Method and System for Path Planning of Autonomous Orchard Vehicles Using Trunk Detection

Path Planning Method and System for Autonomous Orchard Vehicles Using Trunk Detection

This technology is a path planning mechanism for autonomous vehicles that generates navigation routes within orchards by calculating the ratio of local minima to maxima (LL ratio) and the coordinate ratio (x-y ratio) for each segment in near-infrared camera images, then inputting these into a Bayesian classifier to probabilistically estimate the base of tree trunks.

In orchard environments, irregular ground patterns caused by complex weeds, low-hanging branches, and foliage have historically made machine vision-based tree trunk recognition and accurate positioning difficult.

This technology converts images into binary black-and-white images to separate obstacles into segments, applies a Bayesian probability model to the shape information (LL ratio, x-y ratio) of each segment to detect the base of tree trunks, establishes a center line for the driving path based on the extracted trunk positions via linear regression, and improves the algorithm through feedback from detection results. Applicable to logistics transport, service robots, and autonomous platforms, it enhances the accuracy of trunk detection and improves the stability of navigation path data in orchard environments.

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Key Features:

• The base detection step involves acquiring the LL ratio (the ratio of the local minimum to the local maximum of a segment) and the x-y ratio (the ratio of the x and y values of the local minimum).
• The image conversion step involves converting the image into a black-and-white format to distinguish obstacles from the background.
• The step of calculating the probability of a tree trunk based on Bayesian classification of the LL ratio and x-y ratio.
• The step where the autonomous vehicle's image processing unit acquires and converts images of the surrounding environment.

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This invention was developed with support from the Ministry of Science and ICT for the development of core technologies for next-generation intelligent systems.