Scraper Work Planning Using Route and State Transition Control
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Solution Overview
Problem
Conventional earth working machines lack efficient systems for generating customized work plans that integrate route planning, work state transitions, and performance optimization, leading to suboptimal operation and resource inefficiencies.
Innovation Solution
A method and system for generating and implementing customized work plans for self-propelled earth working machines, which include loading, transporting, and unloading operations, by using performance optimization data sets and design plans to control working parameters such as route, work state transitions, and resource allocation based on real-time data and user inputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional earth working machines operate without integrated work planning systems, then device complexity is reduced, but productivity and operational efficiency deteriorate
Solution Approach 1:
The work planning system is segmented into distinct functional modules: route planning module, work state transition module, and performance optimization module. Each module handles specific aspects of operation control, allowing the complex system to be managed through independent, specialized components that can be developed and maintained separately while working together to improve overall productivity
Solution Approach 2:
The system performs preliminary actions by pre-planning routes and work states before actual earth moving operations begin. The controller generates optimized work plans in advance, determining the sequence of operations, transport routes, and state transitions beforehand, which allows operations to execute efficiently without real-time decision delays
2Productivity
If customized work plans with performance optimization data are implemented, then productivity improves, but device complexity and data processing requirements worsen
Solution Approach 1:
The system utilizes parameter changes by incorporating performance optimization data sets that contain loading capacities, loading rates, and working parameters for different earth working conditions. The controller dynamically adjusts operational parameters based on these data sets and real-time sensor feedback, optimizing work cycles without requiring overly complex system architecture
Solution Approach 2:
The system implements feedback mechanisms where sensors monitor actual working parameters, loading rates, and machine states, and this information is fed back to the controller. The controller compares actual performance against the performance optimization data sets and adjusts work plans accordingly, creating a closed-loop system that improves productivity through continuous optimization rather than static complexity
3Manufacturing precision
If automated control of working parameters is implemented, then manufacturing precision and work plan adherence improve, but ease of operation deteriorates
Solution Approach 1:
The earth working machine implements self-service through automated control where the controller autonomously manages working parameters, state transitions, and route following based on the generated work plan. The system serves itself by making real-time decisions about when to load, transport, unload, and return without requiring constant operator intervention, thereby ensuring precise adherence to optimized work plans while reducing operational complexity
Data Source
AI summary
A system and method are provided for controlling operation of earth working machines, e.g., scraper units configured to load, transport, and unload material depending on the respective work state. A design plan (e.g., a cut-fill map) is obtained corresponding to a working area to which the earth working machines are assigned. For each of the machines, the method further includes generating and/or selectively retrieving performance optimization data sets comprising a loading capacity and loading rates correlated to combinations of input data for working parameters for the respective machine, generating a work plan comprising a route of advance and corresponding work state transitions of the machine with respect to the working area, wherein the work plan is generated based at least in part on the performance optimization data sets and the design plan, and automatically controlling working parameters for the machine in accordance with the generated work plan.