Unmanned Vehicles Technology and Embodied Intelligence Systems for Intelligent Transportation

Topic Information

Dear Colleagues,

At present, the new unmanned vehicles technology and embodied intelligence systems for intelligent transportation are in a period of change. In the foreseeable near future, unmanned systems represented by UGV (Unmanned Ground Vehicle) and UAV (Unmanned Aerial Vehicle) will build new ground and air transportation, logistics, and operation systems, which will have great application potential in various fields of industry and agriculture. Unmanned driving systems (on the open road and the closed road) and intelligent agricultural machinery and equipment are representative intelligent transportation applications. 'Interactive' perception, 'learnable' cognition and decision making, and 'self-growth' behavior control are three important features of embodied intelligence. Correspondingly, multi-sensor (Lidar, millimeter wave radar, and optical sensor) and multi-source information fusion technology, SLAM technology, and bionic vision technology are applied to the perception stage. Brain-imitating intelligence and end-to-end deep learning neural networks are applied to the cognition and decision-making stage. Disturbance self-rejection control, integration control, bionic formation control, and manned/unmanned hybrid cooperative control technology are applied to the behavior control stage.

The scope of solicitation includes, but is not limited to, the following:

• Automatic driving, intelligent driving, and unmanned driving; embodied intelligence;
• Perception, cognition, and behavior;
• SLAM (Simultaneous Localization and Mapping);
• Lidar, millimeter-wave radar, RGB and RGB-D machine vision perception, and multi-spectral optical perception; 'interactive' perception;
• 'Learnable' cognition and decision making;
• 'Self-growth' behavior control; biologically inspired visual perception;
• Multi-sensor and multi-source information fusion;
• Brain-imitating intelligence and end-to-end deep learning neural networks;
• Disturbance observer and active disturbance rejection control;
• Perception, decision-making and control integration technology;
• Biologically inspired formation control;
• Hybrid cooperative control of manned/unmanned systems.

Dr. Jian Chen
Dr. Min Xia
Dr. Hui Xie
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Aerospace aerospace	2.1	3.4	2014	24 Days	CHF 2400	Submit
Applied Sciences applsci	2.5	5.3	2011	17.8 Days	CHF 2400	Submit
Drones drones	4.4	5.6	2017	21.7 Days	CHF 2600	Submit
Electronics electronics	2.6	5.3	2012	16.8 Days	CHF 2400	Submit
Eng eng	-	2.1	2020	28.3 Days	CHF 1200	Submit
Remote Sensing remotesensing	4.2	8.3	2009	24.7 Days	CHF 2700	Submit
Sensors sensors	3.4	7.3	2001	16.8 Days	CHF 2600	Submit

MDPI Topics is cooperating with Preprints.org and has built a direct connection between MDPI journals and Preprints.org. Authors are encouraged to enjoy the benefits by posting a preprint at Preprints.org prior to publication:

1. Immediately share your ideas ahead of publication and establish your research priority;
2. Protect your idea from being stolen with this time-stamped preprint article;
3. Enhance the exposure and impact of your research;
4. Receive feedback from your peers in advance;
5. Have it indexed in Web of Science (Preprint Citation Index), Google Scholar, Crossref, SHARE, PrePubMed, Scilit and Europe PMC.

Published Papers (2 papers)

Order results

Content type Publication Date

Result details

Normal Extended Compact

Journals

All Aerospace Applied Sciences Drones Electronics Eng Remote Sensing Sensors

Show export options expand_more Show export options expand_less

Select all

Export citation of selected articles as:

Plain Text BibTeX BibTeX (without abstracts) Endnote Endnote (without abstracts) Tab-delimited RIS

Error

Oops... you haven't selected anything for export.

