Task Allocation Control Using Sensor Trust in Human-Robot Teams
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current systems lack effective methods for safe and efficient collaboration between autonomous machines and human task agents in shared environments, particularly in dynamically changing industrial settings.
Innovation Solution
The implementation of a system that utilizes sensor reliability matrices, trust scores, and tolerance profiles to enable safe and efficient task allocation between autonomous machines and human task agents, incorporating machine learning algorithms for dynamic adaptation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If autonomous machines are deployed in shared environments with human task agents, then productivity and operational efficiency are improved, but safety risks and potential harmful interactions increase
Solution Approach 1:
The patent introduces a controller as an intermediary between autonomous machines and human task agents. The controller receives sensor data from both sources, processes it through a task performance model, and generates coordinated control instructions. This intermediary layer enables safe collaboration by mediating interactions and preventing harmful conflicts between autonomous and human operations in shared environments.
2Reliability
If sensor reliability assessment and trust score mechanisms are implemented, then safety and reliability of interactions are improved, but system complexity and computational requirements increase
Solution Approach 1:
The system performs preliminary assessment of sensor reliability and generates trust scores before executing task allocations. The controller evaluates the reliability of sensor data from autonomous machines and human agents in advance, establishing trust metrics that guide subsequent task assignments. This preliminary action ensures safety decisions are made based on pre-assessed reliability rather than reactive measures.
Solution Approach 2:
The patent implements continuous feedback mechanisms where sensor measurements from autonomous machines and human agents are constantly monitored and fed back to the controller. The task performance model processes this feedback to update trust scores and adjust task allocations dynamically. This closed-loop feedback system maintains reliability by adapting to changing conditions while managing complexity through structured information flow.
3Adaptability or versatility
If dynamic task allocation based on real-time sensor assessments is implemented, then adaptability to environmental changes is improved, but processing time and computational load increase
Solution Approach 1:
The patent implements dynamic task allocation where the controller continuously receives sensor data from autonomous machines and human agents, reassesses task requirements in real-time, and reallocates tasks based on current environmental conditions and agent capabilities. The task performance model dynamically adjusts control instructions to adapt to changing environments, ensuring optimal task distribution while maintaining responsive operation.
Solution Approach 2:
The system focuses computational resources on critical assessment parameters and essential task allocation decisions rather than processing all possible data comprehensively. The controller prioritizes key sensor measurements and trust score calculations that have the greatest impact on safety and task performance, performing sufficient rather than exhaustive analysis to maintain real-time responsiveness.
Data Source
AI summary
A controller including a processor configured to obtain a message from a task performing agent of a group of task performing agents allocated to a plurality of tasks, wherein the message comprises information about one or more assessments of the task performing agent, wherein the one or more assessments are based on a sensing process performed by one or more sensors of the task performing agent, wherein the task performing agent is an autonomous machine or a human agent equipped with sensors; and allocate a task of the plurality of tasks to the task performing agent, based on the information and based on whether the task performing agent is an autonomous machine or a human agent.