Mobile Robot Fleet Task Assignment Using Actual Path Lengths
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Solution Overview
Problem
Existing mobile robot management systems face inefficiencies in task assignment due to reliance on Euclidean distances, which do not account for obstacles and navigation rules, leading to delayed task assignment and incomplete path length databases.
Innovation Solution
A mobile robot fleet management system that communicates with a fleet of robots, storing path length information in a database and assigning tasks based on actual path lengths, using a processor to select robots with the shortest paths and update the database with actual path lengths when discrepancies exceed a threshold.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If Euclidean distance is used for task assignment, then task assignment is simple and fast, but path accuracy deteriorates because obstacles and navigation rules are not accounted for
Solution Approach 1:
The system pre-calculates and stores path length information in a database before task assignment occurs. Mobile robots perform pathfinding operations in advance to populate the database with accurate path lengths that account for obstacles and navigation rules, so that task assignment can quickly retrieve pre-computed values without performing complex calculations in real-time.
Solution Approach 2:
The system maintains a database buffer that stores path length information for multiple locations and robots. This pre-stored information acts as a cushion that allows the task assignment system to make accurate decisions without needing to perform time-consuming path calculations at the moment of assignment, thus maintaining both speed and accuracy.
2Measurement precision
If actual path lengths are calculated for all robot locations, then path accuracy improves, but system complexity and computation time increase
Solution Approach 1:
Path length calculations are performed in advance and stored in a database. The system proactively computes path lengths for various robot locations and task locations before they are needed for task assignment, converting a potentially complex real-time calculation problem into a simple database lookup operation.
Solution Approach 2:
Instead of performing complex pathfinding calculations during task assignment, the system creates a copy of the path length information in a database. This copied data can be quickly retrieved and used for multiple task assignments without repeating the computationally intensive pathfinding process.
3Measurement precision
If path length database is continuously updated with actual values, then database accuracy improves, but data processing time and system overhead increase
Solution Approach 1:
The system implements a feedback mechanism where mobile robots report their actual path lengths after completing navigation tasks. The task assignment system compares these actual values with expected values from the database and updates the database when discrepancies are detected, continuously improving accuracy based on real-world performance data.
Solution Approach 2:
The system does not update the entire database continuously, but rather performs partial updates only when actual path length measurements differ from expected values by more than a threshold. This selective updating approach maintains database accuracy while minimizing processing overhead.
Data Source
AI summary
A mobile robot management system can receive path length values from a fleet of mobile robots that navigate an environment. The system can populate a database with the path length values. When assigning tasks to the robots, the system can use the task locations and the mobile robot locations to look up path length values in the database, and the system can use those path length values to select which mobile robot to select for the task.