Category: Whitepaper

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Fuzzy Collision Avoidance Algorithm for UAVs

Authors:
Dr. James A. Crowder, Systems Fellow, Colorado Engineering Inc.
Dr. John Carbone, Department of Electrical and Computer Engineering, Southern Methodist University

Abstract:
Multi-vehicle, multi-route conflict (collision) avoidance is an issue that will become prevalent over the next decade. With the dramatic increase in the number of UAV drones and number of separate companies planning to employ UAVs for everything from aerial reconnaissance to delivery to search and rescue operations, understanding how to simply and effectively keep UAVs from coming in conflict with each other is a major concern. One of the issues associated with conflict avoidance is to not have to tax the computational, memory, and energy usage of the UAV to employ a conflict resolution strategy. Here we present a fuzzy-based algorithm for detection and implementation of conflict avoidance for UAVs/ drones that will not adversely affect the power, weight, or size of the individual units.

What we propose is a simple, fuzzy logic-driven algorithm for collision/conflict avoidance for UAVs or small drones. The use of a random, fuzzy-based algorithm allows for fast, efficient computation. There would be a software agent on the drones/UAVs that would manage the collision/conflict avoidance system and send information to the flight control system for speed/directional change, depending on the output of the collision/conflict avoidance algorithms. Included will be a discussion of the ISCAN© radar system capable of implanting these algorithms on small drones.

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[download id=”19961″]

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Artificial Intelligence Profiles for Foundational Counselor Training Sessions

Authors:
Dr. James Crowder, Systems Fellow, Colorado Engineering Inc.
Dr. Shelli Friess, LPC, NCC, ACS, School of Counseling, Walden University

Abstract:
Training for practicing counselors involves training to help people learn to cope more effectively with mental health issues and developmental issues, along with life issues in general. Counselors are trained in a variety of techniques, based on the best available research and minimal standards based on professional accreditation. However, live training in the beginning, with actual patients is difficult since it is not practical to have counselors “practice” on people with actual psychological issues. Presented here is a proposed training system, called the “Cognitive, Interactive, Psychological Training System (CIPTS), which provides artificially intelligent profiles instantiated as “avatars for interaction with counselors,” based on a set of different personality profiles created by a team of leading counselor educators and represent a variety of psychological, social, and biopsychosocial health issues that can be used to help train counselors in a non-live, non-threatening environment, but yet valuable.

Full access to this whitepaper requires submitting the form on the following page. Click the link below to continue.

[download id=”19955″]

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Human Cognition and Artificial Intelligence: The Artificial Prefrontal Cortex Revisited

Authors:
Dr. James A. Crowder, Systems Fellow, Colorado Engineering Inc.
Dr. Shelli Friess, LPC, NCC, ACS, School of Counseling, Walden University

Abstract:
Many researchers have postulated that human cognition is implemented by a multitude of relatively small, special purpose processes, almost always unconscious communication between them is rare and over a narrow bandwidth. Coalitions of such processes find their way into consciousness. This limited capacity work space available for our cognition serves to broadcast the message of the coalition to all the unconscious processors within the human brain, to recruit other processors to join in handling the current novel situation, or in solving the current problem. Therefore, consciousness in this theory allows us to deal with novelty or problematic situations that can’t be dealt with efficiently, or at all, by habituated unconscious processes. It provides access to appropriately useful resources, thereby solving the relevance problem. Here we present the design and testing of and Artificial Prefrontal Cortex (APC) model for use cognitive state transition and management in artificial cognition systems.

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[download id=”19932″]

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Methodologies for Continuous Life-long Machine Learning for AI Systems

Authors:
Dr. James A. Crowder, Systems Fellow, Colorado Engineering Inc.
John N. Carbone, Electrical and Computer Engineering Department, Southern Methodist University

Abstract:
Current machine learning architectures, strategies, and methods are typically static and non-interactive, making them incapable of adapting to changing and/or heterogeneous data environments, either in real-time, or in near-real-time. Typically, in real-time applications, large amounts of disparate data must be processed, learned from, and actionable
intelligence provided in terms of recognition of evolving activities….

