Robotic Cleaner Relocalization Using Multimodal Path Exclusion
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Solution Overview
Problem
Existing robotic cleaners face challenges in localization, particularly in dynamic environments where sensors may be blocked, leading to failures in localization techniques like Markov localization, which assume a static environment and struggle with multimodal distributions.
Innovation Solution
A robotic device equipped with sensors, cameras, and processors that capture spatial data, infer location probabilities, and update movement paths to exclude previously cleaned areas, using probabilistic methods and sensor data to navigate and clean efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If Markov localization is used with unimodal Gaussian distribution assumption, then the localization algorithm is computationally simple, but it cannot represent multimodal position distributions and fails in dynamic environments
Solution Approach 1:
The patent changes the fundamental parameter of probability distribution representation from unimodal Gaussian to multimodal distribution. This allows the system to represent multiple possible locations simultaneously, resolving the contradiction by maintaining computational tractability while dramatically improving localization reliability in dynamic environments where the robot may be uncertain about its position among multiple possibilities.
Solution Approach 2:
The patent introduces dynamic adaptability to the localization system by allowing the probability distribution to transition between unimodal and multimodal states based on environmental conditions. The system dynamically adjusts its representation of position uncertainty, switching from simple Gaussian when confident to multimodal when facing ambiguity, thus resolving the contradiction between simplicity and reliability.
2Measurement precision
If continuous tracking of robotic cleaner movement is used, then localization accuracy is maintained, but the system cannot recover if tracking is lost and requires known initial position
Solution Approach 1:
The patent performs preliminary action by pre-building a map of the environment with multiple possible locations and their characteristics before tracking failure occurs. When tracking is lost, the system can immediately query this pre-prepared information to re-localize, eliminating the need for continuous tracking and enabling recovery without knowing the initial position.
Solution Approach 2:
The patent implements feedback mechanisms where sensor data continuously compares actual observations against the pre-built map to confirm or update position hypotheses. This feedback loop allows the system to maintain location awareness even after tracking loss by constantly validating positions against environmental features, resolving the contradiction between tracking precision and recovery capability.
3Stability of the object's composition
If sensors block landmarks for extended periods, then localization fails in static environment assumptions, but the environment is actually dynamic
Solution Approach 1:
The patent explicitly models the environment as dynamic rather than static, allowing landmarks and obstacles to change positions over time. The localization algorithm accounts for temporal changes in environmental features, enabling reliable localization even when sensors temporarily block moving landmarks, thus resolving the contradiction between stability assumption and actual dynamic reliability.
Data Source
AI summary
Provided is a robotic device, including: a chassis; a set of wheels; one or more motors to drive the set of wheels; a controller in communication with the one or more motors; one or more surface cleaning tools; at least one sensor; a camera; one or more processors; a medium storing instructions that when executed by the one or more processors effectuate operations including: capturing, with the camera of the robotic device, spatial data of surroundings of the robotic device; generating, with the one or more processors of the robotic device, a movement path based on the spatial data of the surroundings; inferring, with the one or more processors of the robotic device, a location of the robotic device; and updating, with the one or more processors of the robotic device, the movement path to exclude locations of the movement path that the robotic device has previously been located.