Ok

clear

19 pages, 11959 KiB  

Open AccessArticle

Learning Autonomous Navigation in Unmapped and Unknown Environments

by Naifeng He, Zhong Yang, Chunguang Bu, Xiaoliang Fan, Jiying Wu, Yaoyu Sui and Wenqiang Que

Sensors 2024, 24(18), 5925; https://doi.org/10.3390/s24185925 - 12 Sep 2024

Abstract

Autonomous decision-making is a hallmark of intelligent mobile robots and an essential element of autonomous navigation. The challenge is to enable mobile robots to complete autonomous navigation tasks in environments with mapless or low-precision maps, relying solely on low-precision sensors. To address this, [...] Read more.

Autonomous decision-making is a hallmark of intelligent mobile robots and an essential element of autonomous navigation. The challenge is to enable mobile robots to complete autonomous navigation tasks in environments with mapless or low-precision maps, relying solely on low-precision sensors. To address this, we have proposed an innovative autonomous navigation algorithm called PEEMEF-DARC. This algorithm consists of three parts: Double Actors Regularized Critics (DARC), a priority-based excellence experience data collection mechanism, and a multi-source experience fusion strategy mechanism. The algorithm is capable of performing autonomous navigation tasks in unmapped and unknown environments without maps or prior knowledge. This algorithm enables autonomous navigation in unmapped and unknown environments without the need for maps or prior knowledge. Our enhanced algorithm improves the agent’s exploration capabilities and utilizes regularization to mitigate the overestimation of state-action values. Additionally, the priority-based excellence experience data collection module and the multi-source experience fusion strategy module significantly reduce training time. Experimental results demonstrate that the proposed method excels in navigating the unmapped and unknown, achieving effective navigation without relying on maps or precise localization. Full article

(This article belongs to the Topic Unmanned Vehicles Technology and Embodied Intelligence Systems for Intelligent Transportation)

►▼ Show Figures

Figure 1

37 pages, 10534 KiB  

Open AccessArticle

Optimization of Urban Target Area Accessibility for Multi-UAV Data Gathering Based on Deep Reinforcement Learning

by Zhengmiao Jin, Renxiang Chen, Ke Wu, Tengwei Yu and Linghua Fu

Drones 2024, 8(9), 462; https://doi.org/10.3390/drones8090462 - 5 Sep 2024

Abstract

Unmanned aerial vehicles (UAVs) are increasingly deployed to enhance the operational efficiency of city services. However, finding optimal solutions for the gather–return task pattern under dynamic environments and the energy constraints of UAVs remains a challenge, particularly in dense high-rise building areas. This [...] Read more.

Unmanned aerial vehicles (UAVs) are increasingly deployed to enhance the operational efficiency of city services. However, finding optimal solutions for the gather–return task pattern under dynamic environments and the energy constraints of UAVs remains a challenge, particularly in dense high-rise building areas. This paper investigates the multi-UAV path planning problem, aiming to optimize solutions and enhance data gathering rates by refining exploration strategies. Initially, for the path planning problem, a reinforcement learning (RL) technique equipped with an environment reset strategy is adopted, and the data gathering problem is modeled as a maximization problem. Subsequently, to address the limitations of stationary distribution in indicating the short-term behavioral patterns of agents, a Time-Adaptive Distribution is proposed, which evaluates and optimizes the policy by combining the behavioral characteristics of agents across different time scales. This approach is particularly suitable for the early stages of learning. Furthermore, the paper describes and defines the “Narrow-Elongated Path” Problem (NEP-Problem), a special spatial configuration in RL environments that hinders agents from finding optimal solutions through random exploration. To address this, a Robust-Optimization Exploration Strategy is introduced, leveraging expert knowledge and robust optimization to ensure UAVs can deterministically reach and thoroughly explore any target areas. Finally, extensive simulation experiments validate the effectiveness of the proposed path planning algorithms and comprehensively analyze the impact of different exploration strategies on data gathering efficiency. Full article

(This article belongs to the Topic Unmanned Vehicles Technology and Embodied Intelligence Systems for Intelligent Transportation)

►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less

Select all

Export citation of selected articles as:

Plain Text BibTeX BibTeX (without abstracts) Endnote Endnote (without abstracts) Tab-delimited RIS

 

Error

Oops... you haven't selected anything for export.

Ok

clear

Displaying articles 1-2