Autonomous Vehicle Routing With Soft-Hard Locks for Deadlock Avoidance
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Solution Overview
Problem
Existing autonomous vehicle management systems face challenges in navigating multiple heterogeneous devices and avoiding deadlocks in complex warehouse environments with multiple levels and devices like mobile racks, vertical conveyors, and temporary spaces.
Innovation Solution
A processor-implemented method and system that includes a Multi-Robot Route Planner (MRRP) and Global Behavior Controller (GBC) to optimize route planning and device control, ensuring collision-free navigation and deadlock avoidance by using soft and hard locks, and dynamic route adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple heterogeneous autonomous vehicles operate in a complex warehouse environment with multiple devices, then the productivity and operational efficiency are improved, but the risk of deadlocks and collisions increases
Solution Approach 1:
The system performs preliminary route planning and deadlock detection before autonomous vehicles execute their paths. The Multi-Robot Route Planner generates complete routes and identifies potential deadlocks in advance, allowing the system to prevent deadlocks before they occur rather than reacting after they happen.
Solution Approach 2:
The patent introduces an intermediary mechanism (lock system with soft locks and hard locks) that mediates access to shared resources and passages. This intermediary controls vehicle access to prevent deadlocks by ensuring proper ordering of resource acquisition and release, acting as a mediator between competing vehicles.
2Device complexity
If a single autonomous vehicle operates in a single rack, then the system complexity is low, but the productivity and operational efficiency are limited
Solution Approach 1:
The patent creates a universal route planning and control system that can manage multiple autonomous vehicles of different types across multiple racks and devices. The same MRRP and GBC infrastructure handles diverse vehicle types (AGVs, AMRs, forklifts) and various warehouse devices, providing multi-functionality without proportionally increasing complexity.
Solution Approach 2:
The system segments the warehouse environment into discrete devices and passages, allowing independent control and routing decisions for each segment. This segmentation enables scalable management where complexity is distributed across modular components rather than concentrated in a monolithic system.
3Adaptability or versatility
If multiple heterogeneous devices are introduced to enhance functionality, then the versatility and operational capability are improved, but the navigation complexity and deadlock risk increase
Solution Approach 1:
The system performs preliminary analysis of device interactions and passage configurations to identify potential deadlock scenarios before vehicles navigate. The MRRP pre-computes routes considering all device states and transitions, allowing the system to handle complex device interactions without increasing real-time navigation complexity.
Solution Approach 2:
The Global Behavior Controller continuously monitors vehicle positions, device states, and lock conditions, providing real-time feedback to adjust routing decisions. This feedback mechanism enables the system to adapt to dynamic changes in the complex environment while maintaining overall system coordination and preventing deadlocks.
Data Source
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AI summary
The disclosure generally relates to method and system for autonomous vehicles management in an operating environment. The method may include sending a route plan in response to a request received by an autonomous vehicle from amongst a plurality of autonomous vehicles in an operating environment including one or more devices, wherein the one or more device comprises device or device space. The method may further include categorizing the route plan into path segments, the path segments including a first path segment including one or more nodes from the plurality of nodes being inside a device zone of the one or more devices and a second path segment including one or more nodes from the plurality of nodes being outside the device zone of the one or more devices. The method may further include determining a type of path segment from the categorized path segments for each of the node in the navigation segment and annotating the first node of the first path segment with a second precondition based on determination of a destination node from the one or more nodes and the second precondition includes performing a hard lock action of the first device for the autonomous vehicle traversing the first path segment to the destination node.