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Motion Planning and Control for Automatic Machines, Robots and Multibody Systems

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

Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 9681

Special Issue Editor

Dr. Shuang Song

E-Mail Website
Guest Editor
School of Mechanical Engineering and Automation, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China
Interests: novel design and optimization; control and sensing; plan and navigation of minimally invasive surgery robots, including the wireless capsule robot, the wire-driven flexible robot, the concentric tube robot, the magnetic driven microrobot, etc.

Special Issue Information

Dear Colleagues,

Intelligent control is considered one of the most fundamental problems for robotic research. Over the last few decades, robotic motion control has achieved great success in static, structured environments with low degree of freedom robots, mainly robot arms. However, the thriving of automatic machine and robot research leads to new application scenarios, e.g., unstructured, dynamic, or narrow environments, and complicated robot systems, e.g., humanoid, quadruped robots, swarm robots. For these scenarios and robotics systems, the motion control process can be very long, and the generated motion may be insecure or collide with or violate the requirements. In this Special Issue, we invite experimental and theoretical contributions on motion planning and control research for automatic machines, robots, and multibody systems in the robotics area, such as medical robots, industrial robots, service robots, mobile robots, micro/nanorobots, automatic machines, visual servoing, multirobot cooperation, and so on.

Dr. Shuang Song
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

  • motion planning
  • intelligent control
  • multibody system
  • medical robots
  • industrial robots
  • service robots
  • mobile robots
  • automatic machines
  • visual servoing
  • multi-robot cooperation

Published Papers (5 papers)

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Research

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13 pages, 1875 KiB  
Article
Comparison of Various Reinforcement Learning Environments in the Context of Continuum Robot Control
by Jakub Kołota and Turhan Can Kargin
Appl. Sci. 2023, 13(16), 9153; https://doi.org/10.3390/app13169153 - 11 Aug 2023
Cited by 3 | Viewed by 1338
Abstract
Controlling flexible and continuously structured continuum robots is a challenging task in the field of robotics and control systems. This study explores the use of reinforcement learning (RL) algorithms in controlling a three-section planar continuum robot. The study aims to investigate the impact [...] Read more.
Controlling flexible and continuously structured continuum robots is a challenging task in the field of robotics and control systems. This study explores the use of reinforcement learning (RL) algorithms in controlling a three-section planar continuum robot. The study aims to investigate the impact of various reward functions on the performance of the RL algorithm. The RL algorithm utilized in this study is the Deep Deterministic Policy Gradient (DDPG), which can be applied to both continuous-state and continuous-action problems. The study’s findings reveal that the design of the RL environment, including the selection of reward functions, significantly influences the performance of the RL algorithm. The study provides significant information on the design of RL environments for the control of continuum robots, which may be valuable to researchers and practitioners in the field of robotics and control systems. Full article
(This article belongs to the Special Issue Motion Planning and Control for Automatic Machines, Robots and Multibody Systems)
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16 pages, 3663 KiB  
Article
Task-Based Motion Planning Using Optimal Redundancy for a Minimally Actuated Robotic Arm
by Yanai Gal and David Zarrouk
Appl. Sci. 2022, 12(19), 9526; https://doi.org/10.3390/app12199526 - 22 Sep 2022
Viewed by 1423
Abstract
In planning robotic manipulations, heuristic searches are commonly considered impractical due to the high dimensionality of the problem caused by redundancy in the kinematic chain. In this paper, we present an optimal motion planning algorithm for an overly redundant minimally actuated serial robot [...] Read more.
In planning robotic manipulations, heuristic searches are commonly considered impractical due to the high dimensionality of the problem caused by redundancy in the kinematic chain. In this paper, we present an optimal motion planning algorithm for an overly redundant minimally actuated serial robot (MASR) using the manipulator workspace as a foundation for the heuristic search. By utilizing optimized numerical probability methods, a novel sub-workspace search was developed. The sub-workspace allows the search to quickly and accurately find the minimal sub-set of joints to be actuated and ensures the existence of a path to a given target. Further on, the search result is used as a search graph for the heuristic planning problem which guarantees an optimal solution within the problem boundaries. Using this approach, optimal heuristic search can become practical for various types of manipulators, tasks, and environments. We describe our workspace minimization and heuristic search using the example of a general robotic arm and then implement the approach on a MASR model, a robotic arm with five passive joints and a single mobile actuator that is free to travel along the arm and rotate each joint separately. A series of simulations show how our minimal redundancy approach can benefit from path planning in the case of traditional hyper-redundant manipulators, and its greater effectiveness when addressing the specific design of the MASR. Full article
(This article belongs to the Special Issue Motion Planning and Control for Automatic Machines, Robots and Multibody Systems)
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12 pages, 3025 KiB  
Article
Cartesian Constrained Stochastic Trajectory Optimization for Motion Planning
by Michal Dobiš, Martin Dekan, Adam Sojka, Peter Beňo and František Duchoň
Appl. Sci. 2021, 11(24), 11712; https://doi.org/10.3390/app112411712 - 9 Dec 2021
Cited by 3 | Viewed by 2890
Abstract
This paper presents novel extensions of the Stochastic Optimization Motion Planning (STOMP), which considers cartesian path constraints. It potentially has high usage in many autonomous applications with robotic arms, where preservation or minimization of tool-point rotation is required. The original STOMP algorithm is [...] Read more.
This paper presents novel extensions of the Stochastic Optimization Motion Planning (STOMP), which considers cartesian path constraints. It potentially has high usage in many autonomous applications with robotic arms, where preservation or minimization of tool-point rotation is required. The original STOMP algorithm is unable to use the cartesian path constraints in a trajectory generation because it works only in robot joint space. Therefore, the designed solution, described in this paper, extends the most important parts of the algorithm to take into account cartesian constraints. The new sampling noise generator generates trajectory samples in cartesian space, while the new cost function evaluates them and minimizes traversed distance and rotation change of the tool-point in the resulting trajectory. These improvements are verified with simple experiments and the solution is compared with the original STOMP. Results of the experiments show that the implementation satisfies the cartesian constraints requirements. Full article
(This article belongs to the Special Issue Motion Planning and Control for Automatic Machines, Robots and Multibody Systems)
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17 pages, 2569 KiB  
Article
Quadrotor Attitude Control by Fractional-Order Fuzzy Particle Swarm Optimization-Based Active Disturbance Rejection Control
by Qi Zhang, Yaoxing Wei and Xiao Li
Appl. Sci. 2021, 11(24), 11583; https://doi.org/10.3390/app112411583 - 7 Dec 2021
Cited by 8 | Viewed by 1735
Abstract
In this paper, Active Disturbance Rejection Control (ADRC) is utilized in the attitude control of a quadrotor aircraft to address the problem of attitude destabilization in flight control caused by parameter uncertainties and external disturbances. Considering the difficulty of optimizing the parameter of [...] Read more.
In this paper, Active Disturbance Rejection Control (ADRC) is utilized in the attitude control of a quadrotor aircraft to address the problem of attitude destabilization in flight control caused by parameter uncertainties and external disturbances. Considering the difficulty of optimizing the parameter of ADRC, a fractional-order fuzzy particle swarm optimization (FOFPSO) algorithm is proposed to optimize the parameters of ADRC for quadrotor aircraft. Simultaneously, the simulation experiment is designed, which compares with the optimized performance of traditional particle swarm optimization (PSO), fuzzy article swarm optimization (FPSO) and adaptive genetic algorithm-particle swarm optimization (AGA-PSO). In addition, the turbulent wind field model is established to verify the disturbance rejection performance of the controller. Finally, the designed controller is deployed to the actual hardware platform by using the model-based design method. The results show that the controller has a small overshoot and stronger disturbance rejection ability after the parameters are optimized by the proposed algorithm. Full article
(This article belongs to the Special Issue Motion Planning and Control for Automatic Machines, Robots and Multibody Systems)
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Review

