AMR Route Network Generation from Incomplete Route Demonstration
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Solution Overview
Problem
Existing training methods for autonomous mobile robots (AMRs) require extensive duplication and precise coordination to navigate complex environments, particularly in areas with intersections, leading to inefficient route generation and potential user errors.
Innovation Solution
A system and method for generating route networks by allowing users to demonstrate routes on an AMR, utilizing sensors and user interfaces to collect training data, and automatically generating route segments, including reverse navigation and unnamed nodes, to reduce redundant travel and improve efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional training methods are used for AMR route generation, then complete route coverage can be achieved, but extensive duplication and precise coordination are required leading to inefficient training processes
Solution Approach 1:
The system creates virtual copies of demonstrated route segments and automatically generates reverse navigation paths. When a user demonstrates a route from point A to point B, the system automatically creates the corresponding reverse route from B to A, eliminating the need to physically demonstrate both directions separately. This copying approach maintains complete route coverage while dramatically reducing training time and redundancy.
Solution Approach 2:
The system performs preliminary automated processing of demonstrated routes by pre-generating reverse segments, connecting unnamed nodes, and creating complete path networks before actual AMR operation. This preliminary action prepares the route network in advance, eliminating the need for extensive coordination and repeated demonstrations during deployment.
2Measurement precision
If users demonstrate complete routes with all intersections and actions, then accurate navigation can be achieved, but extensive duplication and precise coordination are required
Solution Approach 1:
The system segments the route demonstration process into essential components only. Instead of requiring complete demonstration of all intersections and actions, the system divides the route into key segments and automatically infers the remaining portions. This segmentation maintains navigation accuracy by preserving critical path information while reducing training complexity through automated completion of intermediate segments.
Solution Approach 2:
The system enables self-service route generation by automatically connecting unnamed nodes and generating reverse navigation paths without user intervention. When a user demonstrates a route, the system autonomously completes the route network by creating connecting segments and reverse paths, eliminating the need for users to manually coordinate every intersection and action while maintaining complete navigational coverage.
3Productivity
If redundant travel is eliminated in route generation, then training efficiency improves, but automated connectivity of path networks is required
Solution Approach 1:
The system implements self-service automated path network generation that connects unnamed nodes and creates complete route segments without user intervention. The automated system independently processes demonstrated routes, generates reverse navigation paths, connects intermediate nodes, and validates complete route coverage. This self-service approach eliminates redundant travel while maintaining complete path connectivity through autonomous network generation.
Data Source
AI summary
Provided is a system and method for generating routes for use by a mobile robot. The mobile robot can comprise a navigation system in operative communication with a drive system; one or more sensors configured to collect sensor data, wherein the one or more sensors are configured to collect training data representative of a route or portions of a route as the mobile robot is navigated along the route; a user interface configured to receive user inputs providing route information; and a route generator configured to process the route information and the training data to generate a route network comprising a plurality of route segments. The training data can be generated while the mobile robot is navigated in a first direction and the mobile robot is configured to autonomously navigate in a second direction that is opposite the first direction.