Robot Return Path Scheduling for Congestion-Aware Site Exit
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
In high-density robot clusters, existing technologies fail to efficiently manage the ordered departure of robots from working areas, leading to reduced efficiency due to path intersections and potential crowding, especially when robots need to pass through critical points quickly.
Innovation Solution
A method and apparatus that calculate the nearest target destination for idle robots based on distance and time to optimize backflow paths, reducing path intersections and implementing queuing management at crowding points to enhance efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple robots return to storage areas simultaneously using nearest destination calculation, then the departure order is improved and path intersections are reduced, but the system complexity increases due to centralized coordination requirements
Solution Approach 1:
A centralized control server acts as an intermediary between robots and storage areas. The server receives return requests from multiple robots, calculates optimal destinations and paths, and issues scheduling commands to coordinate robot movements. This mediator prevents path intersections and ensures orderly departure without requiring direct robot-to-robot communication or complex distributed algorithms.
Solution Approach 2:
The system performs preliminary path planning and destination assignment before robots actually depart. The control server calculates backflow paths and determines optimal storage areas in advance, issuing scheduling commands that pre-coordinate multiple robot movements. This preliminary action prevents conflicts before they occur, maintaining high departure efficiency while managing system complexity through proactive rather than reactive coordination.
2Object-affected harmful factors
If robots are dispatched to multiple storage areas, then path intersections are reduced and crowding at single points is avoided, but the difficulty of path planning and coordination increases
Solution Approach 1:
The system segments the robot fleet into different groups based on their calculated backflow paths and destination storage areas. Each robot is assigned to a specific storage area and follows a dedicated path, preventing crowding at single points. The control server manages these segmented groups independently, reducing coordination difficulty compared to managing all robots as a single group.
Solution Approach 2:
The control server dynamically changes routing parameters by calculating optimal backflow paths based on real-time robot positions, storage area capacities, and traffic conditions. This parameter optimization distributes robots across multiple storage areas and paths, reducing path intersections and crowding while maintaining manageable planning complexity through algorithmic rather than manual route design.
3Productivity
If robots quickly pass through crowding points, then the working efficiency is improved, but the risk of collisions and path conflicts increases
Solution Approach 1:
The control server implements periodic monitoring and scheduling of robot movements through crowding points. Robots receive timed scheduling commands that coordinate their passage through shared areas, ensuring they move quickly but at staggered intervals. This periodic coordination maintains high working efficiency while preventing collisions by ensuring robots don't occupy the same space simultaneously.
Solution Approach 2:
The system uses feedback from robot position reports to dynamically adjust scheduling commands. As robots approach crowding points, the control server receives position feedback and issues updated commands to optimize passage timing and speed. This feedback loop enables rapid movement through efficient real-time coordination while maintaining collision avoidance through continuous monitoring and adjustment.
Data Source
Figure 1~2
Figure 3~4
Figure 5~6
AI summary
Disclosed are a method and a device for returning robots (1, 2) from a site. The method comprises: obtaining the current coordinates of currently idle robots (1, 2) in a working area (S101); obtaining all return destination coordinates for the currently idle robots (1, 2) (S102); calculating, according to distances and times from the current coordinates to each set of destination coordinates, target destination coordinates nearest to the current coordinates (S103); controlling the currently idle robots (1, 2) to move out of the working area according to return paths corresponding to the target destination coordinates so as to ensure that the currently idle robots (1, 2) exit in an orderly manner (S104); if the paths intersect, performing queue management on the robots (1, 2), and determining a congestion area (501); respectively configuring, according to a request for passage sent by each robot (1, 2) in the congestion area, a dispatch instruction for said robot (1, 2) in the congestion area (S502); and respectively sending the dispatch instruction to said robot (1, 2) in the congestion area to enable said robot (1, 2) having received the dispatch instructions to pass through the congestion area according to said dispatch instruction (S503).