Escalating Hazard Response in Mobile Robots for Collapse Risk
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Solution Overview
Problem
Dynamically stable robots pose hazards due to unpredictable collapses in collaborative environments, making it challenging to ensure human safety without compromising efficiency.
Innovation Solution
A mobile robot employs an escalating hazard response strategy that includes deceleration, reconfiguration, and safe operating stops based on real-time environmental data to mitigate risks, balancing efficiency and safety.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a mobile robot operates in a collaborative environment with humans, then productivity and automation efficiency are improved, but safety hazards arise due to unpredictable robot collapses
Solution Approach 1:
The robot performs preliminary actions by detecting human presence and predicting potential collapse scenarios before they occur. The system proactively identifies at-risk humans and prepares safety responses in advance, rather than reacting only after a collapse begins. This allows the robot to maintain productivity while preemptively mitigating safety hazards.
Solution Approach 2:
The robot implements beforehand cushioning by creating virtual safety zones and predictive hazard buffers around detected humans. When a human is detected in a vulnerable position, the system预先 establishes protective parameters and potential escape routes, cushioning against the harmful effect of unpredictable collapses before they can cause harm.
2Object-affected harmful factors
If the robot implements safety measures to prevent unpredictable collapses, then human safety is improved, but operational efficiency and mobility are compromised
Solution Approach 1:
The robot applies partial action by implementing safety measures selectively rather than continuously. It uses machine learning to identify specific situations where collapse risk is elevated and applies safety interventions only in those contexts. This partial application of safety measures maintains operational efficiency while still protecting human safety when genuinely needed.
Solution Approach 2:
The safety system is dynamic and adaptive, adjusting its level of intervention based on real-time environmental assessment. The robot continuously monitors human positions, robot stability, and task requirements, dynamically modulating safety measures to match the actual risk level. This allows maximum operational efficiency when risk is low while providing enhanced safety when risk increases.
3Difficulty of detecting and measuring
If the robot uses complex hazard detection and response systems, then safety monitoring capability is improved, but device complexity increases
Solution Approach 1:
The robot employs universal sensors and processing units that serve multiple functions: hazard detection, human presence identification, robot state monitoring, and environmental mapping. This multi-functionality reduces the need for dedicated complex subsystems while maintaining comprehensive hazard detection capability. The same hardware infrastructure supports both productivity optimization and safety monitoring.
Solution Approach 2:
The system uses feedback loops where sensor data about human positions and robot states continuously inform hazard predictions, which in turn adjust robot behavior. This feedback mechanism allows the complex hazard detection capability to self-regulate and adapt without requiring proportionally complex control systems. The feedback-driven approach simplifies the overall architecture while maintaining high detection accuracy.
Data Source
AI summary
A method in accordance with at least some embodiments of the present technology includes determining first hazard information about a human in an environment at a first time. The method further includes decelerating a mobile robot in the environment based at least partially on the first hazard information. The method further includes determining second hazard information about the human at a second time after the first time. The method further includes reconfiguring the mobile robot based at least partially on the second hazard information. Reconfiguring the mobile robot includes moving the mobile robot from a standing configuration to a non-standing configuration. The method further includes determining third hazard information about the human at a third time after the second time. Finally, the method includes causing a safe operating stop of the mobile robot based at least partially on the third hazard information.