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Advances in Robot Path Planning, Volume II
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Advances in Robot Path Planning, Volume II

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Robotics and Automation".

Deadline for manuscript submissions: 30 August 2024 | Viewed by 16390

Special Issue Editor

Prof. Dr. Jonghoek Kim

E-Mail Website
Guest Editor
Defense System Engineering Department, Sejong University, Seoul 05006, Republic of Korea
Interests: robotics; target tracking; multi-agent robotics; optimal estimation; path planning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Path planning is fundamental and crucial for various kinds of robots, such as autonomous vehicles, multiple robots, or robot arms. It is crucial to generate a safe path that avoids collision with obstacles or other robots, in the case of the path planning of multiple robots. Considering aerial or underwater robots, the safe path must be planned considering the 3D environment. The complexity of the path planning of a robot arm increases significantly as the number of degrees of freedom increases. Thus, safe paths must be generated for high-dimensional systems in a time-efficient manner. In practice, an obstacle may move, and thus a robot’s path must be replanned if necessary. Moreover, it is desirable to consider the dynamic model of a robot when generating a path for the robot. This Special Issue will present the recent research advances on these topics.

We welcome original research papers that focus on the theory, practice, and applications of robot path planning. Survey papers or tutorial papers on this topic are also encouraged.

Dr. Jonghoek Kim
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. Applied Sciences is an international peer-reviewed open access semimonthly 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.

Keywords

  • online and dynamic path planning
  • energy-efficient path planning
  • motion planning
  • SLAM
  • coverage problem
  • mobile robots
  • multiple robots
  • robot arms
  • underwater robots
  • UAVs

Published Papers (15 papers)

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Research

21 pages, 1280 KiB  
Article
Kinematic Tripod (K3P): A New Kinematic Algorithm for Gait Pattern Generation
by Daniel Soto-Guerrero, José Gabriel Ramírez-Torres and Eduardo Rodriguez-Tello
Appl. Sci. 2024, 14(6), 2564; https://doi.org/10.3390/app14062564 - 19 Mar 2024
Viewed by 396
Abstract
Insects are good examples of ground locomotion because they can adapt their gait pattern to propel them in any direction, over uneven terrain, in a stable manner. Nevertheless, replicating such locomotion skills to a legged robot is not a straightforward task. Different approaches [...] Read more.
Insects are good examples of ground locomotion because they can adapt their gait pattern to propel them in any direction, over uneven terrain, in a stable manner. Nevertheless, replicating such locomotion skills to a legged robot is not a straightforward task. Different approaches have been proposed to synthesize the gait patterns for these robots; each approach exhibits different restrictions, advantages, and priorities. For the purpose of this document, we have classified gait pattern generators for multi-legged robots into three categories: precomputed, heuristic, and bio-inspired approaches. Precomputed approaches rely on a set of precalculated motion patterns obtained from geometric and/or kinematic models that are performed repeatedly whenever necessary and that cannot be modified on-the-fly to adapt to the terrain changes. On the other hand, heuristic and bio-inspired approaches offer on-line adaptability, but parameter-tuning and heading control can be difficult. In this document, we present the K3P algorithm, a real-time kinematic gait pattern generator conceived to command a legged robot. In contrast to other approaches, K3P enables the robot to adapt its gait to follow an arbitrary trajectory, at an arbitrary speed, over uneven terrain. No precomputed motions for the legs are required; instead, K3P modifies the motion of all mechanical joints to propel the body of the robot in the desired direction, maintaining a tripod stability at all times. In this paper, all the specific details of the aforementioned algorithm are presented, as well as different simulation results that validate its characteristics. Full article
(This article belongs to the Special Issue Advances in Robot Path Planning, Volume II)
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24 pages, 12418 KiB  
Article
Efficient Path Planning Based on Dynamic Bridging Rapidly Exploring Random Tree
by Shulei Qiu, Baoquan Li, Ruiyang Tong, Xiaojing He and Chuanjing Tang
Appl. Sci. 2024, 14(5), 2032; https://doi.org/10.3390/app14052032 - 29 Feb 2024
Viewed by 496
Abstract
In the domain of mobile robotic navigation, the real-time generation of low-cost, executable reference trajectories is crucial. This paper propounds an innovative path planning strategy, termed Dynamic Bridging Rapidly Exploring Random Tree (DBR-RRT), which endeavors to enable safe and expedited path navigation. Initially, [...] Read more.
