Autonomous Agent Task Scheduling Using Sensor-Based Priority Updates
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Solution Overview
Problem
Autonomous systems, such as Autonomous Mobile Robots (AMRs), face inefficiencies in processing resource utilization due to static task priority rules that do not dynamically consider sensor data, leading to suboptimal scheduling decisions.
Innovation Solution
Implementing a task priority scheduling system that utilizes sensor data and fleet management information to dynamically determine task priorities, generating collision-free trajectories and navigation paths by integrating sensor data from AMRs and environmental sensors into a centralized or decentralized environment model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If static priority rules are used for task scheduling, then system simplicity is maintained, but processing resource utilization efficiency deteriorates
Solution Approach 1:
The patent implements dynamic task priority adjustment based on real-time sensor data and environmental conditions. The scheduling system transitions from static priority rules to a dynamic model where task priorities are continuously updated according to current sensor readings, environmental factors, and task criticality assessments, thereby improving resource utilization efficiency while managing system complexity through structured dynamic evaluation
Solution Approach 2:
The system changes the parameter of task priority from a fixed static value to a dynamic value that varies based on sensor data inputs. By introducing parameters such as sensor data quality, environmental conditions, and real-time task criticality into the priority determination process, the system optimizes processing resource allocation without requiring complete redesign of the scheduling architecture
2Ease of operation
If static priority rules are used for task scheduling, then implementation simplicity is maintained, but navigation efficiency deteriorates
Solution Approach 1:
The patent introduces dynamic priority adjustment mechanisms that respond to real-time sensor data and environmental conditions. The scheduling system continuously evaluates task priorities based on current navigation context, obstacle detection data, and task criticality, enabling efficient navigation responses while maintaining implementation simplicity through structured dynamic evaluation frameworks
3Device complexity
If static priority rules are used for task scheduling, then system simplicity is maintained, but collision avoidance capability deteriorates
Solution Approach 1:
The patent implements a feedback mechanism where sensor data from the environment continuously informs task priority adjustments. The system monitors sensor inputs, environmental conditions, and task execution status, then feeds this information back into the scheduling decision process to dynamically adjust priorities for collision avoidance and safety-critical tasks, thereby improving reliability while managing complexity through structured feedback loops
Data Source
AI summary
Techniques are disclosed for task priority scheduling and resource allocation of autonomous agents. The scheduling may utilize sensor data characteristics to facilitate scheduling decisions. The present disclosure also provides refinement of task priority scheduling utilizing fleet management information-based scene and environment information, such as by using information available to the fleet management controller.