Autonomous Ground Vehicle Visual Route Learning for Outdoor Navigation
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Solution Overview
Problem
Current autonomous robotic systems are limited in their ability to safely and efficiently operate in unstructured outdoor environments, particularly in agricultural settings, due to the lack of advanced sensory processing and adaptive capabilities to handle uneven terrain and complex environments, which hinders their collaborative operation with humans.
Innovation Solution
A collaborative autonomous ground vehicle equipped with a control system that includes front and rear camera groups, a drive assembly, and a perception subsystem, enabling it to learn and repeat routes by identifying and following or preceding humans, while navigating through complex environments and avoiding obstacles using stereo cameras, inertial measurement units, and machine learning algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If autonomous vehicles use sophisticated high-precision GPS systems for outdoor navigation, then positioning accuracy is improved, but device complexity and cost increase
Solution Approach 1:
The patent replaces sophisticated high-precision GPS systems with a camera-based visual navigation system. The autonomous vehicle uses cameras to capture images of the environment, and machine learning algorithms process these images to determine position and navigate along learned paths, eliminating the need for complex GPS hardware and reducing system complexity.
Solution Approach 2:
The system creates visual copies of the environment through camera images and uses these optical representations for navigation. By learning paths from visual data rather than relying on GPS coordinates, the system achieves positioning accuracy through image processing and pattern recognition instead of sophisticated positioning hardware.
2Adaptability or versatility
If autonomous machinery operates in close proximity to human workers, then collaborative capability is improved, but safety risks increase
Solution Approach 1:
The autonomous vehicle continuously captures images of its surroundings using cameras and processes these images in real-time to detect human workers. The system uses this visual feedback to dynamically adjust its behavior, slowing down or stopping when humans are detected nearby, thereby enabling safe collaborative operation while maintaining adaptability to work with human workers.
Solution Approach 2:
The vehicle's operational parameters are dynamically adjusted based on real-time environmental conditions detected through camera imaging. When human workers are detected in the vicinity, the system automatically modifies its speed and movement patterns to ensure safety, allowing flexible collaborative work while mitigating safety risks through adaptive behavior.
3Measurement precision
If indoor robotic vehicles use permanently installed visual controls and beacons, then navigation accuracy is improved, but ease of deployment deteriorates
Solution Approach 1:
The patent replaces permanently installed visual controls, beacons, and overhead guidance structures with a camera-based visual learning system. The autonomous vehicle learns navigation paths by processing images of natural environmental features, eliminating the need for complex installed infrastructure and significantly improving ease of deployment while maintaining navigation accuracy through machine learning.
Solution Approach 2:
The system performs its own navigation learning by capturing and processing images of the environment autonomously. Rather than requiring pre-installed guidance infrastructure, the vehicle independently learns paths by analyzing visual features in camera images, making deployment simpler while achieving accurate navigation through self-directed environmental understanding.
4Adaptability or versatility
If autonomous vehicles traverse uneven outdoor terrain, then adaptability to environment is improved, but measurement precision of position deteriorates
Solution Approach 1:
The patent replaces GPS-based position measurement with camera-based visual positioning. The system uses images captured by cameras to identify environmental features and determine position relative to learned paths, maintaining position accuracy on uneven terrain where GPS signals may be unreliable or unavailable.
Solution Approach 2:
The system changes its positioning approach from coordinate-based GPS measurements to visual feature-based positioning. By detecting and tracking environmental features in camera images, the vehicle maintains accurate position measurement on varied terrain, adapting to different surface conditions while preserving navigation precision through visual feedback.
Data Source
AI summary
A collaborative autonomous ground vehicle or robot for traversing a ground surface is disclosed. The robot is a wheeled vehicle includes two camera groups and other sensors to sense the environment in which the robot will operate. The robot includes a control system configured to operate in a teach mode to learn a route by either following a person or preceding a person and to store the learned route as a taught route. The control system is configured for identifying persons and objects, such as obstacles, within the fields of view of the camera groups. When the robot is in its repeat mode it will take the taught route over the ground surface and take appropriate action if an obstacle is identified in the field of view.