Perception, Decision-Making and Control of Intelligent Unmanned System

A special issue of Drones (ISSN 2504-446X).

Deadline for manuscript submissions: 25 October 2024 | Viewed by 3111

Special Issue Editors

College of Aeronautics, Nanjing University of Aeronautics and Astronautics, Nanjing, China
Interests: dynamics and control; guidance navigation and control
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Guest Editor
School of Astronautics, Harbin Institute of Technology, Harbin 150001, China
Interests: perception and decision-making
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
1. College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
2. Shanghai Key Laboratory of Aerospace Intelligent Control Technology, Shanghai 200233, China
Interests: Intelligent perception

Special Issue Information

Dear Colleagues,

In recent years, the field of unmanned systems has witnessed remarkable advancements, leading to the emergence of intelligent unmanned systems capable of perceiving, making decisions, and autonomously controlling their actions. These systems, often referred to as autonomous or intelligent unmanned systems, have revolutionized various industries, including transportation, agriculture, surveillance, and exploration. They have the potential to enhance efficiency, reduce human error, and perform tasks that are too dangerous or tedious for humans. Unmanned Aerial Vehicles (UAVs), commonly known as drones, have experienced significant advancements in recent years thanks to the integration of intelligent capabilities. These intelligent unmanned systems have revolutionized the field of UAVs, enabling a wide range of applications across various industries, such as aerial photography and videography, precision agriculture, search and rescue operations, infrastructure inspection, environmental monitoring, and so on.

At the core of these intelligent unmanned systems lie three fundamental components: perception, decision making, and control. Perception involves the ability to gather and interpret information from the environment using various sensors such as cameras, lidar, radar, and other specialized sensors. This information is then processed to create a representation of the system's surroundings, enabling it to understand and interact with the world. Once the perception phase is complete, the intelligent unmanned system moves on to the decision-making stage. These decisions can range from simple tasks, such as obstacle avoidance, to complex actions, such as route planning, target identification, or collaborative decision making in a multi-agent environment. Finally, the control component comes into play, where the intelligent unmanned system executes the decisions made during the decision-making phase.

The integration of perception, decision making, and control in intelligent unmanned systems is a highly interdisciplinary field, drawing upon expertise from various domains such as artificial intelligence, robotics, computer vision, machine learning, and control theory. Researchers and engineers in this field strive to develop algorithms, architectures, and techniques that enable unmanned systems to operate autonomously, adapt to dynamic environments, and interact safely and effectively with humans and other agents. The challenges in developing perception, decision-making, and control capabilities for intelligent unmanned systems are numerous. These include the need for robust and accurate perception algorithms, efficient and scalable decision-making frameworks, real-time control strategies, and the ability to handle uncertainty and unexpected situations.

In conclusion, the field of perception, decision making, and control of intelligent unmanned systems holds immense potential for transforming various industries and enabling a wide range of applications. As technology continues to advance, the development of robust and reliable algorithms and systems will pave the way for the deployment of intelligent unmanned systems that can operate autonomously, adapt to changing environments, and collaborate with humans and other agents effectively.

As technology continues to advance, we can expect further innovations in areas such as swarm intelligence, collaborative decision making, and enhanced autonomy, opening up new possibilities for UAVs in various industries and domains.

This Special Issue focuses on the design of intelligent drone systems, including research into control systems, artificial intelligence, decision- making, UAV modelling and simulation, etc. The aim of this Special Issue is to provide a venue for drones research on artificial intelligence, unmanned systems, robotics, automation, intelligent systems, etc. All papers will be published in an open access format following peer review.

Both research papers and overview papers are welcome. Topics of interest include (but are not limited to) the following:

  • Intelligence unmanned systems;
  • Artificial intelligence;
  • Robotics and automation;
  • Machine learning;
  • Safe learning;
  • Formation control;
  • Group formation tracking;
  • Bipartite cooperative;
  • SLAM (simultaneous localization and mapping);
  • Collaborative perception and positioning;
  • Decision making;
  • Drones;
  • Path planning;
  • Image fusion;
  • Feature fusion;
  • Sensor fusion;
  • Scene understanding.

