Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Advertisement

Springer Nature Link
Log in

Planning and control of mobile manipulator’s operation on objects with restricted motion in intelligent environment

  • Original Paper
  • Published:
Personal and Ubiquitous Computing Aims and scope Submit manuscript

Abstract

With the rapid development of science and technology, the current ordinary industrial manipulator cannot complete the task well, because it is difficult to control. This research mainly discusses the planning and control of the mobile manipulator for the operation of restricted motion objects in an intelligent environment. In this study, the rapid search random tree (RRT) method is used to study the obstacle avoidance motion planning of the manipulator. The 6-axis robotic arm system uses CAN (controller area network) bus to connect each joint in series and then connect to the multi-axis controller Maestro, and the host computer and Maestro send and receive control commands based on the TCP/IP protocol. A 1-DOF grip is installed at the end of the robotic arm, and the grip is connected to the end of the robotic arm (i.e., at the wrist joint of the robotic arm) through a 6-dimensional force 1 torque sensor. The operating system of the host computer is Ubuntu 14.04. The position of the object is recognized through Kinect, and the position of the object is sent to the robotic arm control software. The PTP (precision time protocol) of the robotic arm moves to a certain distance in front of the object, and then the movement of the robotic arm is controlled by visual servoing to complete the object capture. Each joint of the manipulator uses a speed control mode. In a single control cycle, the joint angular velocity of the manipulator remains unchanged. The controller used by the robotic arm is a 51-core STC12C5410AD single-chip microcomputer, which ensures the response speed of the joint motor linkage. In the environment of multiple obstacles, the RRT algorithm has 10 simulation experiments, the average sampling time is 95.651 s, and the average sampling point is 1308. The results show that the RRT algorithm proposed in this study enables the manipulator to move from the starting point to the endpoint in the environment of multiple obstacles, while avoiding the obstacles, and the mobile manipulator can better operate the motion-limited objects.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7

Similar content being viewed by others

Abbreviations

RRT:

rapidly random tree

CAN:

controller area network

PTP:

precision time protocol

CDIG:

color interval diagram

ROS:

robot operating system

DH:

Denavit Hartenberg

Equivalent connecting rod length:

L3 joint axis to the end of the manipulator

Drive wheels:

wheelchair width

Robotic arm:

that is, at the wrist joint of the robotic arm

References

  1. Schroeder JS, Perry JC (2018) Development of a series wrapping cam and energy-storing spring system for application in wearable robotic arm supports. Technol Innov 20(1-2):21–36

    Article  Google Scholar 

  2. Mann MP, Zion B, Shmulevich I, Rubinstein D, Linker R (2016) Combinatorial optimization and performance analysis of a multi-arm Cartesian robotic fruit harvester—extensions of graph coloring. J Intell Robot Syst 82(3-4):399–411

    Article  Google Scholar 

  3. Banerjee D, Yu K, Aggarwal G (2018) Robotic arm based 3D reconstruction test automation. IEEE Access 6(1):7206–7213

    Article  Google Scholar 

  4. Li S, Rameshwar R, Votta AM, Onal CD (2019) Intuitive control of a robotic arm and hand system with pneumatic haptic feedback. IEEE Robot Autom Lett 4(4):4424–4430

    Article  Google Scholar 

  5. Wang W, Gao W, DongMei W (2017) Research on a mobile manipulator for biochemical sampling tasks. Ind Robot 44(4):467–478

    Article  Google Scholar 

  6. Vestermark GL, Bhowmik-Stoker M, Springer BD (2018) Cognitive training for robotic arm-assisted unicompartmental knee arthroplasty through a surgical simulation mobile application. J Knee Surg 32(10):984–988

    Google Scholar 

  7. Sikorski J, Heunis CM, Franco F, Misra S (2019) The ARMM system: an optimized mobile electromagnetic coil for non-linear actuation of flexible surgical instruments. IEEE Trans Magn 55(9):1–9

    Article  Google Scholar 

  8. Jiang H, Duerstock BS, Wachs JP (2017) A machine vision-based gestural interface for people with upper extremity physical impairments. IEEE Trans Syst Man Cybern: Systems 44(5):630–641

    Article  Google Scholar 

  9. Kaczmarek M, Domski W, Mazur A (2017) Position-force control of mobile manipulator – nonadaptive and adaptive case. Nephron Clin Pract 27(4):487–503

    MathSciNet  MATH  Google Scholar 

  10. Bayraktar E, Yigit CB, Boyraz P (2020) Object manipulation with a variable-stiffness robotic mechanism using deep neural networks for visual semantics and load estimation. Neural Comput & Applic 32:9029–9045

