Autonomous Mission Planner and Supervisor (AMPS) for UAVs
- Dr. James Crowder, Colorado Engineering, Inc.
John N. Carbone, Electrical and Computer Engineering Dept, Southern Methodist University
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. The proposed AMPS herein will describe mechanisms for employing continuous monitoring capabilities and continuously learning from multiple Unmanned Aerial Vehicles (UAVs) while coordinating activities when necessary to more rapidly and more effectively achieve mission objectives.
Full access to this whitepaper requires completing and submitting the following form: