Autonomous Mobile Robot Fleet Composition for Low-Cost Path Planning
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Solution Overview
Problem
The management of material delivery in automated facilities using fleets of autonomous mobile robots is hindered by complex path planning, fleet composition, and task distribution, which affects material flow throughput due to factors like traversal and pivoting costs, and the existing methods are inefficient in optimizing these processes.
Innovation Solution
The method employs a Branch and Bound algorithm for determining the sequence of waypoints, a dual graph method for path planning, and incremental fleet composition optimization, using a combination of these techniques to minimize total path costs and operational costs by considering movement constraints and staggering waypoints for more efficient path planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If complex path planning methods are used for multi-load robots, then path optimization improves, but computational complexity and processing time increase
Solution Approach 1:
The path planning problem is segmented into discrete waypoints that are determined sequentially using Branch and Bound method. The continuous path is divided into manageable segments between waypoints, making the complex optimization problem tractable while maintaining material flow throughput efficiency.
Solution Approach 2:
The patent extracts the essential path planning elements into a dual graph representation, separating the critical path components from unnecessary computational details. This extraction allows for efficient path cost calculation by focusing only on the essential traversal and pivoting costs.
2Productivity
If fleet size and composition are optimized, then operational efficiency improves, but computational resources and time required increase
Solution Approach 1:
The patent performs preliminary determination of fleet composition and task distribution using Branch and Bound method before executing path planning. By pre-optimizing the fleet configuration and task assignment, the system reduces the computational burden during real-time operation while maintaining high operational efficiency.
3Productivity
If robots spend less time on travelling and turning, then material flow throughput increases, but path planning complexity increases
Solution Approach 1:
The patent introduces waypoints as intermediary points that simplify path planning. Instead of directly optimizing continuous paths between pickup and drop-off locations, the system uses discrete waypoints as mediators, reducing the complexity of path planning while minimizing travel and turning time for improved material flow throughput.
4Productivity
If task distribution is optimized among robots, then fleet productivity improves, but computational complexity increases
Solution Approach 1:
The task distribution problem is segmented and solved using Branch and Bound method, which divides the complex allocation problem into manageable decision trees. This segmentation allows for optimal task distribution across the fleet while keeping computational complexity tractable through systematic exploration of possible assignments.
Data Source
AI summary
A method of managing a fleet of robots for delivery of materials in a facility is provided. The method includes: determining a sequence of waypoints by a Branch and Bound (B&B) method; determining a path through the sequence of waypoints by a dual graph method; determining distribution of tasks among the robots by the B&B method; and determining a fleet composition by the B&B method.