Method for constructing a map while performing work
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Solution Overview
Problem
Autonomous robots face challenges in effectively mapping and navigating complex environments due to limitations in sensor data processing and path planning, leading to inefficient coverage of workspaces and potential obstacles.
Innovation Solution
A method and system for autonomous robots that utilize a combination of sensors and processors to capture and process data on object positions and movements, generate workspace maps, and plan movement paths, including iterative linear segments and rotations, to efficiently cover workspaces while avoiding obstacles and adapting to changing environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If autonomous robots use sensor data processing and path planning methods to navigate environments, then navigation capability is improved, but workspace coverage efficiency deteriorates due to computational overhead and planning delays
Solution Approach 1:
The patent segments the workspace into discrete grid cells and divides the navigation problem into local path planning segments. The robot processes sensor data in discrete time steps, creating a simplified representation of the environment that enables faster decision-making while maintaining navigation reliability through systematic exploration of grid cells.
Solution Approach 2:
The robot performs partial mapping by focusing on relevant workspace areas rather than complete environmental modeling. The path planning uses limited lookahead steps and prioritizes unexplored or partially explored regions, achieving sufficient navigation capability without the computational burden of exhaustive planning.
2Loss of information
If robots perform comprehensive sensor data capture and processing, then environmental awareness is improved, but operational speed deteriorates due to extensive data analysis requirements
Solution Approach 1:
The patent extracts only the essential features from sensor data for path planning purposes. Rather than processing complete environmental models, the system extracts key information such as obstacle positions, workspace boundaries, and unexplored regions, discarding redundant data to maintain operational speed while preserving necessary environmental awareness.
Solution Approach 2:
The system changes the parameter representation of environmental data by converting continuous sensor readings into discrete grid cell states. This parameter transformation reduces data complexity and enables faster processing while maintaining sufficient environmental awareness for navigation decisions.
3Reliability
If robots use complex path planning algorithms to avoid obstacles, then navigation safety is improved, but task completion time deteriorates due to repeated planning computations
Solution Approach 1:
The robot performs preliminary exploration to identify safe corridors and open spaces before executing main cleaning tasks. By预先 mapping navigable areas and obstacle-free zones, the system reduces the need for repeated obstacle avoidance computations during task execution, maintaining navigation safety while reducing overall task completion time.
Solution Approach 2:
The path planning operates continuously in the background during robot operation, updating the workspace model and adjusting paths in real-time without interrupting the cleaning task. This continuous planning approach maintains navigation safety through constant environmental awareness while minimizing task completion time by avoiding stop-and-plan cycles.
Data Source
AI summary
Provided is a method including: capturing first data indicative of the position of the robot in relation to objects within the workspace and second data indicative of movement of the robot; recognizing, with a processor of the robot, a first area of the workspace based on at least one of: a first part of the first data and a first part of the second data; generating, with the processor of the robot, at least part of a map of the workspace based on at least one of: the first part of the first data and the first part of the second data; generating, with the processor of the robot, a first movement path covering at least part of the first recognized area; actuating, with the processor of the robot, the robot to move along the first movement path.