This technology generates collision-avoidance driving paths by modeling speed control uncertainty based on the difference between the reference speeds and actual candidate speeds of a two-wheeled mobile robot's left and right wheels, dynamically expanding the clearance space for collision avoidance.
Previously, failure to properly model speed control errors—which arise from limitations in sensor and motion control performance during robot operation—often led to collision risks or reduced driving efficiency due to excessively large clearance settings.
This technology proposes a method that quantitatively models the speed control errors of the left and right wheels using standard deviation and the chi-squared distribution. This model is applied to the existing clearance to create an expanded buffer, which is then incorporated into the cost function of the path planning algorithm. It is suitable for indoor service and delivery robots, ensuring an optimal balance between safety and driving efficiency.
This invention was developed with support from the National Research Foundation of Korea's "Intelligent Growth Autonomous Driving System for Unmanned Vehicles Operating Safely in Congested Residential Road Environments" and the Ministry of Agriculture, Food and Rural Affairs' "[Sub-project 2-1] Autonomous Driving Platform for Greenhouse Transport Operations."
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