Robot Fleet Navigation Sequencing for Congestion-Aware Task Simulation
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Solution Overview
Problem
Current robot navigation and control systems struggle to accurately model complex, dynamic environments where multiple robots and actors interact, leading to inefficiencies and errors due to oversimplification of task completion times and interactions, such as congestion and bottlenecks.
Innovation Solution
A simulation technique using a graph representation of physical areas with nodes and edges, where agents model robots and other actors, incorporating state machines and duration distributions based on interactions, allows for more accurate prediction of task completion times and efficient resource allocation by accounting for stochastic behavior and indirect interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If discrete event simulation is used to model robot tasks, then scalability and ease of modeling individual tasks are improved, but accuracy in modeling complex dynamic systems and interactions is worsened
Solution Approach 1:
The system segments the simulation model into discrete components: a graph representation dividing the physical area into nodes and edges, individual robot agents with separate state machines, and distinct event types. This segmentation allows each component to be modeled independently while maintaining overall system accuracy, resolving the contradiction between ease of modeling and modeling accuracy.
Solution Approach 2:
The patent introduces an intermediary simulation engine that processes events and coordinates interactions between multiple robot agents. This intermediary layer handles the complex dynamic interactions and congestion scenarios that simple discrete event simulation cannot capture, while maintaining the scalability and ease of modeling benefits of discrete events.
2Productivity
If more robots are added to the fleet, then task completion capacity is improved, but congestion and gridlock in the facility worsen
Solution Approach 1:
The system dynamically adjusts robot navigation and task allocation based on real-time simulation of facility conditions. The graph-based model with area nodes and terminal nodes allows the system to identify congestion points and dynamically reroute robots or redistribute tasks, enabling the fleet to scale without proportionally increasing congestion.
Solution Approach 2:
The simulation provides feedback on facility utilization, congestion levels, and task completion metrics. This feedback loop allows the system to optimize robot allocation and navigation strategies, balancing increased task completion capacity with minimized congestion by adjusting operations based on simulated performance data.
3Device complexity
If simple task duration modeling is used, then computational simplicity is improved, but accuracy in predicting task completion times worsens
Solution Approach 1:
The patent changes the parameters used to model task durations from simple fixed values to distributions that capture variability and uncertainty. By using duration distributions associated with different task types and incorporating factors like robot speed variations and interaction effects, the system achieves higher prediction accuracy while maintaining computational efficiency through the structured graph model.
Data Source
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AI summary
This document describes a simulation system that simulates robots and other actors performing tasks in an area. In one aspect, a method includes obtaining a graph representing a physical area. The graph includes area nodes that represent regions of the area that are traversed by a set of actors that perform tasks in the area and terminal nodes that represent regions of the facility where the actors perform the tasks. A set of agents that each include a model corresponding to an actor is identified. At least a portion of the agents includes models for robots that perform tasks in the area. The model of an agent represents durations of time for traversing area nodes and performing tasks are terminal nodes during simulations. A sequence of tasks being performed in the area is simulated using the graph and the set of agents.