Augmented Loader Controls for Autonomous Task Repetition
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Solution Overview
Problem
Existing power machines, particularly loaders, face challenges in efficiently performing repetitive tasks due to the need for repetitive manual control, which can be tiresome and inefficient.
Innovation Solution
Implementing augmented control systems that learn a series of machine operations through a learning mode, set a home position, and autonomously repeat these operations to complete tasks, utilizing controllers and positioning devices for precise navigation and obstacle avoidance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual control is used for repetitive tasks, then the operator can perform tasks with flexibility, but operator fatigue increases and efficiency decreases
Solution Approach 1:
The system enables self-service by allowing the power machine to autonomously perform repetitive tasks through automated control. The machine learns the task sequence through a learning mode and then executes it automatically without continuous human intervention, thereby reducing operator fatigue while maintaining task completion efficiency.
Solution Approach 2:
The system applies preliminary action by recording and storing the task sequence during a learning phase before actual task execution. The controller saves the series of operations in memory, allowing the machine to automatically replay and execute the recorded sequence without real-time human input, thus improving efficiency and reducing repetitive manual control.
2Productivity
If automated control is implemented, then task repetition efficiency improves, but system complexity increases
Solution Approach 1:
The system uses copying by creating a digital replica of the task sequence through the learning mode. The controller records the series of operations performed during learning and stores this copied sequence in memory. During automated execution, the machine replays this copied sequence, enabling efficient task repetition without requiring complex real-time decision-making systems.
3Manufacturing precision
If learning mode is used to record operations, then task accuracy improves, but time required for setup increases
Solution Approach 1:
The learning mode performs preliminary action by recording and storing the accurate task sequence in advance. Although this requires initial setup time, it enables highly accurate and consistent task execution during automated operation. The one-time learning process pays off through improved precision and repeatability in subsequent task executions.
Data Source
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AI summary
A method of performing a task using a power machine and a corresponding power machine are disclosed. The method comprises initiating a learning mode of a controller using a learning mode input; setting a home position for the power machine using a parameter input to provide the home position to the controller; while an operator controls the power machine to perform an iteration of a task, recording in memory associated with the controller positions, movements and/or functions of the power machine in performing the iteration of the task; terminating the learning mode; and controlling the power machine, using the controller, to autonomously repeat at least one additional iteration of the task.