scholarly journals Actions, Observations, and Decision-Making: Biologically Inspired Strategies for Autonomous Aerial Vehicles

2004 ◽  
Author(s):  
Greg Pisanich ◽  
Corey Ippolito ◽  
Laura Plice ◽  
Larry Young ◽  
Benton Lau
Keyword(s):  
Decision Making ◽  
Biologically Inspired ◽  
Aerial Vehicles ◽  
Autonomous Aerial Vehicles

scholarly journals A Bio-inspired Motivational Decision Making System for Social Robots Based on the Perception of the User

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2691 ◽  
Author(s):  
Marcos Maroto-Gómez ◽  
Álvaro Castro-González ◽  
José Castillo ◽  
María Malfaz ◽  
Miguel Salichs
Keyword(s):  
Decision Making ◽  
Human Robot Interaction ◽  
Learning Mechanisms ◽  
Biologically Inspired ◽  
Robot Interaction ◽  
Detection Mechanism ◽  
Learned Behaviors ◽  
Decision Making System ◽  
Real Robot ◽  
Self Learning

Nowadays, many robotic applications require robots making their own decisions and adapting to different conditions and users. This work presents a biologically inspired decision making system, based on drives, motivations, wellbeing, and self-learning, that governs the behavior of the robot considering both internal and external circumstances. In this paper we state the biological foundations that drove the design of the system, as well as how it has been implemented in a real robot. Following a homeostatic approach, the ultimate goal of the robot is to keep its wellbeing as high as possible. In order to achieve this goal, our decision making system uses learning mechanisms to assess the best action to execute at any moment. Considering that the proposed system has been implemented in a real social robot, human-robot interaction is of paramount importance and the learned behaviors of the robot are oriented to foster the interactions with the user. The operation of the system is shown in a scenario where the robot Mini plays games with a user. In this context, we have included a robust user detection mechanism tailored for short distance interactions. After the learning phase, the robot has learned how to lead the user to interact with it in a natural way.

From Sketches and Installations to Bioinspired 5D Printing Models

2021 ◽  
pp. 365-387
Author(s):  
Silvia Titotto
Keyword(s):  
Decision Making ◽  
Additive Manufacturing ◽  
Future Development ◽  
Smart Cities ◽  
Virtual Prototyping ◽  
Interdisciplinary Approach ◽  
Biologically Inspired ◽  
Digital Twins ◽  
Cad Cam ◽  
The Impact

This chapter opens up discussions upon the relevance of interaction of representations and data visualization modes for smart cities design, planning, and development that occur beyond paper and computer drawing. Although many practitioners usually relate smart cities and digital twins design exclusively to CAD/CAM/CAE and BIM methods, processes, and tools, a wider pool of techniques and forms of expression might be the key to a more accurate and comprehensive way of simulating the several kinds of alterations that happen in the planned built environment. The chapter deals with the study of concepts that relate to both physical and virtual prototyping, which underlines an interdisciplinary approach to design and the impact of integrating biologically inspired principles from different backgrounds to the field of smart cities design. In this regard, biomimetics and additive manufacturing may play key roles in smart city's modeling design and the frontier technology of 5D printing reveals real-time decision-making programmable 4D printing process as a potential future development.

scholarly journals Selection of Unmanned Aerial Vehicles by Using Multicriteria Decision-Making for Defence

2020 ◽  
Vol 2020 ◽  
pp. 1-11 ◽  
Author(s):  
Mustafa Hamurcu ◽  
Tamer Eren
Keyword(s):  
Decision Making ◽  
Unmanned Aerial Vehicles ◽  
Selection Process ◽  
Critical Role ◽  
Multicriteria Decision Making ◽  
Critical Factor ◽  
Unmanned Systems ◽  
Multicriteria Decision ◽  
Aerial Vehicles ◽  
Selection Of

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.

Vehicle Health Inferencing Using Feature-Based Neural-Symbolic Networks

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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