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[download id=”6912″]

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Enhancing Prosthetic Musculotendinous Proprioception Utilizing Multidisciplinary Artificially Intelligent Learning Approach

Authors:
John N. Carbone, Electrical and Computer Engineering Department, Southern Methodist University
Ryan A. Carbone, Biological Sciences, Baylor University
Dr. James A. Crowder, Systems Fellow, Colorado Engineering Inc.

Abstract:
Historically, research shows analysis, characterization, and classification of complex heterogeneous non-linear systems and interactions have been difficult to accurately understand and effectively model. Advanced biophysical and biomechanical prosthesis research shows that development of patient specific physiologically meaningful musculotendinous proprioception would generate a marked impact on reflex control, fine volitional motor control, and overall user experience.

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[download id=”6928″]

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Data Analytics: The Big Data Analytics Process (BDAP) Architecture

Authors: Dr. James A. Crowder, Systems Fellow, Colorado Engineering Inc.
John N. Carbone, Electrical and Engineering Dept., Southern Methodist University

Abstract:
Analysis, characterization and classification of large, heterogeneous, complex data sets (the “Big Data” problem) has been a major source of R&D for decades. Current and future space, air, and ground systems are growing in complexity and capability, creating a serious challenge to operators who monitor, maintain, and utilize systems in an ever growing network of assets…

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[download id=”6892″]

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Bi-Objective Study for the Assignment of Unmanned Aerial Vehicles to Targets

Authors:
Ryan D. Friese, Pacific NW National Laboratory and U.S. Department of Energy
Dr. James A. Crowder, Systems Fellow, Colorado Engineering Inc.
Howard Jay Siegel, Electrical and Computer Engineering and Computer Science Departments, Colorado State University
John N. Carbone, Electrical and Computer Engineering Department, Southern Methodist University

Abstract:
Historically, research shows that multi-vehicle, multiconstraint, surveillance problems require a combinatorial optimization solution. In many of these surveillance missions, the overall objective is to provide plans for surveillance tasks for unmanned aerial vehicles (UAVs) visiting or “surveilling” targets across geographically distributed areas.

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[download id=”6926″]

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Autonomous Mission Planner and Supervisor (AMPS) for UAVs

Authors:
Dr. James A. Crowder, Systems Fellow, Colorado Engineering Inc.
John N. Carbone, Electrical and Computer Engineering Department, Southern Methodist University

Abstract:
To reduce mission manning and increase adaptability and evolvability for managing current operations of Unmanned Aerial Vehicle (UAV), Miniature Air-Launched Decoy (MALD) and future systems, an Autonomous Mission Planner and Supervisor (AMPS), based upon an Intelligent Information Agent (I2A) architecture for real-time, adaptive, decision making is proposed. AMPS will use a naturalistic decision-making approach to comparing sensor inputs to a priori situational “scripts” and previously collected data to improve determination/decision and execution time of appropriate actions thereby, enhancing quality and minimizing time to achieve each related mission goal….

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[download id=”6917″]

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Artificial Neural Diagnostics and Prognostics: Self-Soothing in Cognitive Systems

Authors:
Dr. James A. Crowder, Systems Fellow, Colorado Engineering Inc.
John N. Carbone, Electrical and Computer Engineering Dept., Southern Methodist University

Abstract:
Self-diagnostics and prognostics in multi-agent processing systems is explored in the context of self-soothing concepts in neuropsychology. This is one of the first steps to facilitate systems-level thinking in AI. Autonomous or semi-autonomous system must be able to understand, at a system wide level, how every part of the system is influencing the other parts of the system.

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[download id=”6921″]

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Basics of Possibilistic PSYOPS for Decoy/Countermeasure Methods

Author:
Dr. James A. Crowder, Systems Fellow, Colorado Engineering Inc.

Abstract:
Situational/threat assessment strategies have been studied for generations. Typically, these threat assessments utilize Bayesian belief networks and inference engines, based on decision tree technologies, to determine the likelihood of different deployment strategies and prevention methods (psyops). These are typically represented as a directed “acyclic” graph and utilize joint probability distributions, which are typically based on incomplete information as to the probabilities involved in various aspects of the current mission parameters.

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[download id=”7203″]