AR Robot-IoT Task Authoring With Dynamic SLAM Navigation
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Solution Overview
Problem
Current systems for robot navigation and task planning, particularly in IoT environments, face limitations due to restricted perception capabilities, outdated SLAM maps, and the need for external tracking systems, which hinder robots' ability to adapt to changing environments and perform complex ad-hoc tasks effectively.
Innovation Solution
An authoring system utilizing a mobile device with AR-SLAM capabilities allows users to create dynamic SLAM maps and interactively plan robot navigation pathways and IoT tasks, enabling robots to adapt to environmental changes and perform complex tasks by leveraging IoT devices as spatial landmarks and communication mediums.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If pre-scanned SLAM maps are used for robot navigation, then the authoring interface can be separated from navigation, but the maps become static and cannot adapt to environmental changes
Solution Approach 1:
The patent implements dynamic SLAM map generation where the mobile device continuously updates the environmental map as it moves through the space. The map transitions from a static pre-scanned structure to a dynamic representation that automatically adapts to environmental changes, allowing the robot to navigate updated layouts without re-authoring.
2Loss of information
If external vision systems with fixed cameras are used for robot tracking, then live camera view can be displayed, but the authoring scene is limited to camera perspective only
Solution Approach 1:
The mobile device serves multiple functions: it acts as the authoring interface for task planning, generates and updates the SLAM map for robot navigation, and provides live visual feedback through the camera. This multi-functional approach eliminates the need for separate fixed camera systems while enabling authors to work from any position in the environment.
3Adaptability or versatility
If hand-held or head-mounted AR devices are used for mobile authoring, then users can move around and author from different perspectives, but the limited field-of-view constrains robot navigation range
Solution Approach 1:
The system transitions from the limited two-dimensional field-of-view of hand-held AR devices to a comprehensive three-dimensional SLAM map representation. The mobile device captures spatial data from multiple positions and angles, constructing a complete environmental model that enables robot navigation beyond the instantaneous camera view, effectively expanding the navigable space.
4Extent of automation
If robots are equipped with on-board SLAM capabilities, then navigation can be separated from authoring, but the pre-scanned SLAM map becomes outdated and cannot adapt to changes
Solution Approach 1:
The system implements continuous feedback by having the mobile device periodically re-scan the environment and update the SLAM map. This feedback loop detects environmental changes such as moved objects or altered layouts, and automatically updates the navigation map to reflect current conditions, maintaining both automation and reliability.
Data Source
AI summary
Disclosed is a visual and spatial programming system for robot navigation and robot-IoT task authoring. Programmable mobile robots serve as binding agents to link stationary IoT devices and perform collaborative tasks. Three key elements of robot task planning (human-robot-IoT) are coherently connected with one single smartphone device. Users can perform visual task authoring in an analogous manner to the real tasks that they would like the robot to perform with using an augmented reality interface. The mobile device mediates interactions between the user, robot(s), and IoT device-oriented tasks, guiding the path planning execution with Simultaneous Localization and Mapping (SLAM) to enable robust room-scale navigation and interactive task authoring.