Robotic Surface Coverage Using Polar Map Zone Segmentation
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Solution Overview
Problem
Conventional autonomous cleaning robots face inefficiencies due to random movement patterns leading to overlapping coverage, which decreases their operational time and increases battery charging needs, and existing solutions require expensive technologies like advanced sensors and processors.
Innovation Solution
A method and system using a base station and robotic device to create a polar map of the workspace, divide it into zones, and assign these zones for coverage, with the robotic device reporting actual coverage and calculating penalties to minimize redundancy and maximize rewards, employing machine learning techniques to optimize zone selection and reduce overlap.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If random coverage pattern is used, then robot can cover workspace autonomously, but redundancy increases and efficiency decreases over time
Solution Approach 1:
The workspace is segmented into a polar map with discrete cells and organized into multiple zones. The robot systematically progresses through zones and cells rather than moving randomly, ensuring each area is covered exactly once unless necessary for accessibility. This segmentation eliminates redundant coverage while maintaining autonomous operation.
2Productivity
If complex mapping systems with advanced sensors and processors are used, then surface coverage efficiency improves, but acquisition and maintenance costs become prohibitive
Solution Approach 1:
The system uses a universal polar coordinate map that can represent any workspace geometry without requiring expensive specialized sensors or processors. The same base station and algorithmic approach work across different environments, reducing the need for costly custom hardware while maintaining high coverage efficiency.
3Device complexity
If basic unplanned movement patterns are used, then device complexity is reduced, but overlapping of serviced areas is inevitable
Solution Approach 1:
The polar map and zone assignments are pre-planned before the robot begins cleaning. The base station calculates the optimal sequence of zones and cells to minimize redundancy. This preliminary planning eliminates overlapping coverage without requiring complex real-time decision-making hardware, maintaining simple device architecture while preventing waste.
4Area of stationary object
If robot operates for extended periods to cover entire workspace, then total coverage increases, but battery charging requirements increase
Solution Approach 1:
The workspace is divided into zones that can be completed in single battery cycles. The robot systematically progresses through assigned zones rather than traversing the entire workspace continuously. This segmentation allows the robot to achieve substantial coverage within energy constraints, reducing charging frequency while maximizing productive operation time.
Data Source
AI summary
Methods for minimizing redundancy of surface coverage of a workspace using a robotic device and a base station are presented, the methods including: creating a polar map of the workspace defined by a number of cells; creating a policy by dividing the workspace into a number of zones each defined by a portion of the number of cells and by ordering the number of zones for surface coverage; selecting a zone of the number of zones for surface coverage by the robotic device; creating a cell matrix of the portion of number of cells representing the selected zone; assigning the selected zone to the robotic device; covering the selected zone by the robotic device; reporting an actual zone coverage to the base station; updating a coverage matrix corresponding with the cell matrix of the selected zone to indicate coverage; and calculating a penalty.