on a more high-powered computer. Accordingly, the cost of implementing and operating the disclosed system is relatively low.