Autonomous Robot Dock Placement With Image-Based Obstacle Checks
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Solution Overview
Problem
Autonomous mobile robots face challenges in validating dock locations due to obstacles, which can obstruct or interfere with their docking behavior, leading to potential charging failures and debris evacuation issues.
Innovation Solution
A system comprising a mobile cleaning robot with a drive system and a controller circuit that detects obstacles in the docking area using images from cameras or sensors, generating notifications and recommendations for users to clear the area or reposition the docking station, and suggesting alternative locations based on docking failure rates and wireless communication signal strength.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the mobile robot autonomously docks at the docking station, then charging and debris evacuation functions are enabled, but obstacles in the docking area can obstruct or interfere with docking behavior causing charging failures
Solution Approach 1:
The system performs preliminary validation of the docking area by capturing images and detecting obstacles before the robot attempts to dock. This advance detection allows the system to identify potential docking failures due to obstacles and notify users in advance, preventing actual docking failures.
Solution Approach 2:
The system captures images of the docking area, processes them to detect obstacles, and provides feedback to users about the docking area status. This feedback loop enables users to clear obstacles or reposition the docking station, thereby improving docking reliability.
2Ease of operation
If the docking station is placed in a convenient location, then ease of operation is improved, but obstacles may be present in the docking area interfering with robot docking
Solution Approach 1:
The system provides visual feedback through captured images showing whether the docking area is clear of obstacles. This allows users to evaluate the suitability of convenient docking station locations and make informed decisions about placement that balance ease of operation with docking reliability.
Solution Approach 2:
By validating the docking area in advance through image capture and obstacle detection, the system allows users to set up the docking station in convenient locations while ensuring the area is clear of obstacles before actual docking operations begin.
3Reliability
If the robot validates the docking area using image processing, then docking area clearance is ensured, but device complexity increases due to additional sensors and processing requirements
Solution Approach 1:
The system uses the robot's existing camera, which serves multiple functions including navigation, mapping, and now docking area validation. This multi-functional use of the camera minimizes additional hardware complexity while achieving reliable docking area validation.
Solution Approach 2:
The robot uses its own onboard camera to validate the docking area, eliminating the need for separate validation devices. The robot performs self-validation by capturing and processing images of the docking area using its existing imaging capabilities.
Data Source
AI summary
Described herein are systems, devices, and methods for validating location of a docking station for docking a mobile robot. In an example, a mobile robot system includes a docking station and a mobile cleaning robot. The mobile cleaning robot includes a drive system to move the mobile cleaning robot about an environment including a docking area within a distance of the docking station, and a controller circuit to detect, from an image of the docking area, a presence or absence of one or more obstacles in the docking area. A notification may be generated to inform a user about the detected obstacles. The mobile device may generate a recommendation to the user to clear the docking area or reposition the docking station, or suggest one or more candidate locations for placing the docking station.