Autonomous Worksite Control System with Real-Time Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems for controlling autonomous worksites, such as mining sites, fail to optimize key indices like productivity, efficiency, and profitability due to lack of real-time adaptation and inability to override control decisions, leading to suboptimal performance and inefficiencies.
Innovation Solution
A control system that collects information on autonomous worksite indices, builds optimization models using constraint models, determines control variables, and makes decisions to optimize these indices, allowing for real-time updates and user override.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a control system uses predefined project modules to direct participant behavior, then the work process can be modeled and coordinated, but the system cannot optimize indices like productivity, efficiency, and profitability in real-time
Solution Approach 1:
The control system dynamically adjusts control decisions in real-time based on current worksite conditions, participant status, and index optimization goals. The system transitions from static predefined modules to dynamic adaptive control that continuously responds to changing conditions, enabling real-time optimization of productivity while maintaining coordination through updated project modules.
Solution Approach 2:
The system implements feedback mechanisms that continuously monitor worksite indices, participant behavior, and system performance. This feedback loop enables the control system to evaluate the effectiveness of current control decisions and adjust future decisions to optimize productivity, efficiency, and profitability while adapting to real-time changes in the worksite environment.
2Extent of automation
If the control system makes automated decisions without override capability, then automation extent increases, but the system lacks flexibility when human intervention is needed
Solution Approach 1:
The system implements partial automation where the control system makes automated control decisions that can be overridden by users when necessary. This partial action approach maintains high automation for routine optimization while preserving human intervention capability for exceptional cases, balancing automated control with user authority and operational flexibility.
3Device complexity
If the control system uses static project modules, then device complexity is reduced, but the system cannot update decisions in real-time response to dynamic worksite conditions
Solution Approach 1:
The control system uses dynamic project modules that can be generated and updated in real-time based on current worksite conditions and optimization goals. This dynamic approach maintains relatively simple system structure while enabling rapid adaptation and real-time decision updates, resolving the contradiction between structural simplicity and adaptive speed.
Data Source
AI summary
A control system is disclosed for providing a control decision to an autonomous worksite. The control system may include a communication interface configured to collect information related to an index of the autonomous worksite. The control system may also include a storage device configured to store the collected information and a plurality of constraint models. Each constraint model may characterize the mathematical relationship between the index and at least one control variable. The control system may further include a processor coupled to the communication interface and the storage device. The processor may be configured to build an optimization model for optimizing the index, based on the plurality of constraint models and the collected information. The processor may be further configured to determine the at least one control variable associated with the autonomous worksite by solving the optimization model. The processor may also be configured to make a control decision based on the determined control variable, and provide the control decision to the autonomous worksite.