In the domain of mobile robotic navigation, the real-time generation of low-cost, executable reference trajectories is crucial. This paper propounds an innovative path planning strategy, termed Dynamic Bridging Rapidly Exploring Random Tree (DBR-RRT), which endeavors to enable safe and expedited path navigation. Initially, a heuristic discrimination method is engaged in the path search phase, whereby the issue of sluggish search velocity is tackled by evaluating whether sampled points reside at “bridging locations” within a free space, and by assessing the spatial–geometric relationships between proximate obstacles and auxiliary points. Subsequently, by leveraging extended speed, additional sampling points are generated in the vicinity of existing points to augment the search’s efficacy. Ultimately, the path is optimized and pruned by synthesizing the local curvature of the sampling points and the proximity to obstacles, assigning varied priorities to nodes, thus ensuring that the path’s quality and smoothness is upheld. Full article
(This article belongs to the Special Issue Advances in Robot Path Planning, Volume II)
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16 pages, 5078 KiB  
Article
Energy-Saving Breakthrough in the Point-to-Point Control of a Flexible Manipulator
by Akira Abe
Appl. Sci. 2024, 14(5), 1788; https://doi.org/10.3390/app14051788 - 22 Feb 2024
Viewed by 394
Abstract
This study aims to contribute academically valuable insights into energy-efficient drives for the positioning control of flexible structures. It focuses on the point-to-point (PTP) motion control of a flexible manipulator to suppress residual vibration and reduce driving energy simultaneously. The driving energy for [...] Read more.
This study aims to contribute academically valuable insights into energy-efficient drives for the positioning control of flexible structures. It focuses on the point-to-point (PTP) motion control of a flexible manipulator to suppress residual vibration and reduce driving energy simultaneously. The driving energy for PTP motion is influenced by the initial deflection of the flexible manipulator. Considering this phenomenon, the study proposes a trajectory planning method for the joint angle of a flexible manipulator. In this method, the evaluation function is defined as the sum of drive torques, and its minimization through particle swarm optimization generates an optimal trajectory that minimizes drive energy and suppresses residual vibration. Numerical simulations indicate that significant energy savings can be achieved by actively deforming the manipulator. These simulation results are corroborated by experimental data, which demonstrate the practical applicability and effectiveness of the proposed method. Full article
(This article belongs to the Special Issue Advances in Robot Path Planning, Volume II)
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15 pages, 9853 KiB  
Article
Trajectory Planning of Shape-Following Laser Cleaning Robot for the Aircraft Radar Radome Coating
by Zhen Zeng, Chengzhao Jiang, Shanting Ding, Qinyang Li, Zhongsheng Zhai and Daizhe Chen
Appl. Sci. 2024, 14(3), 1163; https://doi.org/10.3390/app14031163 - 30 Jan 2024
Viewed by 580
Abstract
At present, aircraft radome coating cleaning mainly relies on manual and chemical methods. In view of this situation, this study presents a trajectory planning method based on a three-dimensional (3D) surface point cloud for a laser-enabled coating cleaning robot. An automated trajectory planning [...] Read more.