We are pleased to invite you to submit manuscripts to this MDPI Drones Special Issue on “Perception, Decision-Making and Control of Intelligent Unmanned System”.

Prof. Dr. Shuang Li
Dr. Chengchao Bai
Dr. Jinzhen Mu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Drones is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (2 papers)

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Research

16 pages, 4801 KiB  
Article
A Cooperative Decision-Making Approach Based on a Soar Cognitive Architecture for Multi-Unmanned Vehicles
by Lin Ding, Yong Tang, Tao Wang, Tianle Xie, Peihao Huang and Bingsan Yang
Drones 2024, 8(4), 155; https://doi.org/10.3390/drones8040155 - 18 Apr 2024
Viewed by 838
Abstract
Multi-unmanned systems have demonstrated significant applications across various fields under complex or extreme operating environments. In order to make such systems highly efficient and reliable, cooperative decision-making methods have been utilized as a critical technology for successful future applications. However, current multi-agent decision-making [...] Read more.
Multi-unmanned systems have demonstrated significant applications across various fields under complex or extreme operating environments. In order to make such systems highly efficient and reliable, cooperative decision-making methods have been utilized as a critical technology for successful future applications. However, current multi-agent decision-making algorithms pose many challenges, including difficulties understanding human decision processes, poor time efficiency, and reduced interpretability. Thus, a real-time online collaborative decision-making model simulating human cognition is presented in this paper to solve those problems under unknown, complex, and dynamic environments. The provided model based on the Soar cognitive architecture aims to establish domain knowledge and simulate the process of human cooperation and adversarial cognition, fostering an understanding of the environment and tasks to generate real-time adversarial decisions for multi-unmanned systems. This paper devised intricate forest environments to evaluate the collaborative capabilities of agents and their proficiency in implementing various tactical strategies while assessing the effectiveness, reliability, and real-time action of the proposed model. The results reveal significant advantages for the agents in adversarial experiments, demonstrating strong capabilities in understanding the environment and collaborating effectively. Additionally, decision-making occurs in milliseconds, with time consumption decreasing as experience accumulates, mirroring the growth pattern of human decision-making. Full article
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26 pages, 4764 KiB  
Article
The Situation Assessment of UAVs Based on an Improved Whale Optimization Bayesian Network Parameter-Learning Algorithm
by Weinan Li, Weiguo Zhang, Baoning Liu and Yicong Guo
Drones 2023, 7(11), 655; https://doi.org/10.3390/drones7110655 - 1 Nov 2023
Cited by 2 | Viewed by 1513
Abstract
To realize unmanned aerial vehicle (UAV) situation assessment, a Bayesian network (BN) for situation assessment is established. Aimed at the problem that the parameters of the BN are difficult to obtain, an improved whale optimization algorithm based on prior parameter intervals (IWOA-PPI) for [...] Read more.
To realize unmanned aerial vehicle (UAV) situation assessment, a Bayesian network (BN) for situation assessment is established. Aimed at the problem that the parameters of the BN are difficult to obtain, an improved whale optimization algorithm based on prior parameter intervals (IWOA-PPI) for parameter learning is proposed. Firstly, according to the dependencies between the situation and its related factors, the structure of the BN is established. Secondly, in order to fully mine the prior knowledge of parameters, the parameter constraints are transformed into parameter prior intervals using Monte Carlo sampling and interval transformation formulas. Thirdly, a variable encircling factor and a nonlinear convergence factor are proposed. The former and the latter enhance the local and global search capabilities of the whale optimization algorithm (WOA), respectively. Finally, a simulated annealing strategy incorporating Levy flight is introduced to enable the WOA to jump out of the local optimum. In the experiment for the standard BNs, five parameter-learning algorithms are applied, and the results prove that the IWOA-PPI is not only effective but also the most accurate. In the experiment for the situation BN, the situations of the assumed mission scenario are evaluated, and the results show that the situation assessment method proposed in this article is correct and feasible. Full article
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