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Machines
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Advances in Path Planning and Autonomous Navigation
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Advances in Path Planning and Autonomous Navigation

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Automation and Control Systems".

Deadline for manuscript submissions: 31 January 2025 | Viewed by 5616

Special Issue Editor

Dr. Endrowednes Kuantama

E-Mail Website
Guest Editor
School of Computing, Macquarie University, Sydney 2109, Australia
Interests: internet of drones; design and implementation of unmanned aerial vehicles for aerial manipulation; sensing; recognition; and path planning for autonomous drone; machine learning and data analytics; SLAM algorithms and robotics control system
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

An essential factor in the development of robotics, autonomous vehicles, and unmanned aerial vehicles (UAVs) is the determination of safe and intelligent navigation in complex and dynamic environments, known as path planning or motion planning. From the initial position to the desired goal, efficient and effective path-planning algorithms are crucial in enabling a sequence of actions or motions to be carried out while avoiding obstacles or constraints in the environment. Certain factors must be considered in path planning, e.g., the minimal distance travelled, evasion of hindrance, consideration of dynamic obstacles, or optimization of other performance metrics. Path planning and autonomous navigation have attracted considerable interest and attention in recent years owing to several factors:

  • Rapid developments in robotics, sensor technologies, and computing power have created new possibilities for autonomous navigation.
  • The rise of autonomous vehicles, including self-driving cars, drones, and others, has led to a strong demand for efficient and safe path-planning algorithms.
  • Industrial automation and robotics: robots are increasingly employed for logistics, warehouse automation, and manufacturing tasks.
  • Applications in unstructured environments: path planning and autonomous navigation are crucial when human intervention is restricted or impractical.

The present high interest in path planning and autonomous navigation can be attributed to the desire to cultivate intelligent, efficient, and safe systems that can operate independently in varied surroundings. It is predicted that further high-quality research and technological improvements in this field will significantly affect industries, transportation, and various aspects of our everyday lives. This Special Issue aims to collect innovative contributions on this subject. Specifically, contributions may address the following aspects of “Path Planning and Autonomous Navigation”:

  1. Dynamic and motion planning for mobile robots and UAVSs
  2. SLAM
  3. Energy-efficient path planning
  4. Real-time and adaptive path planning
  5. Autonomous navigation systems using reinforcement learning
  6. Deep reinforcement learning
  7. Localization and mapping algorithms
  8. Multi-agent systems and swarm robotics
  9. Navigation in GPS-denied environments
  10. Efficient exploration and mapping

"Advances in Path Planning and Autonomous Navigation" aims to provide a leading resource for researchers and practitioners in the field, and we invite authors to contribute their expertise and research findings to this Special Issue.

Dr. Endrowednes Kuantama
Guest Editor

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. Machines 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 2400 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.

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (4 papers)

