Autonomous Robot Behavior Zones for Obstacle and Stuck Avoidance
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Solution Overview
Problem
Autonomous mobile robots face challenges such as getting stuck or encountering obstacles during cleaning missions, leading to inefficient operation and potential damage, with existing systems failing to effectively manage these issues through behavior control zones.
Innovation Solution
The system identifies sensor events to create behavior control zones, allowing the robot to avoid certain areas and adjust its behavior based on user input and sensor data, using a user interface to visualize and modify these zones.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the autonomous mobile robot operates autonomously to clean the environment, then productivity is improved, but the robot may encounter obstacles or get stuck leading to reliability deterioration
Solution Approach 1:
The system performs preliminary actions by identifying problematic locations during initial cleaning missions and creating behavior control zones before issues arise. Sensor events are recorded and analyzed to predict potential obstacles or stuck situations, allowing the robot to preemptively adjust its behavior in identified high-risk areas.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring sensor events during cleaning missions, analyzing the data to identify patterns of obstacles or problematic areas, and using this information to dynamically adjust behavior control zones. This closed-loop feedback improves both productivity and reliability over time.
2Reliability
If the robot encounters obstacles and triggers escape behavior, then reliability is improved by avoiding damage, but productivity deteriorates due to time loss
Solution Approach 1:
The system prepares for potential obstacles by pre-defining behavior control zones based on historical sensor data and identified problematic areas. When the robot enters these zones, escape behaviors are preemptively triggered or modified, reducing the need for reactive escape maneuvers and associated productivity losses.
Solution Approach 2:
The system modifies escape behavior parameters dynamically based on location within behavior control zones. By adjusting escape thresholds, response times, and behavior intensity based on historical data from similar locations, the robot optimizes the balance between damage avoidance and productivity.
3Reliability
If behavior control zones are implemented to restrict robot access, then reliability is improved, but device complexity increases
Solution Approach 1:
The environment is segmented into discrete behavior control zones based on sensor event locations and patterns. Each zone is independently defined and managed, allowing the system to handle complexity in a modular fashion rather than requiring global environmental analysis.
Solution Approach 2:
The system creates simplified representations of problematic areas as behavior control zones, copying essential characteristics from sensor data without requiring complete environmental modeling. This abstraction reduces computational complexity while maintaining reliability benefits.
4Reliability
If sensor data is collected and analyzed to create behavior control zones, then reliability is improved, but loss of time increases due to data processing
Solution Approach 1:
The system performs partial data analysis by focusing only on relevant sensor events and locations that indicate potential problems. Rather than analyzing all sensor data comprehensively, the system identifies and processes only the subset of data necessary for creating effective behavior control zones, reducing processing time while maintaining reliability.
Data Source
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AI summary
A method includes receiving sensor data collected by an autonomous mobile robot as the autonomous mobile robot moves about an environment, the sensor data being indicative of sensor events and locations associated with the sensor events. The method includes identifying a subset of the sensor events based on the locations. The method includes providing, to a user computing device, data indicative of a recommended behavior control zone in the environment, the recommended behavior control zone containing a subset of the locations associated with the subset of the sensor events. The method includes defining, in response to a user selection from the user computing device, a behavior control zone such that the autonomous mobile robot initiates a behavior in response to encountering the behavior control zone, the behavior control zone being based on the recommended behavior control zone.