Snow Removal Robot Path Planning for Multi-Area Clearing
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Solution Overview
Problem
Existing automatic snow removal vehicles are inefficient as they cannot clear multiple areas at once, limiting their snow removal efficiency due to reliance on ultra-wide band (UWB) tag wireless carrier communication for localization.
Innovation Solution
A smart snow removal method using GPS-RTK localization technology to generate a snow removal map, perform grid processing, and apply potential-field processing with a breadth-first search algorithm to determine a planned path for the snow removal robot to travel and clear multiple areas efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If ultra-wide band (UWB) tag wireless carrier communication is used for localization, then the snow removal vehicle can be localized, but it cannot clear multiple areas at one time, resulting in low efficiency
Solution Approach 1:
The patent segments the working area into multiple independent areas, each with its own starting point and potential field. The snow removal vehicle can sequentially clear multiple areas by switching between different potential fields, thereby improving productivity while maintaining the ability to handle multiple areas through the multi-area potential field construction method
Solution Approach 2:
The patent implements a universal path planning system that can handle multiple areas through a unified multi-area potential field model. The same path planning algorithm and control system are used for single-area and multi-area scenarios, making the system versatile across different working conditions while achieving high efficiency in clearing multiple areas
2Productivity
If GPS-RTK localization technology is used to generate snow removal map with multiple areas, then the robot can clear multiple areas at one time, but the path planning complexity increases
Solution Approach 1:
The patent performs preliminary actions by pre-constructing the multi-area potential field model and pre-calculating the optimal path before the snow removal operation begins. The breadth-first search algorithm is executed in advance to determine the sequence of area clearance, so that during actual operation, the vehicle simply follows the pre-planned path, reducing real-time computational complexity while maintaining high productivity
Data Source
AI summary
Provided are a smart snow removal method and equipment, and a snow removal robot. Based on GPS-RTK localization technology, latitude and longitude coordinates of a target snow throwing area and a target snow removal area are acquired, and a snow removal map is generated. Grid processing is performed on the snow removal map. Potential-field processing is performed on the snow removal map by: taking a grid located in the target snow throwing area as a starting point, and assigning, based on a breadth-first search algorithm, to grids located in the target snow removal area potential energy values in a manner of spreading outward. The snow removal robot is controlled to travel grid by grid from an uncleared grid whose potential energy value is currently the largest, and the snow removal operation is performed on an arrived grid, until the snow removal robot travels to the target snow throwing area.