Autonomous Bot Routing With Priority-Based Conflict Resolution
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing multi-agent pathfinding techniques in automated systems, such as logistics facilities and warehouses, face increased runtime issues due to the number of conflicts between autonomous transport vehicles, particularly when traffic becomes severe, leading to inefficiencies in collision-free path planning.
Innovation Solution
A multi-agent pathfinding algorithm that employs a priority-based search function to resolve conflicts by determining conflict-free bot routes using heuristic determination and precedence constraints, ensuring each route leg is free of collisions and prioritizes autonomous guided bots effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If priority-based search techniques (PBS or PBS-PC) are used to resolve conflicts between autonomous transport vehicles, then collision-free path planning is achieved, but runtime increases cubically as the number of conflicts increases
Solution Approach 1:
The patent segments the fleet management system into decentralized autonomous vehicles that each independently determine their own path priorities based on local conditions, rather than using a centralized system that processes all conflicts globally. This segmentation reduces the computational burden on any single system and allows parallel processing of path planning decisions.
Solution Approach 2:
The patent implements dynamic priority assignment where autonomous vehicles can adjust their path priorities in real-time based on changing traffic conditions, rather than using fixed priorities. This dynamic approach allows the system to adapt to severe traffic conditions more efficiently and reduces the number of conflicts that require centralized resolution.
2Productivity
If the number of autonomous transport vehicles is increased to improve productivity, then more tasks can be performed, but the number of conflicts between vehicles increases rapidly
Solution Approach 1:
By segmenting the control system into independent autonomous vehicles with onboard pathfinding capabilities, the system can scale to larger fleets without proportionally increasing centralized processing complexity. Each vehicle independently manages its own path planning, reducing the overall system complexity despite increased fleet size.
Solution Approach 2:
Autonomous vehicles perform self-service path planning by determining their own priorities and making independent routing decisions based on local traffic conditions. This self-service capability eliminates the need for centralized conflict resolution for every vehicle interaction, allowing the fleet size to increase without linearly increasing the complexity of conflict management.
3Reliability
If centralized multi-agent processes are used to manage autonomous vehicle routing, then coordinated path planning is achieved, but runtime is dominated by the number of conflicts to resolve
Solution Approach 1:
The patent segments the centralized multi-agent process into distributed autonomous decision-making units. Each autonomous vehicle independently determines its routing priorities based on local conditions, eliminating the need for centralized coordination of every routing decision. This segmentation maintains reliable coordinated routing while dramatically improving routing efficiency by removing the centralized bottleneck.
Solution Approach 2:
The system transitions from static centralized priority assignment to dynamic decentralized priority determination. Autonomous vehicles dynamically adjust their routing priorities based on real-time traffic conditions, allowing the system to maintain coordinated routing reliability while improving overall routing efficiency through distributed intelligence.
Data Source
AI summary
An automated storage and retrieval system includes a storage array, a plurality of bots, and a controller. The controller is connected to each bot to assign a series of tasks, or goals, to each bot. The controller has a bot route planner that has a multi-agent path finding algorithm resolver that determines, for each bot route effecting at least one task, or goal, occurrence and type of a conflict between bots performing the series of tasks, or goals. From the determination of occurrence and type of conflict, the multi-agent path finding algorithm resolver resolves each conflict free bot route, that determines the bot route respectively for each bot performing the at least one task, or goal, based on bot priority and precedence constraint between route legs describing, at least in part, the respective bot route of a common bot performing the at least one task, or goal.