Autonomous Mobile Robot Guidance for Variable Endpoints
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Solution Overview
Problem
Existing material handling systems are inflexible and inefficient in transporting items between variable endpoints, particularly in unstructured environments, rough terrains, and dynamic workspaces, where conventional systems face challenges with adaptability and infrastructure requirements.
Innovation Solution
An automated system comprising autonomous mobile robots interacting with a dynamically reconfigurable guidance system to identify and adapt to changing endpoints, using beacons, markers, or SLAM navigation to navigate and deliver items, with sensors for obstacle detection and route adjustment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional material handling systems are used, then infrastructure requirements are reduced, but adaptability to variable endpoints and unstructured environments deteriorates
Solution Approach 1:
The system employs dynamically reconfigurable guidance systems that can adapt to changing endpoints and unstructured environments. The guidance system is not fixed but can be reconfigured to accommodate variable transportation needs, allowing the robots to operate flexibly without requiring permanent infrastructure installation at each endpoint.
Solution Approach 2:
The autonomous mobile robots navigate and adapt to their environment using onboard sensors and guidance systems without requiring external infrastructure. The robots independently identify endpoints, plan routes, and adjust to obstacles, eliminating the need for complex external infrastructure while maintaining high adaptability.
2Productivity
If automated autonomous robots are deployed, then transportation efficiency improves, but system complexity increases
Solution Approach 1:
The autonomous mobile robots are designed as multi-functional units that can perform various transportation tasks across different environments and endpoints. This universality allows a single robot design to handle diverse transportation needs, reducing the number of specialized systems required and thereby managing overall system complexity while maintaining high productivity.
Solution Approach 2:
The system replaces complex mechanical infrastructure with software-based guidance systems and autonomous navigation algorithms. Instead of physically complex fixed conveyor systems or rail networks, the solution uses computational guidance and sensor-based navigation, reducing mechanical complexity while improving transportation efficiency.
3Adaptability or versatility
If fixed infrastructure systems are used, then system stability is maintained, but flexibility to change endpoints deteriorates
Solution Approach 1:
The guidance system is designed to be dynamically reconfigurable rather than statically fixed. This allows the system to maintain operational stability through consistent autonomous navigation while simultaneously adapting to changing endpoints and transportation requirements, resolving the contradiction between stability and flexibility.
Data Source
AI summary
An automated system for transporting items between variable endpoints includes a guidance system for identifying the endpoints and at least one autonomous mobile robot interacting with the guidance system for automatically moving items between the endpoints. The at least one robot is configured to (a) collect an item to be transported at a source end point, (b) travel to a destination endpoint utilizing the guidance system to locate the destination endpoint, (c) deliver the item to the destination endpoint, and (d) repeat (a) through (c) for a given set of items. The guidance system is dynamically reconfigurable to identify new endpoints.