Dynamic Landmark Update in Robot Navigation
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Solution Overview
Problem
Current systems require specialized training for human operators to instruct robots in retail environments, and updating geographical layouts with dynamic landmarks is cumbersome, making it difficult to translate natural language instructions into robot programming instructions accurately.
Innovation Solution
A computing device dynamically updates landmarks in a geographical layout by processing natural language instructions and interacting with operators to identify new landmarks, using an incomplete semantic map and a modified optimization process to execute directions and tasks without requiring a detailed map of static and dynamic landmarks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a detailed geographical layout with all static and dynamic landmarks is provided, then the robot can accurately carry out directional instructions, but the system complexity and cost increase due to requiring specialized training for operators
Solution Approach 1:
The robot performs self-localization by independently identifying landmarks in the environment using sensors and processing algorithms, rather than relying on pre-programmed detailed maps. This allows the robot to autonomously build and update its geographical understanding, eliminating the need for specialized operator training while maintaining navigation accuracy
Solution Approach 2:
The system dynamically updates the geographical layout by adding, removing, or modifying landmarks in real-time based on robot observations and environmental changes. This dynamic approach allows the map to adapt automatically without requiring manual intervention or specialized training, resolving the contradiction between map accuracy and system complexity
2Reliability
If a detailed geographical layout is provided initially, then the robot can execute natural language instructions accurately, but updating the layout when dynamic landmarks change requires stopping and manual intervention
Solution Approach 1:
The robot continuously updates its geographical layout understanding during normal operation without stopping task execution. Landmark identification and map updates occur in real-time as the robot moves through the environment, ensuring both accurate task execution and up-to-date mapping without interruption
Solution Approach 2:
The system uses sensor feedback from the robot's environment to automatically detect and update landmark changes. This closed-loop feedback mechanism allows the geographical layout to be dynamically updated based on actual observations, maintaining reliability while improving productivity by eliminating manual update requirements
3Ease of operation
If specialized training is provided to human operators, then they can effectively direct the robot, but the operating cost substantially increases
Solution Approach 1:
The robot autonomously performs landmark identification, natural language interpretation, and navigation planning without human intervention. This self-service capability eliminates the need for trained operators, reducing operating costs while maintaining ease of use for untrained users who can simply provide natural language instructions
Solution Approach 2:
The patent replaces the mechanical system of human operator training and manual robot control with an automated computational system using natural language processing, computer vision, and optimization algorithms. This substitution eliminates training requirements and reduces operating costs while preserving ease of operation
Data Source
AI summary
A computing device obtains an incomplete semantic map of a predefined space. The incomplete semantic map includes static landmarks. The computing device receives a set of natural language instructions including a sequence of semantically directive clauses, processes the sequence of semantically directive clauses, decodes one of an action and a path in the set of natural language instructions using an optimization process and based on the incomplete semantic map. In response to the decoding, the computing device inserts a newly identified landmark into the incomplete semantic map.