Mobile Robot Visual Waypoint Navigation on Uneven Terrain
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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 input and struggle with obstacle avoidance and terrain adaptation.
Innovation Solution
A method and system that utilize image data from multiple 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 using ground plane estimates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual input methods are used for robot navigation, then navigation control is achieved, but operator ease of operation deteriorates and navigation efficiency decreases
Solution Approach 1:
The patent replaces manual mechanical control operations with an automated visual interface system. Operators select destination points by simply touching the screen display, and the system automatically calculates navigation paths and controls robot movement, eliminating complex manual control inputs while reducing navigation time through automated path planning and execution.
Solution Approach 2:
The robot system performs self-navigation by automatically processing the selected destination point, calculating the optimal path, avoiding obstacles, and executing movement without continuous manual intervention. The system serves itself by autonomously translating a simple point selection into complete navigation execution, significantly improving operational efficiency.
2Ease of operation
If simple point selection interface is used, then ease of operation improves, but navigation precision and terrain adaptation worsen
Solution Approach 1:
The system introduces an intermediary processing layer between the simple point selection and the robot's navigation execution. This intermediary automatically performs terrain analysis, obstacle detection, path optimization, and coordinate transformation, ensuring that even simple point selections result in precise and safe navigation paths that adapt to complex environmental conditions.
Solution Approach 2:
The system performs preliminary actions by pre-calculating terrain features, identifying obstacles, and optimizing navigation paths before the robot begins movement. This advance processing ensures that the simple point selection interface delivers precise navigation results, as all complex computations are completed beforehand to guide accurate robot execution.
3Measurement precision
If multiple cameras and image processing are used, then navigation accuracy and terrain estimation improve, but device complexity increases
Solution Approach 1:
The system achieves multi-functionality by using the camera array not only for terrain estimation and obstacle detection but also for generating the visual display interface and calculating navigation paths. This universal use of the imaging system across multiple functions reduces the need for separate specialized sensors and processing systems, managing complexity while maintaining high measurement precision.
Solution Approach 2:
The patent merges multiple functions into an integrated processing system that combines image acquisition from multiple cameras, terrain plane estimation, obstacle detection, path calculation, and display generation into a unified workflow. This consolidation reduces system complexity by eliminating separate processing chains while maintaining accurate terrain estimation and navigation precision through coordinated multi-camera operation.
Data Source
AI summary
A method for 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.