Mapping for autonomous mobile robots
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Solution Overview
Problem
Autonomous mobile robots face challenges in efficiently navigating and cleaning environments due to the lack of effective mapping and data sharing, leading to errors and inefficiencies in task performance and fleet management.
Innovation Solution
The development of an intelligent robot-facing map that allows autonomous mobile robots to collect and share data on environmental features, enabling them to plan paths, avoid errors, and prioritize cleaning areas based on feature states and types, while integrating with other smart devices for improved navigation and control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If autonomous mobile robots rely only on immediate sensor responses for navigation, then the robot can react quickly to detected features, but the robot encounters error conditions and has poor task performance due to lack of intelligent path planning
Solution Approach 1:
The system performs preliminary actions by constructing a map of the environment and identifying features before the robot navigates. The controller uses this pre-collected mapping data to plan paths and anticipate potential error conditions, rather than reacting only to immediate sensor inputs. This allows the robot to avoid obstacles and features that could cause errors before encountering them.
Solution Approach 2:
The patent introduces mapping data as an intermediary between the robot's sensors and its navigation system. Instead of directly reacting to sensor data, the controller references pre-processed mapping information that has already identified and categorized environmental features. This intermediary layer enables intelligent decision-making by providing structured information about the environment.
2Productivity
If autonomous mobile robots collect and process mapping data from previous missions, then the robot can intelligently plan paths and avoid error conditions, but the robot requires more complex data collection and processing capabilities
Solution Approach 1:
The system performs preliminary data collection during cleaning missions by continuously gathering sensor data and constructing environmental maps. Feature identification and categorization are performed in advance, allowing the robot to make quick navigation decisions during subsequent missions without time-consuming real-time analysis.
Solution Approach 2:
The mapping data collection and processing occur continuously during the robot's normal cleaning operations rather than as separate tasks. The robot collects sensor data, constructs maps, and identifies features while performing its primary cleaning function, eliminating the need for dedicated data collection time and maintaining continuous productive action.
3Productivity
If multiple autonomous mobile robots operate in the same environment without data sharing, then each robot can independently navigate, but fleet management efficiency is reduced and map construction is slower
Solution Approach 1:
The patent merges mapping data from multiple robots into a shared environmental map. Each robot contributes its sensor data and feature identifications to a collective understanding of the environment. This combined approach accelerates map construction and enables all robots to benefit from the cumulative knowledge, improving overall fleet management efficiency.
Solution Approach 2:
The mapping data structure is designed to be universal and applicable to multiple robots with different sensor capabilities. The system accommodates various robot types and sensor suites while maintaining a common map format that all robots can utilize, enabling data sharing across the entire fleet regardless of individual robot differences.
Data Source
AI summary
A method includes constructing a map of an environment based on mapping data produced by an autonomous cleaning robot in the environment during a first cleaning mission. Constructing the map includes providing a label associated with a portion of the mapping data. The method includes causing a remote computing device to present a visual representation of the environment based on the map, and a visual indicator of the label. The method includes causing the autonomous cleaning robot to initiate a behavior associated with the label during a second cleaning mission.