Orchestrator Module for Robotic Agent Coordination
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Solution Overview
Problem
Robotic process automation (RPA) systems lack coordination and fault tolerance, leading to inefficiencies and errors in executing repetitive tasks, as they are not designed to detect anomalies or optimize task execution without specific programming.
Innovation Solution
An orchestrator module using machine learning to manage and enhance the performance of multiple robotic agents by rescheduling tasks, detecting anomalies, and determining candidate tasks for automation, allowing for coordinated execution and error checking without exhaustive programming.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If robotic agents execute tasks using pre-programmed instructions, then task execution accuracy is improved, but the system loses fault tolerance and anomaly detection capability
Solution Approach 1:
An orchestrator module is introduced as an intermediary between robotic agents and tasks. This orchestrator learns the normal operational patterns of agents through machine learning and detects anomalies by comparing actual execution against learned patterns, enabling fault tolerance without interfering with the precision of individual agent executions
Solution Approach 2:
The system implements feedback mechanisms where the orchestrator continuously monitors agent performance and provides anomaly detection. The machine learning model learns from historical execution data and provides feedback about deviations from normal patterns, enabling the system to detect faults while maintaining execution accuracy
2Device complexity
If multiple robotic agents operate independently, then system simplicity is maintained, but coordination efficiency is lost
Solution Approach 1:
The orchestrator serves as a mediator that coordinates multiple robotic agents without requiring complex inter-agent communication. It manages task allocation, monitors performance, and optimizes resource utilization, achieving coordination efficiency while keeping individual agent design simple
Solution Approach 2:
The orchestrator module performs multiple functions including task allocation, performance monitoring, anomaly detection, and coordination optimization. This multi-functional approach consolidates complexity into a single component rather than distributing it across multiple agents
3Speed
If robotic agents process data without anomaly detection programming, then processing speed is improved, but error detection capability deteriorates
Solution Approach 1:
The orchestrator acts as an intermediary that separates data processing from anomaly detection. Robotic agents continue to process data at high speed without interruption, while the orchestrator independently monitors for anomalies by comparing execution patterns against learned normal behavior
Solution Approach 2:
The anomaly detection by the orchestrator operates continuously in parallel with agent task execution. This allows data processing to proceed without interruption while maintaining constant surveillance for errors, preserving both speed and detection capability
4Reliability
If exhaustive programming is used to detect all possible errors, then anomaly detection capability is improved, but system complexity and programming effort increase
Solution Approach 1:
The orchestrator uses machine learning to automatically learn normal operational patterns from historical data without requiring explicit programming of anomaly detection rules. The system serves itself by adapting to new patterns and scenarios automatically, reducing programming complexity while maintaining detection capability
Solution Approach 2:
Instead of programming specific anomaly detection rules for each scenario, the system changes the approach to learning parameters dynamically. The machine learning model adapts its detection parameters based on observed normal behavior, eliminating the need for exhaustive programming of all possible error conditions
Data Source
AI summary
Systems and methods for managing and enhancing the performance of a number of robotic agents. An orchestrator module receives work output from a number of robotic agents and determines whether efficiencies can be obtained by rescheduling tasks and/or steps executed by the various robotic agents. As well, the orchestrator learns the various actions and values used by the agents and can check for anomalous actions and/or values. A workstation operated by a human can also send its work output to the orchestrator and this output, along with the steps performed by the human, can be analyzed to determine if the task executed can be done by a robotic agent.