Distributed Robot Congestion Control Near Shared Target Points
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Solution Overview
Problem
Centralized congestion control systems in multi-robot environments are prone to instability and high resource occupation, leading to inefficiencies and increased production costs due to reliance on a central server, which can fail and face increased computing pressure with more robots.
Innovation Solution
A distributed congestion control method where each robot maintains a state machine to independently detect and coordinate with adjacent robots, determining optimal motion strategies to avoid collisions and manage congestion without relying on a central server, by defining areas around target points and switching between states to manage robot flow.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If centralized control is used to calculate optimal moving trajectories for each robot, then collision risk is reduced, but system reliability deteriorates due to central server failure risk
Solution Approach 1:
The centralized control system is segmented into distributed state machines on each robot. Each robot independently manages its own congestion control through local state transitions (normal, suspended, locked, continue states), eliminating the single point of failure represented by the central server while maintaining coordinated collision avoidance through shared area-based rules.
2Reliability
If centralized control is used to manage robot congestion, then collision risk is reduced, but computing pressure on the central server increases with more robots
Solution Approach 1:
Each robot serves itself by running an independent state machine that autonomously determines its congestion control actions. Robots self-manage their trajectory adjustments and state transitions based on local observations of target areas, eliminating the need for a central server to perform computationally intensive trajectory optimization for each robot while maintaining collision avoidance through coordinated state-based rules.
3Reliability
If centralized control is used for congestion management, then robot coordination is improved, but production cost increases
Solution Approach 1:
Instead of using a single expensive central server, the control functionality is copied to each robot as an independent state machine. This distributed copying approach maintains coordination capabilities through shared area-based rules while reducing overall system cost by eliminating the need for high-performance centralized computing hardware and reducing dependency on expensive server infrastructure.
Data Source
AI summary
The present disclosure provides a distributed congestion control method and a robot. The method includes: disposing a first area and a second area centered on a target point; detecting, when a first robot moves towards the target point, whether there are other robots in the second area or not, and in a case where there are other robots in the second area, coordinating the first robot and the other robots to determine a second robot allowed to simultaneously enter the first area; and controlling the second robot to move towards the target point in a case where the second robot has entered the first area.