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 navigation and task completion.
Innovation Solution
A method involving a robot that captures and processes sensor data to recognize areas, generate maps, and plan movement paths, including iterative linear segments and rotations, to enhance navigation and task efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If autonomous robots use traditional mapping and navigation methods, then they can operate in environments, but the navigation efficiency and task completion speed are insufficient
Solution Approach 1:
The robot performs preliminary mapping of the environment before executing navigation tasks. The map is constructed in advance using sensor data, allowing the robot to plan optimal paths without real-time exploration delays, thus improving task completion efficiency while reducing navigation time.
Solution Approach 2:
The navigation system dynamically adjusts movement paths based on real-time sensor feedback and map data. The robot can adapt its route during execution to avoid obstacles or optimize for different task requirements, maintaining high productivity while minimizing navigation time through flexible path replanning.
2Measurement precision
If robots collect comprehensive sensor data for accurate mapping, then map accuracy improves, but data processing complexity and time increase
Solution Approach 1:
The mapping process is segmented into discrete processing steps: sensor data acquisition, feature extraction, map construction, and validation. Each segment handles specific aspects of data processing independently, reducing overall complexity while maintaining map accuracy through systematic processing of comprehensive sensor data.
Solution Approach 2:
An intermediate data structure or processing layer is introduced between raw sensor data and the final map. This intermediary organizes and pre-processes sensor inputs before map construction, simplifying the complexity of handling comprehensive sensor data while preserving the accuracy needed for precise environmental representation.
3Productivity
If robots use complex path planning algorithms for optimal navigation, then navigation efficiency improves, but computational load and processing time increase
Solution Approach 1:
Optimal paths are planned in advance using pre-constructed maps before the robot begins navigation tasks. This preliminary path planning allows complex computational algorithms to execute when the robot is stationary or low-power, improving navigation efficiency without increasing energy consumption during active movement.
Solution Approach 2:
The path planning system dynamically adjusts its computational intensity based on task requirements and environmental complexity. For simple environments, lighter algorithms are used to minimize energy consumption, while complex scenarios trigger more intensive planning only when necessary, optimizing the balance between navigation efficiency and energy usage.
Data Source
AI summary
Provided is a method including: capturing, with at least one sensor of a robot, 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 observing 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.