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16 pages, 2480 KiB  
Review
Joint Communication–Motion Planning in Networked Robotic Systems
by Zixuan Zhang, Bo Zhang and Yunlong Wu
Appl. Sci. 2022, 12(12), 6261; https://doi.org/10.3390/app12126261 - 20 Jun 2022
Cited by 2 | Viewed by 1356
Abstract
In the recent decade, many research efforts in robotic society have considered motion planning for maintaining connectivity in networked robotic system (NRS) by exploiting robotic autonomous mobility. On the other hand, cognitive radio (CR) in the communication society aims at fully exploiting the [...] Read more.
In the recent decade, many research efforts in robotic society have considered motion planning for maintaining connectivity in networked robotic system (NRS) by exploiting robotic autonomous mobility. On the other hand, cognitive radio (CR) in the communication society aims at fully exploiting the spectrum in a wireless network, while the motion planning is seldom considered, as a wireless device itself may not decide where to go. In this article, joint communication–motion planning (JCMP) is proposed to boost the capability of NRS by exploiting both the adaptive communications and mobility control of autonomous robots. Specifically, we propose a JCMP framework for NRS, which aims at jointly exploiting the degree-of-freedom in mobility, space, time, frequency and power dimensions from both the motion and communication components. Afterward, we design and evaluate JCMP in a conventional and a CR-relay-assisted robot system, which shows the capability of JCMP in improving the performance of NRS. Finally, we summarize the proposed JCMP-enabled NRS framework and provide a series of future research directions. Full article
(This article belongs to the Special Issue Motion Planning and Control for Automatic Machines, Robots and Multibody Systems)
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