Telepresence Robot Intent Recognition for Adaptive Navigation
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Solution Overview
Problem
Conventional telepresence robots rely on pre-loaded maps and manual training for navigation, which limits their ability to adapt to dynamic environments and human intentions, leading to suboptimal interactions and navigation.
Innovation Solution
The system employs multimodal human intention recognition using sensor data, semantic information, and historical response reactions to dynamically update the navigation and AV settings of telepresence robots, allowing them to adapt to environmental changes and human interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If pre-loaded maps and manual training are used for navigation, then navigation functionality is provided, but adaptability to dynamic environments and human intentions deteriorates
Solution Approach 1:
The telepresence robot autonomously senses environmental changes and determines human intentions without requiring pre-loaded maps or manual training. The system self-adjusts navigation paths and AV settings based on real-time sensor data, enabling adaptability while avoiding the complexity of manual programming and training procedures
Solution Approach 2:
The system continuously collects sensor data from the environment and uses this feedback to dynamically adjust navigation and AV settings. This closed-loop feedback mechanism enables the robot to adapt to changing environments and human intentions in real-time, resolving the contradiction between adaptability and system complexity
2Adaptability or versatility
If multimodal human intention recognition is implemented, then adaptability to human interactions is improved, but computational requirements and system complexity increase
Solution Approach 1:
A single computing device performs multiple functions including sensor data collection, environmental change detection, human intention determination, navigation control, and AV setting adjustment. This multi-functional approach enables comprehensive human interaction adaptability while consolidating system complexity into one integrated platform rather than requiring separate specialized systems
Solution Approach 2:
The system combines sensor data, environmental information, and intention recognition algorithms into a unified processing framework. By merging these previously separate functions into one integrated system, the patent achieves high adaptability to human interactions while managing computational requirements through consolidated architecture
3Productivity
If real-time navigation optimization is performed, then navigation effectiveness is improved, but processing time and computational load increase
Solution Approach 1:
The navigation system dynamically adjusts paths based on real-time environmental changes and detected human intentions without requiring exhaustive computation. The system adapts navigation parameters on-the-fly, improving effectiveness by responding to current conditions rather than relying on pre-computed static paths, thus reducing processing time while maintaining high productivity
Data Source
AI summary
Disclosed herein are systems and methods for controlling a telepresence robot, sometimes referred to as a receiver. The systems and methods may include obtaining environmental data associated with the receiver and/or an operator of the telepresence robot, sometimes referred to as a sender. A model defining a human intent may be received and an intent of a human proximate the receiver and or the sender may be determined using the model. A first signal may be transmitted to the receiver. The first signal may be operative to cause the receiver to alter a first behavior based on the intent of the human and/or the sender.