    Article  Google Scholar 

  11. Liu M, Chowdhary G, Bruno CDS et al (2018) Gaussian processes for learning and control: a tutorial with examples. IEEE Control Syst Mag 38(5):53–86

    Article  MathSciNet  MATH  Google Scholar 

  12. Horng GJ, Liu MX, Chen CC (2020) The smart image recognition mechanism for crop harvesting system in intelligent agriculture. IEEE Sensors J 20(5):2766–2781

    Article  Google Scholar 

  13. Li Z, Janardhanan MN, Ashour AS, Dey N (2019) Mathematical models and migrating birds optimization for robotic U-shaped assembly line balancing problem. Neural Comput & Applic 31:9095–9111

    Article  Google Scholar 

  14. Bilberg CP, Witting CA, Andersen NA et al (2019) Force-based perception and manipulation, the DTU team competing in MBZIRC 2017. J Robot Syst 36(3):517–530

    Google Scholar 

  15. Yoshida S, Shirokura T, Sugiura Y, Sakamoto D, Ono T, Inami M, Igarashi T (2016) RoboJockey: Designing an entertainment experience with robots. IEEE Comp Graph Appl 36(1):62–69

    Article  Google Scholar 

  16. Wall DG, Economou J, Knowles K (2018) Quasi-real-time confined environment path generation for mobile robotic manipulator arms. Proc Institut Mech Eng 232(3):270–284

    Google Scholar 

  17. Yildiz I (2018) A low-cost and lightweight alternative to rehabilitation robots: omnidirectional interactive mobile robot for arm rehabilitation. Arab J Sci Eng 43(3):1053–1059

    Article  Google Scholar 

  18. Shah D, Tang L, Gai J, Putta-Venkata R (2016) Development of a mobile robotic phenotyping system for growth chamber-based studies of genotype x environment interactions. IFAC Pap OnLine 49(16):248–253

    Article  Google Scholar 

  19. Armando N, Almeida R, Fernandes JM, Silva JS, Boavida F (2021) End-to-end experimentation of a 5G vertical within the scope of blended learning. Discov Intern Things 1:9

    Article  Google Scholar 

  20. Silva FFA, Adorno BV (2016) Whole-body control of a mobile manipulator using feedback linearization based on dual quaternions. J Intell Robot Syst 1:1–14

    Google Scholar 

  21. Brown WR, Ulsoy AG (2017) Robust design of passive assist devices for multi-DOF robotic manipulator arms. Robotica 35(11):1–18

    Article  Google Scholar 

  22. Xu Z, Cheng C, Sugumaran V (2020) Big data analytics of crime prevention and control based on image processing upon cloud computing. J Surveill Secur Saf 1:16–33

    Google Scholar 

  23. Boral S, Chaturvedi SK, Naikan VNA (2019) A case-based reasoning system for fault detection and isolation: a case study on complex gearboxes. J Qual Maint Eng 25(2):213–235

    Article  Google Scholar 

  24. Jin X, Zhao K, Ji J, Qiu Z, He Z, Ma H (2020) Design and experiment of intelligent monitoring system for vegetable fertilizing and sowing. J Supercomput 76(5):3338–3354

    Article  Google Scholar 

  25. Andre Chase O, Andre DNC, Edson DSSA et al (2018) Environmental measurement technology: an approach to the Amazonian environment. IEEE Lat Am Trans 16(4):1036–1041

    Article  Google Scholar 

  26. Howard C, Jordan P, Di Eugenio B et al (2017) Shifting the load: a peer dialogue agent that encourages its human collaborator to contribute more to problem solving. Int J Artif Intell Educ 27(1):1–29

    Article  Google Scholar 

  27. Ferdaus MM, Pratama M, Anavatti SG, Garratt MA, Pan Y (2020) Generic evolving self-organizing neuro-fuzzy control of bio-inspired unmanned aerial vehicles. IEEE Trans Fuzzy Syst 28(8):1542–1556

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Network Engineering, Zhoukou Normal University, Zhoukou, 466001, Henan, China

    Kun Xu & Zhiliang Wang

Authors
  1. Kun Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhiliang Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kun Xu.

Ethics declarations

Consent for publication

All authors have seen the manuscript and approved to submit it to your journal.

Competing interests

The authors declare no competing interests.

Disclosure

We confirm that the content of the manuscript has not been published or submitted for publication elsewhere.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xu, K., Wang, Z. Planning and control of mobile manipulator’s operation on objects with restricted motion in intelligent environment. Pers Ubiquit Comput 27, 1369–1379 (2023). https://doi.org/10.1007/s00779-021-01594-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00779-021-01594-5

Keywords

Search

Navigation