At present, aircraft radome coating cleaning mainly relies on manual and chemical methods. In view of this situation, this study presents a trajectory planning method based on a three-dimensional (3D) surface point cloud for a laser-enabled coating cleaning robot. An automated trajectory planning scheme is proposed to utilize 3D laser scanning to acquire point cloud data and avoid the dependence on traditional teaching–playback paradigms. A principal component analysis (PCA) algorithm incorporating additional principal direction determination for point cloud alignment is introduced to facilitate subsequent point cloud segmentation. The algorithm can adjust the coordinate system and align with the desired point cloud segmentation direction efficiently and conveniently. After preprocessing and coordinate system adjustment of the point cloud, a projection-based point cloud segmentation technique is proposed, enabling the slicing division of the point cloud model and extraction of cleaning target positions from each slice. Subsequently, the normal vectors of the cleaning positions are estimated, and trajectory points are biased along these vectors to determine the end effector’s orientation. Finally, B-spline curve fitting and layered smooth connection methods are employed to generate the cleaning path. Experimental results demonstrate that the proposed method offers efficient and precise trajectory planning for the aircraft radar radome coating laser cleaning and avoids the need for a prior teaching process so it could enhance the automation level in coating cleaning tasks. Full article
(This article belongs to the Special Issue Advances in Robot Path Planning, Volume II)
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30 pages, 12209 KiB  
Article
Application and Research on Improved Adaptive Monte Carlo Localization Algorithm for Automatic Guided Vehicle Fusion with QR Code Navigation
by Bowen Zhang, Shiyun Li, Junting Qiu, Gang You and Lishuang Qu
Appl. Sci. 2023, 13(21), 11913; https://doi.org/10.3390/app132111913 - 31 Oct 2023
Cited by 2 | Viewed by 940
Abstract
SLAM (simultaneous localization and mapping) technology incorporating QR code navigation has been widely used in the mobile robotics industry. However, the particle kidnapping problem, positioning accuracy, and navigation time are still urgent issues to be solved. In this paper, a SLAM fused QR [...] Read more.
SLAM (simultaneous localization and mapping) technology incorporating QR code navigation has been widely used in the mobile robotics industry. However, the particle kidnapping problem, positioning accuracy, and navigation time are still urgent issues to be solved. In this paper, a SLAM fused QR code navigation method is proposed and an improved adaptive Monte Carlo positioning algorithm is used to fuse the QR code information. Firstly, the generation and resampling methods of initialized particle swarms are improved to improve the robustness and weights of the swarms and to avoid the kidnapping problem. Secondly, the Gmapping scan data and the data generated by the improved AMCL algorithm are fused using the extended Kalman filter to improve the accuracy and stability of the state estimation. Finally, in terms of the positioning system, Gmapping is used to obtain QR code data as marker positions on static maps, and the improved adaptive Monte Carlo localization particle positioning algorithm is matched with a library of QR code templates, which corrects for offset distances and achieves precise point-to-point positioning under grey-valued raster maps. The experimental results show that the particles encountered with kidnapping can be quickly adjusted in position, with a 68.73% improvement in adjustment time, 64.27% improvement in navigation and positioning accuracy, and 42.81% reduction in positioning time. Full article
(This article belongs to the Special Issue Advances in Robot Path Planning, Volume II)
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28 pages, 5221 KiB  
Article
Research on Multi-Sensor Data Fusion Positioning Method of Unmanned Ships Based on Threshold- and Hierarchical-Capacity Particle Filter
by Yi Shen, Zeyu Zhao, Mingxin Yuan and Sun Wang
Appl. Sci. 2023, 13(18), 10390; https://doi.org/10.3390/app131810390 - 17 Sep 2023
Viewed by 739
Abstract
To improve the positioning accuracy of unmanned ships, a multi-sensor system including ZigBee, a Global Positioning System (GPS), and BeiDou Navigation Satellite System (BDS) is constructed, and an adaptive multi-sensor data fusion positioning method based on the threshold and hierarchical capacity particle filter [...] Read more.
