Autonomous Parts-Handling AGVs With Mapless Real-Time Navigation
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Solution Overview
Problem
Traditional Automated Guided Vehicle (AGV) systems rely on centralized computer systems that can be inefficient in navigating and managing multiple tasks within two-dimensional spaces, such as warehouses, due to the need for pre-mapped navigation and lack of autonomous decision-making capabilities.
Innovation Solution
The implementation of a decentralized supervisory system with autonomous parts container bases equipped with position sensors, transceivers, and motive assemblies that allow AGVs to navigate and perform tasks without pre-stored maps, using real-time location identification and command execution to optimize navigation and task management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a centralized computer system is used to control AGVs, then navigation and task management can be coordinated, but the system becomes inefficient in navigating and managing multiple tasks within two-dimensional spaces
Solution Approach 1:
The centralized supervisory system is divided into multiple decentralized supervisory systems, each associated with different facilities or zones. Each AGV is equipped with its own supervisory system that can independently make navigation decisions, eliminating the bottleneck of centralized control while maintaining coordinated task management across facilities.
Solution Approach 2:
The system transitions from two-dimensional pre-mapped navigation to three-dimensional spatial awareness by incorporating depth information and real-time environmental sensing. This allows AGVs to navigate dynamically without relying on predetermined two-dimensional maps, improving adaptability and navigation efficiency.
2Extent of automation
If pre-mapped navigation is used, then AGVs can follow predetermined paths, but they lack autonomous decision-making capabilities
Solution Approach 1:
Each AGV is equipped with a supervisory system that enables it to independently sense its environment, make navigation decisions, and execute tasks without continuous human intervention or reliance on pre-programmed paths. The AGV serves itself by autonomously determining optimal routes and adapting to changing conditions in real-time.
Solution Approach 2:
The navigation system transitions from static pre-mapped paths to dynamic real-time navigation. The AGV continuously updates its position, orientation, and route based on current environmental conditions, allowing it to adapt autonomously to obstacles, changing tasks, and dynamic facility layouts.
3Reliability
If centralized control is implemented, then coordination between multiple AGVs is possible, but real-time location identification and command execution efficiency is reduced
Solution Approach 1:
The centralized control architecture is segmented into distributed supervisory systems located at different facilities. Each AGV communicates with the nearest supervisory system, reducing communication latency and enabling faster real-time location identification and command execution while maintaining coordinated task management across the broader network.
Data Source
AI summary
A storage system comprises one or more automated guided vehicles, one or more centralized supervisory systems, one or more parts container storage spaces disposed within a defined two dimensional space comprising one or more addressable parts container spaces and parts bins which comprise a part identifier and which are configured to fit at least partially within the parts container storage space. The automated guided vehicles comprise a controllable autonomous parts container base and one or more position sensors operatively in communication with a controller and operative to determine a coordinate location of the automated guided vehicle within a defined two dimensional space in real time without relying on a map stored in the controller.