Multi-Robot Task Scheduling via Performance Loss Partitioning

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Solution Overview

Problem

Current multi-robot systems face challenges in efficiently scheduling non-preemptive tasks with deadlines and performance loss considerations, particularly in dynamic environments like warehouses, where tasks with varying execution times and performance losses are not optimally managed, leading to missed deadlines and increased performance losses.

Innovation Solution

A method and system for scheduling non-preemptive tasks in a multi-robot environment that partitions tasks into schedulable and non-schedulable sets based on performance loss values, using a compaction mechanism and list-scheduling technique to assign tasks to robots, optimizing task execution and minimizing performance losses by sorting tasks by performance loss values and implementing load balancing.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If traditional scheduling methods are used in multi-robot systems, then the system can handle simple task allocation, but it cannot optimally manage tasks with varying execution times and performance losses, leading to missed deadlines and increased performance losses

Engineering Contradiction:

Improvedeadline completion rateVSAvoidscheduling system complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent segments the scheduling problem into distinct phases: task classification based on performance loss thresholds, schedulability analysis, and hierarchical scheduling strategies. This segmentation allows the system to handle complex scheduling decisions through manageable sub-problems, improving deadline completion rates without overwhelming system complexity

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent introduces performance loss values as a key parameter to prioritize tasks, and dynamically adjusts scheduling decisions based on task characteristics (execution time, deadline, performance loss). By changing the parameter set used for scheduling decisions, the system optimizes deadline completion while managing complexity through parameter-based task differentiation

Inventive Principle:

Principle #35Parameter changes

2Loss of energy

If tasks are prioritized by performance loss values, then performance losses are minimized and deadline misses are reduced, but the scheduling complexity increases due to task partitioning and compaction mechanisms

Engineering Contradiction:

Improveperformance lossVSAvoidscheduling mechanism complexity

Core Design Contradiction:

Loss of energyVSDevice complexity

Solution Approach 1:

The patent performs preliminary task classification and schedulability analysis before actual scheduling. By pre-categorizing tasks into schedulable and non-schedulable sets based on performance loss values and execution characteristics, the system minimizes performance losses through informed prioritization while reducing online scheduling complexity through offline preparation

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent introduces a compaction mechanism as an intermediary layer between task classification and final scheduling. This compaction step optimizes the arrangement of schedulable tasks to maximize resource utilization and minimize performance loss, acting as a mediator that resolves the trade-off between scheduling complexity and performance optimization

Inventive Principle:

Principle #24Intermediary (Mediator)

3Productivity

If a compaction mechanism is implemented to schedule schedulable tasks, then task execution efficiency is improved and performance loss is minimized, but the computational overhead and scheduling time increase

Engineering Contradiction:

Improvetask execution efficiencyVSAvoidscheduling computation time

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The patent applies compaction selectively to schedulable tasks rather than all tasks, and uses iterative compaction that stops when optimality is achieved or computational resources are exhausted. This partial application of compaction maintains task execution efficiency while controlling computational overhead by avoiding exhaustive optimization in all cases

Inventive Principle:

Principle #16Partial or excessive action

4Reliability

If the system iteratively identifies and schedules non-schedulable tasks, then all tasks are eventually completed, but the overall makespan increases due to iterative processing

Engineering Contradiction:

Improvetask completion rateVSAvoidmakespan

Core Design Contradiction:

ReliabilityVSDuration of action of moving object

Solution Approach 1:

The patent performs preliminary schedulability analysis to identify non-schedulable tasks before the main scheduling process. By pre-identifying tasks that cannot be scheduled within deadlines, the system can allocate them separately with appropriate resources, ensuring complete task completion while minimizing the impact on overall makespan through advance planning

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS10929182B2Systems and methods for scheduling a set of non-preemptive tasks in a multi-robot environment

Publication Date: 2021.02.23 TATA CONSULTANCY SERVICES LTD

AI summary

System and method is provided for scheduling of a set of non-preemptive tasks by partitioning, the set of non-preemptive tasks either as a set of schedulable tasks or as a set of non-schedulable tasks; sorting, by a scheduling technique, the set of non-preemptive tasks partitioned; determining, by the scheduling technique, a possibility of execution of each of the set of schedulable tasks; and scheduling the set of schedulable tasks and the set of non-schedulable tasks upon determining the possibility of execution of each of the set of schedulable tasks.