Autonomous Mobile Robot Corner Turning With Dynamic Radius Control
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Solution Overview
Problem
Autonomous mobile systems face challenges in navigating corners efficiently and safely, particularly when turning at corners with obstacles, as they may collide with people or other robots, and existing systems struggle to balance efficiency and safety.
Innovation Solution
An autonomous mobile system that calculates the corner radius based on obstacle data from cameras, adjusting the turning path and speed to ensure both efficiency and safety, even when image data is unavailable, by using a server device to process image data from facility cameras and determine the optimal corner radius and moving speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the transportation robot travels along the shortest route by turning at corners, then travel efficiency is improved, but collision risk with people or other robots increases
Solution Approach 1:
The corner radius is dynamically adjusted based on real-time obstacle detection. When obstacles are detected in the corner area, the system automatically increases the corner radius to create a safer turning path. This dynamic adaptation allows the robot to maintain efficient travel when the path is clear while ensuring safety when obstacles are present, resolving the contradiction between travel efficiency and collision risk.
Solution Approach 2:
The system uses camera-based obstacle detection to provide feedback about the corner environment. This feedback loop enables the robot to perceive obstacles and adjust its turning radius accordingly. The continuous monitoring and adjustment mechanism ensures that the robot can navigate corners efficiently when safe while automatically adapting to avoid collisions when obstacles are detected.
2Reliability
If the corner radius is increased to avoid obstacles, then safety is improved, but travel path length increases reducing efficiency
Solution Approach 1:
The corner radius is not fixed but dynamically adjusted based on obstacle presence. When no obstacles are detected, the robot uses a smaller corner radius to maintain the shortest path and maximize efficiency. When obstacles are detected, the radius is increased only for the duration and distance necessary to safely pass the obstacle. This dynamic adjustment ensures safety is improved only when necessary, minimizing the impact on overall travel efficiency.
Solution Approach 2:
The system changes the geometric parameter (corner radius) based on environmental conditions. By modifying the turning radius parameter in response to detected obstacles, the robot can safely navigate around obstacles when needed while maintaining optimal travel parameters when the path is clear, thus balancing safety improvements with efficiency preservation.
3Measurement precision
If the robot uses camera data to calculate corner radius, then navigation accuracy is improved, but system complexity increases
Solution Approach 1:
The system introduces an intermediary processing layer that receives image data from the camera and calculates the appropriate corner radius. This intermediary computation layer translates raw visual information into actionable navigation parameters without requiring complex hardware modifications. The software-based approach to achieving high navigation accuracy keeps the physical system complexity manageable by using algorithmic processing rather than complex mechanical or sensor systems.
Data Source
AI summary
An autonomous mobile system according to the present embodiment is an autonomous mobile system that autonomously moves in a facility provided with a corner between aisles. When the autonomous mobile system turns at the corner, the autonomous mobile system calculates a magnitude of a corner radius of turning in a traveling path based on an obstacle captured in image data of a camera that captures an image of an exit of the corner.