To improve the positioning accuracy of unmanned ships, a multi-sensor system including ZigBee, a Global Positioning System (GPS), and BeiDou Navigation Satellite System (BDS) is constructed, and an adaptive multi-sensor data fusion positioning method based on the threshold and hierarchical capacity particle filter (TCPF) is designed. First, the ZigBee-GPS/BDS multi-sensor measurement data is preprocessed to achieve a consistent space–time reference and transformed into the same coordinate system by projection. Then, the fault data is weighted and corrected through the consistency inspection of ZigBee-GPS/BDS multi-sensor positioning data, and the corresponding confidence factor is given according to the confidence distance of the positioning data; furthermore, the confidence factor is associated with stratified sampling. After that, the multi-sensor positioning data is filtered and denoised using a basic particle filter. Finally, a TCPF data fusion algorithm is designed, and the navigation positioning data of the unmanned ship is fused and filtered to obtain its positioning information. Numerical tests show that compared with other filtering algorithms, the mean square root error and standard deviation of the proposed TCPF algorithm decrease by an average of 25.0% and 28.0%, respectively, which verifies its high filtering accuracy and its advantages in suppressing particle degradation and avoiding sample scarcity. The experimental tests show that compared with other fusion algorithms, the proposed TCPF algorithm can not only realize the precise positioning during unmanned ship navigation, but also in the positioning and fault tolerance test, the average positioning error, root-mean-square error, and standard deviation of the former decrease by 36.0%, 38.0%, and 37.0%, respectively, and the corresponding performance indicators of the latter decrease by an average of 20.0%, 19.5%, and 17.5%, which verifies that it has the advantages of high data reliability and good filtering fault tolerance, and helps to improve the positioning accuracy of unmanned ships. Full article
(This article belongs to the Special Issue Advances in Robot Path Planning, Volume II)
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20 pages, 8855 KiB  
Article
CERRT: A Mobile Robot Path Planning Algorithm Based on RRT in Complex Environments
by Kun Hao, Yang Yang, Zhisheng Li, Yonglei Liu and Xiaofang Zhao
Appl. Sci. 2023, 13(17), 9666; https://doi.org/10.3390/app13179666 - 26 Aug 2023
Cited by 1 | Viewed by 963
Abstract
In complex environments, path planning for mobile robots faces challenges such as insensitivity to the environment, low efficiency, and poor path quality with the rapidly-exploring random tree (RRT) algorithm. We propose a novel algorithm, the complex environments rapidly-exploring random tree (CERRT), to address [...] Read more.
In complex environments, path planning for mobile robots faces challenges such as insensitivity to the environment, low efficiency, and poor path quality with the rapidly-exploring random tree (RRT) algorithm. We propose a novel algorithm, the complex environments rapidly-exploring random tree (CERRT), to address these issues. The CERRT algorithm builds upon the RRT approach and incorporates two key components: a pre-allocated extension node method and a vertex death mechanism. These enhancements aim to improve vertex utilization and overcome the problem of becoming trapped in concave regions, a limitation of traditional algorithms. Additionally, the CERRT algorithm integrates environment awareness at collision points, enabling rapid identification and navigation through narrow passages using local simple sampling techniques. We also introduce the bidirectional shrinking optimization strategy (BSOS) based on the pruning optimization strategy (POS) to further enhance the quality of path solutions. Extensive simulations demonstrate that the CERRT algorithm outperforms the RRT and RRV algorithms in various complex environments, such as mazes and narrow passages. It exhibits shorter running times and generates higher-quality paths, making it a promising approach for mobile robot path planning in challenging environments. Full article
(This article belongs to the Special Issue Advances in Robot Path Planning, Volume II)
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15 pages, 2682 KiB  
Article
Area Division Using Affinity Propagation for Multi-Robot Coverage Path Planning
by Nikolaos Baras and Minas Dasygenis
Appl. Sci. 2023, 13(14), 8207; https://doi.org/10.3390/app13148207 - 14 Jul 2023
Cited by 1 | Viewed by 689
Abstract
In the wake of advancing technology, autonomous vehicles and robotic systems have burgeoned in popularity across a spectrum of applications ranging from mapping and agriculture to reconnaissance missions. These practical implementations have brought to light an array of scientific challenges, a crucial one [...] Read more.
