Coverage Path Planning With Cell Segmentation for Complex Areas
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Solution Overview
Problem
Complexity in coverage path planning for areas with non-convex shapes leads to impractical computation time and requires expert input, making it difficult for non-experts to plan efficient paths within practical time limits.
Innovation Solution
A method using a computing arrangement to divide areas into cells, allowing user-input for merging, dividing, and reordering cells, and iteratively generating feasible boustrophedon paths, with user interaction to refine the path planning process, enabling non-experts to plan efficient coverage paths.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If optimal coverage path planning is performed for complex areas using cost functions and expert input, then path optimality is improved, but computation time becomes impractical and the process becomes computationally intractable
Solution Approach 1:
The complex area is divided into multiple cells, and the coverage path planning is performed iteratively cell by cell. Each cell is processed independently to generate a feasible path portion, avoiding the need to compute the entire optimal path at once. This segmentation reduces computation time while maintaining reasonable path quality.
Solution Approach 2:
The system pre-divides the area into cells and pre-processes each cell to determine feasible path portions before combining them into the complete coverage path. This preliminary processing of individual cells enables efficient iterative generation of the overall path without requiring computationally intensive global optimization.
2Manufacturing precision
If expert input and cost functions are used to plan coverage paths, then path quality is improved, but device complexity and ease of operation deteriorate due to requiring highly skilled path planning experts
Solution Approach 1:
The system automatically performs path planning by dividing the area into cells and iteratively generating feasible coverage paths without requiring expert intervention. The computing arrangement independently processes each cell and combines results, enabling non-experts to obtain quality paths through automated operation.
Solution Approach 2:
By breaking down the complex path planning task into independent cell-processing steps, the system eliminates the need for expert-level global optimization skills. Each cell can be processed using standardized algorithms, making the system accessible to non-experts while maintaining path quality.
3Length of moving object
If the shortest coverage path is planned for simple convex areas, then path length is minimized, but the approach becomes computationally intractable for complex non-convex areas
Solution Approach 1:
The area is segmented into multiple cells that can be processed independently. For each cell, a simple boustrophedon path portion is generated, and these portions are combined to form the complete coverage path. This segmentation transforms the computationally intractable global optimization problem into multiple tractable local problems.
Solution Approach 2:
Instead of seeking the globally shortest path which is computationally intractable for complex areas, the system generates a feasible path by combining optimal or near-optimal path portions for individual cells. This partial optimization approach produces sufficiently short paths without requiring exhaustive global search.
Data Source
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AI summary
A method of planning a coverage path to cover an area. The method includes a computing arrangement iteratively receiving (211), generating (205) and displaying (207). The computing arrangement is configured to receive a range acceptable user-inputs. The receiving is receiving user-input within the range of acceptable user-inputs. The generating is generating a feasible coverage path based on the user-input. The displaying is displaying at least some detail of the feasible coverage path.