Mobile Robot Navigation Using Image-Guided Waypoint Selection
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Solution Overview
Problem
Current robotic systems lack intuitive and efficient methods for navigation, particularly in complex environments, as they often require manual programming and struggle with obstacle avoidance and terrain adaptation.
Innovation Solution
A method and system that utilize image data from cameras on a robot to create a graphical user interface for operators to select navigation targets, calculate pointing vectors, and transmit waypoint commands, allowing the robot to autonomously navigate while avoiding obstacles and adapting to terrain through terrain estimation and obstacle analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual programming methods are used for robot navigation, then navigation control can be achieved, but the operation complexity increases and intuitiveness decreases
Solution Approach 1:
The patent uses image data from camera sensors to create a visual copy of the robot's environment. Operators can directly select navigation targets by pointing at locations in the captured images, rather than manually programming coordinates. This copying approach translates real-world visual information into navigation commands, dramatically improving intuitiveness while reducing operational complexity
Solution Approach 2:
The system introduces an intermediary processing layer that automatically converts pixel coordinates from operator selection into meaningful navigation waypoints. This intermediary handles the complex coordinate transformations and terrain estimations, shielding operators from complexity while enabling intuitive point-and-select navigation
2Ease of operation
If simple navigation methods are used, then ease of operation improves, but adaptability to complex environments and obstacle avoidance capability deteriorates
Solution Approach 1:
The robot performs self-service through automated terrain estimation and obstacle detection using its onboard sensors. The system automatically processes camera images to understand the environment, calculate ground planes, and identify navigable paths. This self-service capability allows simple operator input to produce sophisticated adaptive navigation behavior in complex terrains
Solution Approach 2:
The system continuously captures image data from the environment and uses this feedback to dynamically adjust navigation paths. By processing real-time visual feedback about obstacles and terrain, the robot can adapt its route while maintaining simple point-and-select operation for the operator
3Measurement precision
If precise terrain estimation is performed, then navigation accuracy improves, but computational complexity and processing time increase
Solution Approach 1:
The system applies partial terrain estimation by focusing computational resources on estimating only the ground plane and relevant navigation surfaces rather than complete environmental modeling. This selective approach achieves sufficient accuracy for navigation while reducing overall computational complexity and processing requirements
Data Source
AI summary
A method tor controlling a robot includes receiving image data from at least one image sensor. The image data corresponds to an environment about the robot. The method also includes executing a graphical user interface configured to display a scene of the environment based on the image data and receive an input indication indicating selection of a pixel location within the scene. The method also includes determining a pointing vector based on the selection of the pixel location. The pointing vector represents a direction of travel for navigating the robot in the environment. The method also includes transmitting a waypoint command to the robot. The waypoint command when received by the robot causes the robot to navigate to a target location. The target location is based on an intersection between the pointing vector and a terrain estimate of the robot.