Sidewalk Perception Navigation for Mobile Robots at Street Crossings
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Solution Overview
Problem
Current autonomous and semi-autonomous mobile robots lack advanced capabilities to safely navigate urban environments, particularly across streets and driveways, requiring human intervention to prevent accidents.
Innovation Solution
A sidewalk perception system using computer vision algorithms, sensors, and a communication module to enable assisted teleoperation, allowing mobile robots to autonomously navigate while avoiding obstacles and staying within designated terrain, utilizing cameras, distance sensors, LiDARs, and stereo cameras, with a processor managing autonomous operations and a communication interface for control values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If mobile robots use basic autonomous navigation capabilities, then device complexity is reduced, but safety and reliability deteriorate when crossing streets and driveways
Solution Approach 1:
The system dynamically switches between teleoperation mode and assisted autonomous navigation mode based on real-time terrain assessment. The processor evaluates sidewalk versus street/driveway conditions and automatically adjusts the level of autonomous control, enabling basic navigation on safe sidewalks while requesting human intervention for risky areas, thus improving safety without requiring permanently advanced navigation capabilities
Solution Approach 2:
The navigation system is segmented into distinct operational modes: teleoperation mode for high-risk areas and assisted autonomous mode for safe areas. This segmentation allows the robot to use simple control mechanisms when needed while leveraging advanced computer vision and terrain classification algorithms only when safety permits, resolving the contradiction between complexity and reliability
2Productivity
If mobile robots require human intervention for all navigation decisions, then safety is improved, but productivity and ease of operation deteriorate
Solution Approach 1:
The robot performs self-assessment of terrain safety using onboard cameras and computer vision algorithms to classify surfaces as sidewalks or streets/driveways. This self-service capability allows the robot to autonomously navigate safe areas without continuous human oversight, improving productivity while maintaining safety through automated risk assessment and mode selection
3Productivity
If mobile robots use advanced autonomous navigation capabilities, then productivity is improved, but device complexity increases
Solution Approach 1:
The system applies different levels of navigation capability locally based on terrain type. Advanced computer vision and autonomous navigation algorithms are activated only when the robot is on sidewalks (safe areas), while simpler teleoperation is used for streets and driveways (risky areas). This local application of complexity reduces overall system burden while maintaining high productivity in appropriate contexts
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances safety by enabling mobile robots to autonomously traverse sidewalks while avoiding obstacles, reducing the need for human intervention in safe areas and allowing teleoperator control in risky situations, thus improving navigation efficiency and safety in urban environments.
Implementation Method 1
a camera for collecting the visual data needed to assess the terrain and potential obstacles
Implementation Method 2
LiDARs (light detection and ranging sensors)
Data Source
AI summary
The assisted navigation system is intended to enable an assisted operation mode in ground mobile robots. The system is designed to achieve an autonomous relocation of a robot from one location to another location within a sidewalk, minimizing the need for constant human intervention. The system includes a camera for collecting the visual data needed to assess the terrain and potential obstacles, a collection of sensors to detect potential obstacles during assisted operations, a communication module to receive inputs from a remote operator to enable the activation of this system, a localization module for teleoperations, a local server module to store the information gathered, a processor configured to operate a robot in an assisted mode of operation based on input from the communication interface in which the robot performs a task without human intervention, and a communication interface coupled to the processor and configured to communicate control values to the systems of the mobile robot.