Robot Load Balancing via Spatial Region Task Servers
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Solution Overview
Problem
Existing robotic systems face inefficiencies in load balancing across task servers, leading to uneven task distributions and potential bottlenecks, which can result in reduced system efficiency and functionality.
Innovation Solution
A system and method for load balancing of robots using task servers that manage spatial regions, where tasks are represented as minimum bounding boxes and assigned to the most suitable task server based on spatial data structures and available resources, allowing for dynamic reassignment of tasks and robots to optimize workload distribution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If tasks are assigned to robots within a single task server's spatial region, then task execution efficiency is improved, but load balancing across multiple task servers deteriorates
Solution Approach 1:
The system divides the overall task management into multiple task servers, each responsible for a specific spatial region. Tasks are segmented and assigned to appropriate task servers based on spatial location, allowing parallel processing while maintaining regional efficiency. This resolves the contradiction by enabling both localized efficient execution and distributed load balancing across multiple servers.
Solution Approach 2:
The patent introduces a spatial dimension to task assignment by representing tasks as minimum bounding boxes in 3D space and using spatial data structures (octrees) to organize task servers by their managed regions. This spatial dimensionality allows the system to efficiently match tasks with appropriate task servers while distributing workload across the spatially-organized server network, achieving both efficiency and load balance.
2Ease of operation
If task servers manage fixed spatial regions, then task assignment simplicity is improved, but system adaptability to dynamic workloads deteriorates
Solution Approach 1:
The system implements dynamic spatial region management where task servers can expand or contract their managed regions based on workload conditions. When a task server becomes overloaded, it can transfer portions of its spatial region to neighboring servers. This dynamic adjustment maintains simple spatial-based assignment logic while adapting to changing workload demands, resolving the contradiction between assignment simplicity and system adaptability.
Solution Approach 2:
The patent incorporates feedback mechanisms where task servers continuously monitor their workload and spatial region utilization. Based on this feedback, servers dynamically adjust their regional boundaries and task assignment capacity. This feedback-driven adaptation allows the system to maintain simple spatial assignment rules while responding flexibly to dynamic workload changes.
3Measurement precision
If tasks are represented as minimum bounding boxes in spatial data structures, then task-spatial region matching precision is improved, but computational complexity increases
Solution Approach 1:
The system pre-processes and organizes task servers into hierarchical spatial data structures (octrees) before task assignment occurs. This preliminary spatial indexing allows for efficient query and matching operations when tasks are introduced. By performing the complex spatial organization in advance, the system achieves precise task-spatial region matching without incurring high computational complexity during actual task assignment.
Data Source
AI summary
A method for load balancing of robots includes: receiving, by a first task server configured to manage a first spatial region, a task to be performed by a robot; determining, by the first task server, that the task cannot efficiently be performed within the first spatial region; finding, by the first task server, a second task server configured to manage a second spatial region to which the task can be assigned; and sending, by the first task server, the task to the second task server.