Mobile Robot Action Grounding From Site Models and Sensor Data
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Solution Overview
Problem
Current robotic systems lack the ability to dynamically perform customized actions based on sensor data and site models, failing to incorporate persona-based interactions and efficient action implementation, leading to inconsistencies and inefficiencies in navigation and task execution.
Innovation Solution
A method involving data processing hardware that transforms site models and sensor data into a text format, allowing for the identification of actions by a computing system and subsequent instruction of a mobile robot to perform these actions, including persona-based interactions and synchronized movements and audio outputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If robotic systems use traditional navigation methods without site models and persona-based interactions, then the system complexity is lower, but the adaptability and interaction capabilities are insufficient
Solution Approach 1:
The system segments the robotic platform into distinct functional modules: site model management module, sensor data processing module, persona-based interaction module, and action execution module. Each module operates independently with defined interfaces, allowing the system to achieve high adaptability through modular composition while managing complexity through clear separation of concerns.
Solution Approach 2:
The robotic platform implements a universal action execution framework that can perform multiple types of actions (navigation, manipulation, communication) through a common architecture. The site model and sensor data processing infrastructure serves multiple purposes including navigation, task planning, and interaction decision-making, reducing overall system complexity while enhancing versatility.
2Reliability
If robotic systems implement customized actions based on sensor data and site models, then the action accuracy and reliability are improved, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary processing of sensor data and site model information to pre-compute relevant features and constraints before action execution. The site model is pre-processed to extract navigable paths, obstacles, and task-relevant locations, allowing the action execution module to make rapid decisions based on pre-computed information rather than processing raw data in real-time.
Solution Approach 2:
The robotic system implements self-service through autonomous decision-making based on integrated sensor data and site models. The platform independently identifies actions, plans execution sequences, and adapts to environmental changes without external intervention, improving action reliability while reducing the time loss associated with human oversight and manual control.
3Productivity
If robotic systems transform site models and sensor data into standardized formats, then the data integration and action identification efficiency are improved, but the data processing complexity increases
Solution Approach 1:
The system transforms site models and sensor data into standardized formats by changing key parameters including spatial coordinate systems, object representation schemas, and semantic annotation structures. This parameter standardization enables efficient data integration and action identification while the transformation processes are encapsulated in dedicated modules that hide processing complexity from higher-level decision-making functions.
Data Source
AI summary
Systems and methods are described for instructing performance of an action by a mobile robot based on transformed data. A system may obtain a site model in a first data format and sensor data in a second data format. The site model and/or the sensor data may be annotated. The system may transform the site model and the sensor data to generate transformed data in a third data format. The system may provide the transformed data to a computing system. For example, the system may provide the transformed data to a machine learning model. Based on the output of the computing system, the system may identify an action and instruct performance of the action by a mobile robot.