Autonomous mobile robot, method for docking autonomous mobile robot, control device and smart cleaning system
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Solution Overview
Problem
Autonomous mobile robots often fail to detect charging station signals when their starting position is far or isolated from the charging station, leading to manual intervention and poor user experience.
Innovation Solution
A method involving determining an effective region, identifying an optimal point on its boundary to move towards, and iteratively adjusting the robot's path to increase the likelihood of signal detection, using a control device with a processor and memory to execute this process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the autonomous mobile robot uses conventional signal-based docking method, then the docking process is simple and fast, but the robot cannot locate the charging station when starting position is far or isolated from it
Solution Approach 1:
The robot performs preliminary mapping and localization actions during its operation to build a cognitive map of the environment and identify potential charging station locations. This preliminary action enables the robot to proactively navigate to charging stations even when conventional signal-based methods fail due to distance or isolation, thereby improving docking reliability without proportionally increasing system complexity
Solution Approach 2:
The patent introduces an intermediary navigation system that acts as a mediator between the robot and the charging station. This intermediary system uses environmental mapping and position estimation to bridge the gap when direct signal detection fails, allowing the robot to locate and dock with charging stations in scenarios where conventional methods are ineffective
2Ease of operation
If the robot manually places the autonomous mobile robot on the charging station, then the robot can be charged, but the operation quality decreases and user experience deteriorates
Solution Approach 1:
The robot performs self-service by autonomously navigating to and docking with charging stations using its enhanced location system. The robot independently determines its position, plans navigation paths, and executes docking maneuvers without human intervention, thereby maintaining ease of operation while significantly improving automatic docking reliability through proactive position estimation and environmental mapping
3Length of stationary object
If the robot expands its detection range to cover isolated areas, then the robot can detect charging station signals from farther positions, but the signal strength decreases and detection accuracy deteriorates
Solution Approach 1:
The robot performs preliminary environmental mapping and position estimation actions that enable it to proactively navigate toward charging stations before relying on signal detection. By building a cognitive map and estimating positions in advance, the robot can extend its effective detection range to cover isolated areas while maintaining detection accuracy through predictive navigation rather than passive signal reception
Solution Approach 2:
The patent transitions from relying solely on signal strength detection (one-dimensional approach) to using multi-dimensional information including environmental features, position estimation, and navigation data. This dimensional expansion allows the robot to detect and locate charging stations in isolated areas with extended range while compensating for reduced signal strength through alternative spatial and contextual cues
Data Source
AI summary
In some examples, a method for docking an autonomous mobile robot includes: determining a first effective region, wherein the first effective region is defined by a boundary, and wherein the autonomous mobile robot is located in the first effective region; determining an optimal point from a plurality of candidate points on the boundary of the first effective region, wherein each candidate point defines a respective second effective region centering on the candidate point and overlapping with the first effective region to form a respective overlapping region, wherein the respective overlapping region associated with the optimal point is smallest among the respective overlapping regions associated with the plurality of candidate points; controlling the autonomous mobile robot to move to the optimal point; and repeating the above steps in one or more iterations until the autonomous mobile robot is within a preset distance from a charging station.