In the wake of advancing technology, autonomous vehicles and robotic systems have burgeoned in popularity across a spectrum of applications ranging from mapping and agriculture to reconnaissance missions. These practical implementations have brought to light an array of scientific challenges, a crucial one among them being Coverage Path Planning (CPP). CPP, the strategic planning of a path that ensures comprehensive coverage of a defined area, while being widely examined in the context of a single-robot system, has found its complexity magnified in the multi-robot scenario. A prime hurdle in multi-robot CPP is the division and allocation of the operation area among the robots. Traditional methods, largely reliant on the number of robots and their initial positions to segment the space, often culminate in suboptimal area division. This deficiency can occasionally render the problem unsolvable due to the sensitivity of most area division algorithms to the robots’ starting points. Addressing this predicament, our research introduced an innovative methodology that employs Affinity Propagation (AP) for area allocation in multi-robot CPP. In our approach, the area is partitioned into ‘n’ clusters through AP, with each cluster subsequently assigned to a robot. Although the model operates under the assumption of an unlimited robot count, it offers flexibility during execution, allowing the user to modify the AP algorithm’s similarity function factor to regulate the number of generated clusters. Serving as a significant progression in multi-robot CPP, the proposed model provides an innovative approach to area division and path optimization, thereby setting a strong foundation for future exploration and practical enhancements in this field. Full article
(This article belongs to the Special Issue Advances in Robot Path Planning, Volume II)
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26 pages, 12354 KiB  
Article
Machine Learning-Based Shoveling Trajectory Optimization of Wheel Loader for Fuel Consumption Reduction
by Yanhui Chen, Gang Shi, Cheng Tan and Zhiwen Wang
Appl. Sci. 2023, 13(13), 7659; https://doi.org/10.3390/app13137659 - 28 Jun 2023
Viewed by 836
Abstract
The difference in fuel consumption of wheel loaders can be more than 30% according to different shoveling trajectories for shoveling operations, and the optimization of shoveling trajectories is an important way to reduce the fuel consumption of shoveling operations. The existing shoveling trajectory [...] Read more.
The difference in fuel consumption of wheel loaders can be more than 30% according to different shoveling trajectories for shoveling operations, and the optimization of shoveling trajectories is an important way to reduce the fuel consumption of shoveling operations. The existing shoveling trajectory optimization method is mainly through theoretical calculation and simulation analysis, which cannot fully consider the high randomness and complexity of the shoveling process. It is difficult to achieve the desired optimization effect. Therefore, this paper takes the actual shoveling operation data as the basis. The factors that have a high impact on the fuel consumption of shoveling are screened out through Kernel Principal Component Analysis. Moreover, the mathematical model of fuel consumption of shoveling operation is established by Support Vector Machine and combined with the Improved Particle Swarm Optimization algorithm to optimize the shoveling trajectory. To demonstrate the generalization ability of the model, two materials, gravel, and sand, are selected. Meanwhile, the influence of different engine speeds on the shoveling operation is considered. We optimize the shoveling trajectories for three different engine speeds. The optimized trajectories are verified and compared with the sample data and manually controlled shoveling data. The results show that the optimized trajectory can reduce the fuel consumption of shoveling operation by 27.66% and 24.34% compared with the manually controlled shoveling of gravel and sand, respectively. This study provides guidance for the energy-efficient operation of wheel loaders. Full article
(This article belongs to the Special Issue Advances in Robot Path Planning, Volume II)
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18 pages, 7270 KiB  
Article
Research on Real-Time Obstacle Avoidance Motion Planning of Industrial Robotic Arm Based on Artificial Potential Field Method in Joint Space
by Yu Chen, Liping Chen, Jianwan Ding and Yanbing Liu
Appl. Sci. 2023, 13(12), 6973; https://doi.org/10.3390/app13126973 - 09 Jun 2023
Cited by 2 | Viewed by 2247
Abstract
The artificial potential field method is a highly popular obstacle avoidance algorithm which is widely used in the field of industrial robotics due to its high efficiency. However, the traditional artificial potential field method has poor real-time performance, making it less suitable for [...] Read more.
The artificial potential field method is a highly popular obstacle avoidance algorithm which is widely used in the field of industrial robotics due to its high efficiency. However, the traditional artificial potential field method has poor real-time performance, making it less suitable for modern factory work patterns, and it is difficult to handle situations when the robotic arm encounters singular configurations. In this paper, we propose an improved artificial potential field method in joint space, which effectively improves the real-time performance of the algorithm, and still performs well when the robotic arm falls into a singular configuration. This method solves the gradient of the repulsive potential field in advance by defining the shortest distance from each joint of the robotic arm to the obstacle, and only needs to calculate potential field function once per cycle, which significantly reduces the calculation time. In addition, when a robotic arm falls into a local minimum position in potential field, the algorithm adds a virtual obstacle to make it leave the position, while this virtual obstacle does not require additional input information. Experimental results show that the algorithm obtains short movement paths and requires very little computing time in the face of different obstacles. Full article
(This article belongs to the Special Issue Advances in Robot Path Planning, Volume II)
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17 pages, 17119 KiB  
Article
RRT*-Fuzzy Dynamic Window Approach (RRT*-FDWA) for Collision-Free Path Planning
by Lintao Zhou, Nanpeng Wu, Hu Chen, Qinge Wu and Yingbo Lu
Appl. Sci. 2023, 13(9), 5234; https://doi.org/10.3390/app13095234 - 22 Apr 2023
Cited by 6 | Viewed by 1439
Abstract
Path planning is an important aspect and component in the research of mobile-robot-related technologies. Many path planning algorithms are only applicable to static environments, while in practical tasks, the uncertainty in dynamic environments increases the difficulty of path planning and obstacle avoidance compared [...] Read more.
