System and method for predicting an adequate ratio of unmanned vehicles to operators

• US 10,031,520 B2
• Filed: 03/02/2016
• Issued: 07/24/2018
• Est. Priority Date: 11/08/2011
• Status: Active Grant

• Current Assignee: the united states of america as represented by the secretary of the navy
• Original Assignee: the united states of america as represented by the secretary of the navy
• Inventors: Rodas, Maria Olinda
• Primary Examiner(s): Ojha, Ajay
• Application Number:
  US15/058,917
• Publication Number:
  US 20160252902A1
• Time in Patent Office: 874 Days
• CPC Class Codes: A61B 5/18 for vehicle drivers or machine operators
• CPC Class Codes: A61B 5/7264 Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• CPC Class Codes: B60K 28/06 responsive to incapacity of driver
• CPC Class Codes: G05D 1/0027 involving a plurality of vehicles, e.g. fleet or convoy travelling fleet control of land vehicles from a control room G05D1/0297; traffic control systems for road vehicles G08G1/00; for marine craft G08G3/00; for aircraft G08G5/00
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

The present invention is a computer decision tool for use in a system for controlling a team of unmanned vehicles. The computer decision tool includes a system performance model for receiving interface usability, automation level and algorithm efficiency variables and an operator performance model. The operator performance model receives task management efficiency and decision making strategy or DM efficiency variables. The system performance model is responsive to the interface usability, automation level and algorithm efficiency variables for providing a system performance status signal. The operator performance model is responsive to task management efficiency and DM strategy variables for providing an operator performance status signal. An operator capacity decision model is responsive to the system performance and operator performance status signals and a workload variable for providing a decision signal representative of an adequate team size or an optimal recommendation, such as changing the team size.

Claims

1. A method for predicting, by a computer decision tool given a plurality of operators, a plurality of unmanned vehicles and a given mission, said computer decision tool including a system performance module, an operator performance module, and a workload module, an adequate ratio of the plurality of unmanned vehicles to the operators, the method comprising the steps of:

A) determining effectiveness of a user interface by an interface usability decision node;

B) using the results of said step A) to determine whether there is an adequate level of automation by an adequate automation level decision node;

C) using the results of said step B) to determine algorithm efficiency by an algorithm efficiency decision node;

D) using the results of said step C) to determine an operator task management efficiency by an operator task management efficiency decision node;

E) using the results of said step D) to determine an operator decision making efficiency by an operator decision making efficiency decision node;

F) providing, by the system performance module, a system performance status signal responsive to an output from the interface usability decision node, an output from the adequate automation level decision node, and an output from the algorithm efficiency decision node;

G) providing, by the operator performance module, responsive to an output from the task management efficiency decision node and an output from the operator decision making efficiency decision node, an operator performance status signal;

H) providing, by the workload module, a team size decision signal representing the predicted adequate ratio of the unmanned vehicles to the operator, the team size decision signal indicating whether to change a team size of the unmanned vehicles, responsive to an increase team size decision output from an increase team size decision node and responsive to the system performance and operator performance status signals, allowing optimal allocation of unmanned vehicles and operator resources; and

,

said step H) occurring before the mission occurs, to allow for deployment of the plurality of operators and the team size sufficient to accomplish the mission.

2. The method of claim 1 wherein said step A) is calculated using outputs of nature and utility nodes, and wherein as decisions are made by said decision nodes are populated and repopulated with probabilities.

3. In a system for controlling a team of unmanned vehicles and at least one operator, a computer decision tool for predicting an optimum ratio of the unmanned vehicles to the at least one operator for a given mission, the computer decision tool comprising:

a system performance module configured to provide a system performance status signal responsive to an interface usability decision output from an interface usability decision node, an adequate automation decision output from an adequate automation decision node, and an algorithm efficiency decision signal output from an algorithm efficiency decision node;

an operator performance module configured to provide an operator performance status signal responsive to a task management efficiency decision output from a task management efficiency decision node and an operator decision making efficiency decision output from an operator decision making efficiency decision node; and

,

a workload module configured to provide a team size decision signal representing the predicted adequate ratio of the unmanned vehicles to the operator for the mission, the team size decision signal indicating whether to change a team size of the unmanned vehicles, responsive to an increase team decision output from an increase team decision node and responsive to the system performance and operator performance status signals, allowing optimal allocation of unmanned vehicles and operator resources before the mission occurs.

4. The decision tool of claim 3, including:

a utility node for each decision node;

multiple nature nodes including;

observable nature nodes representative of one or more node indications from the node group of Total Task Time, Wait Times due to Loss of Situation Awareness (WTSA), Frequency of Reassignment, ID Task Success Rate, Neglect Time (NT), System Interruption, UV Health Status, Utilization Time (UT), Interaction Times (IT), Wait Times due to Queue (WTQ), Elimination Task Success Rate, Total time to Target Elimination, Team Heterogeneity and Team Size nodes; and

unobservable nature nodes representative of one or more node indications from the group of System Performance, Operator Performance, Workload, Situation Awareness (SA), Information Overload, Automation level, Enemy Team, Task Complexity and Task Heterogeneity nodes.