Networked Facility Robots for Real-Time Critical Condition Reporting
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Solution Overview
Problem
Existing facility monitoring systems lack effective real-time detection and response mechanisms for critical conditions, such as near misses and congestion, which can lead to safety incidents and inefficiencies in workflow management.
Innovation Solution
A system comprising autonomous mobile robots connected via a network that can detect critical conditions, switch to high-priority data recording, and coordinate with other robots for real-time response, including generating heat maps and requesting assistance, while also monitoring facility operations and reporting incidents to a user interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If robots continuously monitor facility operations in real-time, then detection speed of critical conditions improves, but energy consumption and system complexity increase
Solution Approach 1:
The robot system dynamically adjusts its monitoring behavior based on detected conditions. During normal operations, robots perform periodic monitoring at lower computational intensity. Upon detecting a critical condition indicator, the system automatically transitions to continuous high-priority monitoring mode, increasing detection speed only when necessary. This dynamic adaptation resolves the contradiction by maintaining reliability improvements while reducing overall system complexity and energy consumption during normal operations.
Solution Approach 2:
The system changes operational parameters based on detected conditions. When a critical condition is detected, the monitoring frequency, data collection rate, and communication priority are automatically increased. This parameter change allows the system to achieve high detection speed for critical events without maintaining continuously high resource consumption, thereby resolving the contradiction between detection speed and system complexity.
2Loss of time
If robots switch to high-priority real-time continuous data recording upon critical condition detection, then response time to critical conditions improves, but data management complexity and processing load increase
Solution Approach 1:
The robot system pre-configures multiple data recording modes and processing pipelines before critical conditions occur. When a critical condition is detected, the system immediately switches to the pre-prepared high-priority recording mode, eliminating the need to configure complex data management systems in real-time. This preliminary preparation allows rapid response while keeping the actual switching action simple, resolving the contradiction between response time and data management complexity.
Solution Approach 2:
Upon detecting a critical condition, the system activates continuous high-priority data recording which captures more data than strictly necessary (excessive action). This ensures that all potentially relevant information is captured without needing to make complex real-time decisions about what data to prioritize. The excess data is managed through pre-established filtering and processing rules, simplifying the response mechanism while ensuring complete data capture for safety-critical events.
3Reliability
If multiple robots coordinate and request assistance upon critical condition detection, then safety response effectiveness improves, but communication overhead and coordination complexity increase
Solution Approach 1:
Multiple robots that detect the same critical condition merge their responses by coordinating through a centralized server. Instead of each robot independently managing complex coordination with all other robots, the system combines their detection data and response actions through a central coordination point. This merging approach improves safety response effectiveness through multi-robot collaboration while reducing individual robot coordination complexity by offloading the coordination burden to the server.
Solution Approach 2:
A centralized server acts as an intermediary between detecting robots and assisting robots. When a critical condition is detected, the server receives the alert, processes the situation, and coordinates the response by directing appropriate assisting robots to the location. This intermediary approach improves safety response effectiveness by enabling comprehensive multi-robot coordination while simplifying the communication overhead for individual robots, as they only need to communicate with the server rather than directly with each other.
Data Source
AI summary
A system for facility monitoring and reporting to improve safety using one or more robots includes: a network; a plurality of autonomous mobile robots operating in a facility, the robots configured to monitor facility operation, the robots further configured to detect a predetermined critical condition, the robots operably connected to the network; a server operably connected to the robots over the network; and an individual robot operably connected to the server over the network, the individual robot operating in the facility, the robots not comprising the individual robot, the individual robot configured to monitor facility operation; wherein the robots are configured to regularly produce a regular report under normal operating conditions, the report displaying data received from the server, wherein the robots are further configured to produce to the server a critical condition report upon occurrence of the critical condition.