Path planning is an important aspect and component in the research of mobile-robot-related technologies. Many path planning algorithms are only applicable to static environments, while in practical tasks, the uncertainty in dynamic environments increases the difficulty of path planning and obstacle avoidance compared with static environments. To address this problem, this paper proposes an RRT*-FDWA algorithm. RRT* first generates a global optimal path, and then, when obstacles exist nearby, an FDWA algorithm fixes the local path in real time. Compared with other path planning algorithms, RRT*-FDWA can avoid local minima, rapidly perform path replanning, generate a smooth optimal route, and improve the robot’s maneuvering amplitude. In this paper, the effectiveness of the algorithm is verified through experiments in dynamic environments. Full article
(This article belongs to the Special Issue Advances in Robot Path Planning, Volume II)
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23 pages, 1006 KiB  
Article
Ship Defense Strategy Using a Planar Grid Formation of Multiple Drones
by Jonghoek Kim
Appl. Sci. 2023, 13(7), 4397; https://doi.org/10.3390/app13074397 - 30 Mar 2023
Viewed by 929
Abstract
This article introduces a ship defense strategy using a planar grid formation of multiple drones. We handle a scenario where a high-speed target with variable velocity heads towards the ship. The ship measures the position of the target in real time. Based on [...] Read more.
This article introduces a ship defense strategy using a planar grid formation of multiple drones. We handle a scenario where a high-speed target with variable velocity heads towards the ship. The ship measures the position of the target in real time. Based on the measured target, the drones guidance laws are calculated by the ships on-board computer and are sent to every drone in real time. The drones form a planar grid formation, whose center blocks the Line-Of-Sight (LOS) line connecting the target and the ship. Since the target is guided to hit its goal (ship), the drones can effectively block the target by blocking the LOS line. We enable slow drones to capture a fast target by making the drones stay close to the ship while blocking the LOS at all times. By using a grid formation of drones, we can increase the capture rate, even when there exists error in the prediction of the target’s position. To the best of our knowledge, this article is unique in using a formation of multiple drones to intercept a fast target with variable velocity. Through MATLAB simulations, the effectiveness of our multi-agent guidance law is verified by comparing it with other state-of-the-art guidance controls. Full article
(This article belongs to the Special Issue Advances in Robot Path Planning, Volume II)
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20 pages, 3605 KiB  
Article
Hierarchical Sliding Mode Control Combined with Nonlinear Disturbance Observer for Wheeled Inverted Pendulum Robot Trajectory Tracking
by Ming Hou, Xuedong Zhang, Du Chen and Zheng Xu
Appl. Sci. 2023, 13(7), 4350; https://doi.org/10.3390/app13074350 - 29 Mar 2023
Cited by 7 | Viewed by 1471
Abstract
A proposed optimized model for the trajectory tracking control of a wheeled inverted pendulum robot (WIPR) system is presented in this study, which addresses the problem of poor trajectory tracking performance in the presence of unknown disturbances due to the nonlinear and underactuated [...] Read more.