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Research

21 pages, 5855 KiB  
Article
Optimal Trajectory Planning for Wheeled Robots (OTPWR): A Globally and Dynamically Optimal Trajectory Planning Method for Wheeled Mobile Robots
by Dingji Luo, Xuchao Huang, Yucan Huang, Mingda Miao and Xueshan Gao
Machines 2024, 12(10), 668; https://doi.org/10.3390/machines12100668 - 24 Sep 2024
Viewed by 430
Abstract
In recent years, with the widespread application of indoor inspection robots, efficient motion planning has become crucial. Addressing the issue of discontinuous and suboptimal robot trajectories resulting from the independent nature of global and local planning, we propose a novel optimal path-planning method [...] Read more.
In recent years, with the widespread application of indoor inspection robots, efficient motion planning has become crucial. Addressing the issue of discontinuous and suboptimal robot trajectories resulting from the independent nature of global and local planning, we propose a novel optimal path-planning method for wheeled mobile robots. This method leverages differential flatness to reduce dimensionality and decouple the problem, achieving globally optimal, collision-free paths in a two-dimensional flat output space through diagonal search and polynomial trajectory optimization. Comparative experiments in a simulated environment demonstrate that the proposed improved path search algorithm reduces search time by 46.6% and decreases the number of visited nodes by 43.1% compared to the original algorithm. This method not only ensures the optimal path and efficient planning but also ensures that the robot’s motion trajectory satisfies the dynamic constraints, verifying the effectiveness of the proposed optimal path planning algorithm for wheeled mobile robots. Full article
(This article belongs to the Special Issue Advances in Path Planning and Autonomous Navigation)
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27 pages, 27661 KiB  
Article
Improvement and Fusion of D*Lite Algorithm and Dynamic Window Approach for Path Planning in Complex Environments
by Yang Gao, Qidong Han, Shuo Feng, Zhen Wang, Teng Meng and Jingshuai Yang
Machines 2024, 12(8), 525; https://doi.org/10.3390/machines12080525 - 1 Aug 2024
Viewed by 964
Abstract
Effective path planning is crucial for autonomous mobile robots navigating complex environments. The “global–local” coupled path planning algorithm exhibits superior global planning capabilities and local adaptability. However, these algorithms often fail to fully realize their potential due to low efficiency and excessive constraints. [...] Read more.
Effective path planning is crucial for autonomous mobile robots navigating complex environments. The “global–local” coupled path planning algorithm exhibits superior global planning capabilities and local adaptability. However, these algorithms often fail to fully realize their potential due to low efficiency and excessive constraints. To address these issues, this study introduces a simpler and more effective integration strategy. Specifically, this paper proposes using a bi-layer map and a feasible domain strategy to organically combine the D*Lite algorithm with the Dynamic Window Approach (DWA). The bi-layer map effectively reduces the number of nodes in global planning, enhancing the efficiency of the D*Lite algorithm. The feasible domain strategy decreases constraints, allowing the local algorithm DWA to utilize its local planning capabilities fully. Moreover, the cost functions of both the D*Lite algorithm and DWA have been refined, enabling the fused algorithm to cope with more complex environments. This paper conducts simulation experiments across various settings and compares our method with A_DWA, another “global–local” coupled approach, which combines A* and DWA. D_DWA significantly outperforms A_DWA in complex environments, despite a 7.43% increase in path length. It reduces the traversal of risk areas by 71.95%, accumulative risk by 80.34%, global planning time by 26.98%, and time cost by 35.61%. Additionally, D_DWA outperforms the A_Q algorithm, a coupled approach validated in real-world environments, which combines A* and Q-learning, achieving reductions of 1.34% in path length, 67.14% in traversal risk area, 78.70% in cumulative risk, 34.85% in global planning time, and 37.63% in total time cost. The results demonstrate the superiority of our proposed algorithm in complex scenarios. Full article
(This article belongs to the Special Issue Advances in Path Planning and Autonomous Navigation)
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26 pages, 8458 KiB  
Article
An Advanced IBVS-Flatness Approach for Real-Time Quadrotor Navigation: A Full Control Scheme in the Image Plane
by Ahmed Alshahir, Khaled Kaaniche, Mohammed Albekairi, Shahr Alshahr, Hassen Mekki, Anis Sahbani and Meshari D. Alanazi
Machines 2024, 12(5), 350; https://doi.org/10.3390/machines12050350 - 19 May 2024
Viewed by 924
Abstract
This article presents an innovative method for planning and tracking the trajectory in the image plane for the visual control of a quadrotor. The community of researchers working on 2D control widely recognizes this challenge as complex, because a trajectory defined in image [...] Read more.
This article presents an innovative method for planning and tracking the trajectory in the image plane for the visual control of a quadrotor. The community of researchers working on 2D control widely recognizes this challenge as complex, because a trajectory defined in image space can lead to unpredictable movements of the robot in Cartesian space. While researchers have addressed this problem for mobile robots, quadrotors continue to face significant challenges. To tackle this issue, the adopted approach involves considering the separation of altitude control from the other variables, thus reducing the workspace. Furthermore, the movements of the quadrotor (pitch, roll, and yaw) are interdependent. Consequently, the connection between the inputs and outputs cannot be reversed. The task complexity becomes significant. To address this issue, we propose the following scenario: When the quadrotor is equipped with a downward-facing camera, flying at high altitude is sensible to spot a target. However, to minimize disturbances and conserve energy, the quadrotor needs to descend in altitude. This can result in the target being lost. The solution to this problem is a new methodology based on the principle of differential flatness, allowing the separation of altitude control from the other variables. The system first detects the target at high altitude, then plots a trajectory in the image coordinate system between the acquired image and the desired image. It is crucial to emphasize that this step is performed offline, ensuring that the image processing time does not affect the control frequency. Through the proposed trajectory planning, complying with the constraints of differential flatness, the quadrotor can follow the imposed dynamics. To ensure the tracking of the target while following the generated trajectory, the proposed control law takes the form of an Image Based Visual Servoing (IBVS) scheme. We validated this method using the RVCTOOLS environment in MATLAB. The DJI Phantom 1 quadrotor served as a testbed to evaluate, under real conditions, the effectiveness of the proposed control law. We specifically designed an electronic card to transfer calculated commands to the DJI Phantom 1 control joystick via Bluetooth. This card integrates a PIC18F2520 microcontroller, a DAC8564 digital-to-analogue converter, and an RN42 Bluetooth module. The experimental results demonstrate the effectiveness of this method, ensuring the precise tracking of the target as well as the accurate tracking of the path generated in the image coordinate system. Full article
(This article belongs to the Special Issue Advances in Path Planning and Autonomous Navigation)
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15 pages, 23883 KiB  
Article
A Portable Artificial Robotic Nose for CO2 Concentration Monitoring
by Christyan Cruz Ulloa, David Orbea, Jaime del Cerro and Antonio Barrientos
Machines 2024, 12(2), 108; https://doi.org/10.3390/machines12020108 - 5 Feb 2024
Viewed by 2416
Abstract
The technological advancements in sensory systems and robotics over the past decade have facilitated the innovation of centralized systems for optimizing resource utilization and monitoring efficiency in inspection applications. This paper presents a novel system designed for gas concentration sensing in environments by [...] Read more.
The technological advancements in sensory systems and robotics over the past decade have facilitated the innovation of centralized systems for optimizing resource utilization and monitoring efficiency in inspection applications. This paper presents a novel system designed for gas concentration sensing in environments by implementing a modular artificial nose (emulating the inhalation and exhalation process) equipped with a strategically designed air capture centralization system based on computational fluid dynamics analysis (CFD). The system incorporates three gas identification sensors distributed within the artificial nose, and their information is processed in real-time through embedded systems. The artificial nose is hardware–software integrated with a quadruped robot capable of traversing the environment to collect samples, maximizing coverage area through its mobility and locomotion capabilities. This integration provides a comprehensive perspective on gas distribution in a specific area, enabling the efficient detection of substances in the surrounding environment. The robotic platform employs a graphical interface for real-time gas concentration data map visualization. System integration is achieved using the Robot Operating System (ROS), leveraging its modularity and flexibility advantages. This innovative robotic approach offers a promising solution for enhanced environmental inspection and monitoring applications. Full article
(This article belongs to the Special Issue Advances in Path Planning and Autonomous Navigation)
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