Mobile Robot Relocalization Using Building State Changes
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Solution Overview
Problem
Conventional methods for mobile robots to recover from mislocalization in building environments are labor-intensive and inefficient, often requiring manual intervention or reliance on limited sensor data, especially in complex or dynamic settings.
Innovation Solution
A mobile robot system that detects changes in its local environment, such as elevator states or environmental conditions, to autonomously identify its location by initiating and detecting changes in these states, using a combination of sensors and communication with building controllers to narrow down its possible location.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual techniques are used to recover mobile robot from mislocalization, then the robot can be relocalized, but the process becomes labor intensive and time consuming
Solution Approach 1:
The mobile robot autonomously performs relocalization by detecting changes in its environment (e.g., elevator states, environmental conditions) and identifying its location without human intervention. The robot independently initiates the relocalization process by communicating with building controllers to generate detectable state changes, eliminating the need for manual techniques and significantly reducing relocalization time.
2Reliability
If manual relocalization by trained operators is used, then localization can be recovered, but the operation becomes difficult when sensor data is limited or features are similar
Solution Approach 1:
The system introduces an intermediary mechanism (building automation system) that generates distinctive, controllable state changes in the environment (e.g., elevator door openings, lighting changes, HVAC adjustments). These intermediary actions create unique sensory signatures that make location identification easier and more reliable, even when the robot's sensor data is limited or when building features are similar across different locations.
3Ease of operation
If the robot uses existing sensor data for localization, then the process is simple, but localization fails in dynamic environments with unexpected events
Solution Approach 1:
The system transitions from static sensor data matching to dynamic environment interaction. The robot actively induces and detects temporal changes in the environment (such as elevator movements, door openings, lighting transitions) to create a dynamic localization process. This dynamic approach maintains simplicity while significantly improving reliability in changing environments, as the robot can distinguish its location based on the sequence and pattern of state changes rather than relying solely on static features that may be ambiguous or altered by unexpected events.
Data Source
AI summary
A mobile robot includes a mode of operation to recover from a localization error. The mobile robot detects a change in state in a local region proximate the mobile robot. The location of the mobile robot is identified based at least in part on the detected change in state. In one implementation, the mobile robot interfaces with a system controller of a building to initiate a change in state in a local region of a building.