A proposed optimized model for the trajectory tracking control of a wheeled inverted pendulum robot (WIPR) system is presented in this study, which addresses the problem of poor trajectory tracking performance in the presence of unknown disturbances due to the nonlinear and underactuated characteristics of the system. First, a kinematic controller was used to track a reference trajectory and generate a control law that specifies the desired forward and rotation speeds of the system. Next, a nonlinear disturbance observer (NDO) was designed to enhance the system’s robustness to external disturbances and improve its tracking performance. Then, the coupled system state variables were decoupled into two subsystems: a forward rotation subsystem and a tilt angle velocity subsystem. An improved hierarchical sliding mode controller was designed to control these subsystems separately. Finally, simulation experiments were conducted to compare the proposed method with a common sliding mode control approach. The simulation results demonstrate that the proposed method achieves better tracking performance in the presence of unknown disturbances. Full article
(This article belongs to the Special Issue Advances in Robot Path Planning, Volume II)
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21 pages, 1431 KiB  
Article
Three-Dimensional Rendezvous Controls of Multiple Robots with Amplitude-Only Measurements in Cluttered Underwater Environments
by Jonghoek Kim
Appl. Sci. 2023, 13(7), 4130; https://doi.org/10.3390/app13074130 - 24 Mar 2023
Cited by 1 | Viewed by 992
Abstract
This study addresses multi-robot distributed rendezvous controls in cluttered underwater environments with many unknown obstacles. In underwater environments, a Unmanned Underwater Vehicle (UUV) cannot localize itself, since a Global Positioning System (GPS) is not available. Assume that each UUV has multiple signal intensity [...] Read more.
This study addresses multi-robot distributed rendezvous controls in cluttered underwater environments with many unknown obstacles. In underwater environments, a Unmanned Underwater Vehicle (UUV) cannot localize itself, since a Global Positioning System (GPS) is not available. Assume that each UUV has multiple signal intensity sensors surrounding it. Multiple intensity sensors on a UUV can only measure the amplitude of signals generated from its neighbor UUVs. We prove that multiple UUVs with bounded speed converge to a designated rendezvous point, while maintaining the connectivity of the communication network. This study further discusses a fault detection method, which detects faulty UUVs based on local sensing measurements. In addition, the proposed rendezvous control is adaptive to communication link failure or invisible UUVs. Note that communication link failure or invisible UUVs can happen due to unknown obstacles in the workspace. As far as we know, our study is novel in developing 3D coordinate-free distributed rendezvous control, considering underwater robots that can only measure the amplitude of signals emitted from neighboring robots. The proposed rendezvous algorithms are provably complete, and the effectiveness of the proposed rendezvous algorithms is demonstrated under MATLAB simulations. Full article
(This article belongs to the Special Issue Advances in Robot Path Planning, Volume II)
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16 pages, 2351 KiB  
Article
Non-Parametric Calibration of the Inverse Kinematic Matrix of a Three-Wheeled Omnidirectional Mobile Robot Based on Genetic Algorithms
by Jordi Palacín, Elena Rubies, Ricard Bitrià and Eduard Clotet
Appl. Sci. 2023, 13(2), 1053; https://doi.org/10.3390/app13021053 - 12 Jan 2023
Cited by 10 | Viewed by 1367
Abstract
Odometry is a computation method that provides a periodic estimation of the relative displacements performed by a mobile robot based on its inverse kinematic matrix, its previous orientation and position, and the estimation of the angular rotational velocity of its driving wheels. Odometry [...] Read more.
Odometry is a computation method that provides a periodic estimation of the relative displacements performed by a mobile robot based on its inverse kinematic matrix, its previous orientation and position, and the estimation of the angular rotational velocity of its driving wheels. Odometry is cumulatively updated from tens to hundreds of times per second, so any inaccuracy in the definition of the inverse kinematic matrix of a robot leads to systematic trajectory errors. This paper proposes a non-parametric calibration of the inverse kinematic (IK) matrix of a three-wheeled omnidirectional mobile robot based on the use of genetic algorithms (GA) to minimize the positioning error registered in a set of calibration trajectories. The application of this non-parametric procedure has provided an average improvement of 82% in the estimation of the final position and orientation of the mobile robot. This is similar to the improvement achieved with analogous parametric methods. The advantage of this non-parametric approach is that it covers a larger search space because it eliminates the need to define feasible physical limits to the search performed to calibrate the inverse kinematic matrix of the mobile robot. Full article
(This article belongs to the Special Issue Advances in Robot Path Planning, Volume II)
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