Autonomous Robot Behavior Zones for Obstacle Avoidance
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Solution Overview
Problem
Autonomous mobile robots, such as cleaning robots, often encounter obstacles that trigger escape behaviors or cause the robot to become stuck, leading to inefficiencies and potential damage.
Innovation Solution
The method involves receiving sensor data from the autonomous mobile robot as it moves, identifying specific sensor events based on their locations, and providing a recommended 'keep out' zone to the user to restrict the robot's access to certain areas. This zone can be defined and modified by the user based on the recommended zone.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the autonomous mobile robot autonomously navigates and cleans the environment, then the productivity is improved, but the robot may encounter obstacles that cause it to become stuck or trigger escape behaviors repeatedly
Solution Approach 1:
The system performs preliminary mapping and identifies problematic areas where the robot frequently gets stuck or triggers escape behaviors. These areas are pre-marked as behavior control zones before the robot encounters them during cleaning operations, allowing the robot to proactively avoid or handle these areas appropriately.
Solution Approach 2:
The system continuously monitors sensor data during robot operation, identifies sensor events indicating problematic conditions (getting stuck, escape behaviors), and updates the behavior control zones accordingly. This feedback loop allows the system to learn from past experiences and improve future performance.
2Object-affected harmful factors
If the robot encounters obstacles and triggers escape behaviors, then the robot can avoid immediate damage, but the cleaning mission is interrupted and time is lost
Solution Approach 1:
Problematic areas are identified and marked as behavior control zones in advance, so the robot can prepare appropriate responses before encountering obstacles in these areas, reducing the need for repeated escape behaviors and mission interruptions.
Solution Approach 2:
The robot autonomously learns from its own sensor data and experiences, automatically identifying problematic areas and creating behavior control zones without external intervention, thereby minimizing disruptions to cleaning missions.
3Loss of information
If the system provides detailed sensor data and recommended behavior control zones to the user, then the user can make informed decisions, but the device complexity increases
Solution Approach 1:
A computing device acts as an intermediary between the robot and the user, processing sensor data, generating behavior control zones, and presenting this information through a user interface. This intermediary handles the complexity of data processing while providing simplified information to the user.
Solution Approach 2:
The system automatically processes sensor data and generates behavior control zones without requiring complex user configuration or intervention, reducing the perceived complexity for users while maintaining comprehensive data analysis.
4Reliability
If the robot restricts access to certain areas using behavior control zones, then the robot avoids problematic areas, but the area coverage during cleaning is reduced
Solution Approach 1:
Behavior control zones are applied locally to specific problematic areas rather than globally restricting the entire environment. The robot maintains normal operation in areas without behavior control zones while applying special handling only where needed, preserving maximum cleanable area.
Solution Approach 2:
The system applies behavior control zones partially, only to areas where the robot has demonstrated problematic behavior. This selective approach avoids over-restriction and maintains access to areas that could be safely cleaned, balancing reliability with area coverage.
Data Source
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.