Cloud Route Planning for Scalable Warehouse Robot Fleets
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Solution Overview
Problem
Current route planning systems for autonomous vehicles in confined spaces, such as warehouses, struggle to scale with increasing numbers of robots, leading to cumbersome re-planning and interference from obstacles, and fail to integrate with existing infrastructure, resulting in inefficiencies and collisions.
Innovation Solution
A cloud-based multi-robot route planner that uses node resolution, multiple modules for route analysis, and optimization techniques like speed scaling, upsampling, and passive paths to generate collision-free routes, dynamically adjusting node positions and applying cost functions to optimize movement between nodes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional route planning systems are used for autonomous vehicles in confined spaces, then the system can handle a small number of robots, but the system becomes cumbersome and inefficient when the number of robots increases
Solution Approach 1:
The route planning problem is segmented into discrete nodes and edges forming a graph structure. This segmentation allows the system to break down complex multi-robot routing into manageable pathfinding problems between nodes, enabling scalable handling of increasing robot numbers through modular computation.
Solution Approach 2:
The system dynamically adjusts route plans in real-time based on changing conditions such as robot positions, obstacles, and traffic congestion. The graph structure and pathfinding algorithms continuously update routes as robots move and environmental conditions change, maintaining efficiency as the system scales.
2Adaptability or versatility
If robots are forced to follow predetermined lines, then the route planning is simple, but the system cannot adapt to obstacles and environmental changes
Solution Approach 1:
The system pre-processes the environment into a graph structure with nodes and edges before robots begin navigation. This preliminary action creates a flexible framework that can adapt to obstacles and changes without requiring complex real-time decision-making, balancing adaptability with computational simplicity.
Solution Approach 2:
The system continuously monitors robot positions and environmental conditions, using this feedback to dynamically update route plans. The graph-based approach allows efficient recalculation of paths when obstacles appear or conditions change, providing adaptability without proportional increases in system complexity.
3Quantity of substance
If the number of robots is scaled up, then the system capacity increases, but the input space blows exponentially making route planning difficult
Solution Approach 1:
By segmenting the environment into a graph of nodes and edges, the system converts the complex multi-robot routing problem into multiple instances of the well-studied pathfinding problem. This segmentation prevents exponential complexity growth by providing a structured framework that scales more gracefully with the number of robots.
Solution Approach 2:
The graph structure acts as an intermediary between the physical environment and the route planning algorithms. This intermediary representation simplifies computations by abstracting complex spatial relationships into discrete nodes and edges, enabling efficient route planning even as the number of robots increases.
4Adaptability or versatility
If existing route planning systems are used, then the system can operate with current infrastructure, but the system cannot integrate with existing warehouse designs and reconfiguring is challenging
Solution Approach 1:
The graph-based route planning system provides a universal framework that can represent various warehouse layouts and infrastructure configurations. The same pathfinding algorithms work across different environments, enabling easy integration with existing warehouse designs and straightforward reconfiguration when layouts change.
Data Source
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AI summary
A system and a method to optimize route plans for handling critical scenarios faced in an operating environment have been described. The system or a platform resolves one or more nodes based on the inputs related to an operating environment. The system plans one or more routes based on the resolved nodes to provide generated route plans. Based on the planning, the system may analyze one or more route plans for critical scenarios, for example, avoiding a collision or minimizing congestion, damage to the robot, performance of vehicle or warehouse, etc. After the route plans are analyzed, the system optimizes one or more route plans to provide optimized route plans. The optimized route plans are distributed to one or more autonomous vehicles. The fleet of autonomous vehicles may then route progress messages and share feedback with the platform.