Robot Coverage Path Planning With Fewer Turns in Non-Convex Regions
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Solution Overview
Problem
Existing coverage path planning methods for autonomous and semi-autonomous cleaning devices fail to optimally minimize the number of turns, leading to inefficient coverage and sub-optimal cleaning performance, especially around turns where the cleaning tool's effectiveness is compromised.
Innovation Solution
A linear programming approach is used to generate an axis-parallel coverage plan that minimizes the number of turns by partitioning the environment into optimal ranks, ensuring that the robot covers all regions with straight-line paths, and then formulating the connection problem as a Generalized Travelling Salesman Problem to compute a complete coverage plan.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the robot takes turns during coverage path planning, then the robot can navigate around obstacles and cover non-convex regions, but the cleaning quality deteriorates and time consumption increases
Solution Approach 1:
The patent segments the non-convex environment into convex sub-regions using decomposition algorithms. By dividing the complex non-convex space into multiple convex portions, the robot can cover each region with straight-line paths without requiring turns, thereby maintaining cleaning quality while achieving complete coverage of the arbitrary non-convex environment.
2Adaptability or versatility
If the robot takes turns during coverage path planning, then the robot can navigate around obstacles and cover non-convex regions, but the coverage time increases
Solution Approach 1:
The patent segments the non-convex environment into convex sub-regions using decomposition algorithms. By dividing the complex non-convex space into multiple convex portions, the robot can cover each region with straight-line paths without requiring turns, thereby maintaining cleaning quality while achieving complete coverage of the arbitrary non-convex environment.
3Productivity
If heuristic methods are used for turn-minimizing coverage, then the solution can be found relatively quickly, but the optimality of the solution is not guaranteed
Solution Approach 1:
The patent replaces heuristic search methods with a mathematical programming formulation. By formulating the turn-minimizing coverage problem as a mixed-integer linear program (MILP), the system substitutes approximate mechanical heuristics with exact mathematical optimization, guaranteeing optimal solutions while maintaining computational efficiency through modern MILP solvers.
Data Source
AI summary
A system and method of minimum turn coverage of arbitrary non-convex regions. Coverage planning is the task of generating a path that ensures the tool carried by the robot covers all regions of interest. The number of turns in the path can affect the time to cover the region and the quality of coverage (tools like cameras and cleaning attachments commonly have poor performance around turns). In recent turn-minimizing coverage methods, the region is partitioned to be covered by the least number of rectangles of width equal to the tool's width. The partitioning problem is typically solved using heuristics that have no optimality guarantees. A linear programming (LP) approach is disclosed to generate an axis-parallel coverage plan that minimizes the number of turns taken by the robot. The LP method solves this problem optimally in polynomial time. Coverage plans are generated for real regions using the LP method.