AGV Route Planning Using Motion-Aware Topological Maps
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Solution Overview
Problem
Current route planning systems for automated guided vehicles (AGVs) lack accuracy and efficiency due to the failure to consider motion information from other vehicles, which can lead to collisions and suboptimal route determination.
Innovation Solution
A method and device for route planning that preprocesses topological maps by modifying node information based on motion status information of multiple AGVs, determining total costs between nodes, and optimizing routes to avoid collisions and improve route reasonability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional route planning systems use static topological maps without considering motion information from other vehicles, then the system complexity is low and computation is fast, but the route planning accuracy and efficiency deteriorate due to collisions and suboptimal routes
Solution Approach 1:
The system performs preliminary actions by modifying the topological map in advance based on predicted motion trajectories of other AGVs. Node information (passability, cost) is updated before route planning to reflect future occupancy, allowing the planner to avoid collisions proactively rather than reactively
Solution Approach 2:
The patent introduces an intermediary layer between the static topological map and the route planning algorithm. This intermediary is the modified topological map that incorporates motion status information of other vehicles, serving as a mediator that translates dynamic environment data into static map structures that the planner can process
2Productivity
If the system modifies node information based on motion status information of multiple AGVs, then route planning efficiency and collision avoidance improve, but the computation time and processing complexity increase
Solution Approach 1:
Instead of modifying the entire topological map uniformly, the system applies local quality changes only to specific nodes and edges that are affected by other AGVs' motion trajectories. This selective modification reduces the computational burden while maintaining the benefits of dynamic awareness
Solution Approach 2:
The system changes parameters of node information (such as passability flag, cost value, or weight) based on motion status information. By adjusting these parameters dynamically, the route planning algorithm can efficiently incorporate temporal-spatial constraints without requiring complete map regeneration
3Reliability
If the system considers motion status information of multiple AGVs when determining node passability, then collision avoidance improves, but the route planning speed decreases due to additional data processing
Solution Approach 1:
The system applies partial action by considering motion status information only for nodes and edges that are relevant to the current routing problem, rather than processing all AGV motion data uniformly. This selective approach maintains collision avoidance capability while reducing unnecessary computational overhead
Data Source
AI summary
The present disclosure may provide a method for route planning. The method may include obtaining a start location and a destination of a target vehicle. The method may also include obtaining a map of a target region including the start location and the destination. The map may include node information of each of a plurality of nodes. Further, the method may include obtaining motion status information associated with one or more vehicles other than the target vehicle in the target region. The method may further include determining a target route of the target vehicle based at least in part on the start location, the destination, the motion status information associated with the one or more vehicles, and the map.