OMPLS Scheduling for Multi-Robot Task Allocation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing multi-robot systems face challenges in efficiently scheduling non-preemptive tasks with deadlines and performance loss considerations, leading to tasks missing deadlines and incurring significant penalties, especially in dynamic and complex environments.
Innovation Solution
The implementation of the Online Minimum Performance Loss Scheduling (OMPLS) technique, which prioritizes tasks based on their performance loss values and deadlines, merges tasks, and iteratively adjusts priorities to minimize overall performance loss by delaying lower-priority tasks that may miss deadlines, ensuring optimal task allocation across a team of homogeneous robots.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional scheduling methods are used in multi-robot systems, then task allocation can be performed, but tasks miss deadlines and incur significant performance loss
Solution Approach 1:
The patent changes the scheduling parameter from simple deadline-based or FIFO ordering to performance loss-based priority ordering. By calculating the performance loss function for each task and scheduling tasks in descending order of performance loss, the system dynamically adjusts scheduling parameters to minimize total performance loss while improving deadline meeting rates in multi-robot environments.
2Productivity
If more tasks are assigned to robots, then productivity increases, but scheduling complexity and difficulty of detecting and measuring optimal allocation increases
Solution Approach 1:
The patent implements a self-service scheduling mechanism where each robot independently calculates its own task priority based on the performance loss function and autonomously selects tasks from the task queue. This eliminates the need for complex centralized scheduling optimization and makes the system scalable to handle increasing numbers of tasks and robots without proportionally increasing scheduling complexity.
3Reliability
If tasks are scheduled strictly by deadline, then deadline adherence improves, but overall performance loss increases due to ignoring performance loss variations
Solution Approach 1:
The patent applies local quality by differentiating task scheduling based on individual task characteristics. Instead of uniform deadline-based scheduling, each task is assigned a local priority level according to its specific performance loss function value. Tasks with higher performance loss values receive higher priority, allowing the system to adapt scheduling decisions to local task requirements and minimize overall performance loss.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
Systems and methods for optimizing scheduling of non-preemptive tasks in a multi-robot environment are provided. Traditional systems and methods cite scheduling of preemptive task(s) allocation but such scheduling techniques generally do not provide for an efficient scheduling in the multi-robot environment since tasks are preemptive. Additionally, critical parameters like deadline and performance loss are not considered. Embodiments of the present disclosure provide for optimizing the scheduling of non-preemptive tasks in the multi-robot environment by defining a plurality of tasks; merging the plurality of tasks; scheduling, by implementing an Online Minimum Performance Loss Scheduling (OMPLS) technique, initially, tasks with a higher performance loss value and then secondly, tasks that can be scheduled within their deadline and having a low performance loss value amongst the merged tasks; and finally minimizing, a performance loss value of a remaining subset of tasks that cannot be scheduled within a pre-defined deadline.