Mining Machine Task Assignment for Concurrent Collision-Free Scheduling
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Solution Overview
Problem
Assigning tasks to multiple autonomous or manually operated mining and construction machines is challenging due to the complexity of coordinating their motions, avoiding collisions, and ensuring efficient task completion within geographical constraints, especially when dealing with a large number of tasks and machines operating concurrently.
Innovation Solution
A method that divides the task assignment problem into sub-problems, using constraint satisfaction techniques to find solutions that satisfy higher-level constraints, allowing for concurrent task execution by multiple machines, with prioritization and heuristically guided backtracking search to optimize task scheduling and machine coordination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If task assignment is solved as a single complex optimization problem, then solution quality may be improved, but computational complexity and processing time increase significantly
Solution Approach 1:
The patent divides the complex task assignment problem into multiple sub-problems (motion planning, task allocation, coordination) that can be solved separately and then integrated. This segmentation reduces computational complexity while maintaining solution quality through iterative validation of sub-solutions against the overall problem constraints.
2Productivity
If multiple machines operate concurrently to improve productivity, then task completion speed increases, but coordination complexity and collision risk increase
Solution Approach 1:
The patent introduces a central coordination system that acts as an intermediary between multiple autonomous machines. This mediator receives task assignments, generates motion plans, and coordinates machine operations to ensure safe concurrent execution while maintaining high productivity through parallel task completion.
3Productivity
If autonomous operation is implemented to reduce manual intervention, then operational efficiency improves, but system complexity and validation difficulty increase
Solution Approach 1:
The patent enables machines to autonomously perform task execution and self-monitoring without continuous manual intervention. The system automatically validates task completion, reports progress, and adjusts operations based on real-time conditions, reducing operational complexity through automated self-management while maintaining high efficiency.
4Reliability
If detailed constraints are enforced to ensure safety and feasibility, then solution reliability improves, but computational processing time increases
Solution Approach 1:
The patent performs preliminary validation of constraints and feasibility checks during the task assignment phase rather than during execution. By pre-processing constraint validation, the system ensures solution reliability while reducing real-time computational overhead and processing time during actual task execution.
Data Source
AI summary
A method for assigning a set of tasks to a plurality of mining machines, said machines being arranged to be driven between positions for performing tasks is provided. The method includes defining a first set of constraints to be fulfilled finding an overall solution fulfilling the first set of constraints by solving each constraint as a sub-problem, a solution to at least one sub-problem being dependent on the solution of at least one other sub-problem; the overall solution fulfilling the first set of constraints being an overall solution where a first of said sub-problems is validated by solutions of others of the sub-problems, and assigning tasks to the machines according to the determined validated overall solution, wherein tasks are arranged to be carried out at least partially overlapping in time by the mining machines.