Generating Certification Evidence for Autonomous Aerial Vehicles Decision-Making

The unmanned systems have been seeing a significant boom in the last ten years in different areas together with technological developments. One of the unmanned systems is unmanned aerial vehicles (UAVs). UAVs are used for reconnaissance and observation in the military areas and play critical role in attack and destroy missions. These vehicles have been winning more features together with developing technology in todays world. In addition, they have been varying with different features. A systematic and efficient approach for the selection of the UAV is necessary to choose a best alternative for the critical tasks under consideration. The multicriteria decision-making (MCDM) approaches that are analytic processes are well suited to deal intricacy in selection of alternative vehicles. This study also proposes an integrated methodology based on the analytic hierarch process (AHP) and technique for order preference by similarity to ideal solution (TOPSIS) to evaluate UAV alternatives for selection process. Firstly, AHP, a MCDM method, is used to determine the weights of each critical factor. Subsequently, it is utilized with the TOPSIS approach to rank the vehicle alternatives in the decision problem. Result of the study shows that UAV-1 was selected as the most suitable vehicle. In results, it is seen that the weights of the evaluation criteria found by using AHP affect the decision-making process. Finally, the validation and sensitivity analysis of the solution are made and discussed.

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Vehicle Health Inferencing Using Feature-Based Neural-Symbolic Networks

Volume 3: Nonlinear Estimation and Control; Optimization and Optimal Control; Piezoelectric Actuation and Nanoscale Control; Robotics and Manipulators; Sensing; System Identification (Estimation for Automotive Applications, Modeling, Therapeutic Control in Bio-Systems); Variable Structure/Sliding-Mode Control; Vehicles and Human Robotics; Vehicle Dynamics and Control; Vehicle Path Planning and Collision Avoidance; Vibrational and Mechanical Systems; Wind Energy Systems and Control ◽

10.1115/dscc2013-3831 ◽

2013 ◽

Author(s):

Christopher M. Aasted ◽

Sunwook Lim ◽

Rahmat A. Shoureshi

Keyword(s):

Historical Data ◽

Fault Tolerant ◽

Current System ◽

Diagnostic Information ◽

Aerial Vehicles ◽

Feature Based ◽

Incident Data ◽

Autonomous Aerial Vehicles ◽

Diagnostics System

In order to optimize the use of fault tolerant controllers for unmanned or autonomous aerial vehicles, a health diagnostics system is being developed. To autonomously determine the effect of damage on global vehicle health, a feature-based neural-symbolic network is utilized to infer vehicle health using historical data. Our current system is able to accurately characterize the extent of vehicle damage with 99.2% accuracy when tested on prior incident data. Based on the results of this work, neural-symbolic networks appear to be a useful tool for diagnosis of global vehicle health based on features of subsystem diagnostic information.

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Computational architecture for autonomous decision-making in unmanned aerial vehicles

Micro- and Nanotechnology Sensors, Systems, and Applications X ◽

10.1117/12.2304506 ◽

2018 ◽

Cited By ~ 1

Author(s):

Shankar Sankararaman ◽

Kai Goebel

Keyword(s):

Decision Making ◽

Unmanned Aerial Vehicles ◽

Aerial Vehicles ◽

Autonomous Decision ◽

Computational Architecture

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A decision-making framework for safe operations of unmanned aerial vehicles in urban scenarios

Annual Conference of the PHM Society ◽

10.36001/phmconf.2020.v12i1.1190 ◽

2020 ◽

Vol 12 (1) ◽

pp. 11

Author(s):

Marcos Quiñones ◽

Timothy Darrah ◽

Gautam Biswas ◽

Chetan Kulkarni

Keyword(s):

Decision Making ◽

Unmanned Aerial Vehicles ◽

Environmental Conditions ◽

System Performance ◽

Trajectory Optimization ◽

Contingency Management ◽

The State ◽

Urban Mobility ◽

Aerial Vehicles ◽

Number Of Factors

This paper presents a decision-making scheme at the level of individual unmanned aerial vehicles (UAVs) with the goal of maintaining safe operations for urban mobility. The decision-making approach for a single UAV will consider the risks associated with the current trajectory given the existing environmental conditions and the state of the vehicle. The proposed scheme combines the analysis of system performance, environmental conditions, and mission level parameters for contingency management, i.e., make a determination on: (1) to abort mission and land safely; (2) re-plan current mission in full or abbreviated form; and (3) change mission. A path planning and trajectory optimization algorithm with the goal of minimizing the overall risk of mission failure by considering a number of factors such as the uncertainties in the environment and operating state of the vehicle is proposed. We will consider the mission failure as the loss of control of the vehicle resulting in a collision with other objects or a crash into the ground. An offline part of the framework generates an initial mission plan by considering the state of the vehicle, the environmental, conditions, and the static features of a map of the environment. Once the vehicle takes off, the risk of mission’ failure associated with the remaining trajectory is re-computed in an online framework to assess whether re-planning is required or not. A key challenge that we consider in this paper is to study the effects of multiple interacting subsystems of the UAV on system performance, especially under degraded conditions.

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