A Sliding-Mode Control Algorithm of Self-Learning and Obstacle Avoidance for Mobile Robots

Tian Tian; Qiuyue Jiang; Zhengying Cai

doi:doi:10.11648/j.iotcc.20160405.11

| Peer-Reviewed

A Sliding-Mode Control Algorithm of Self-Learning and Obstacle Avoidance for Mobile Robots

Tian Tian, Qiuyue Jiang, Zhengying Cai

Published in Internet of Things and Cloud Computing (Volume 4, Issue 5)

Received: 29 November 2016 Accepted: 13 December 2016 Published: 14 January 2017

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

Abstract

It is generally very difficult to make effective obstacle avoidance for mobile robots, especially in uncertain environments. This article models the obstacle avoidance problem as a nonlinear sliding mode, using the self-learning method of non-linear system. First, a sliding mode algorithm is proposed for state dependent layers of the mobile robots, in which two kinds of boundary layers are included in, namely a sector-shaped layer and a constant layer. Second, a multi-input algorithm based on sliding-mode for self-learning of mobile robots is discussed. Some control rules are built for the self-learning and obstacle avoidance of mobile robots, and the solving steps are also presented. Last, an experiment is designed to verify the proposed model and calculate the sliding- mode control for mobile robots. Some interesting conclusions and future work are indicated at the end of the paper.

Published in	Internet of Things and Cloud Computing (Volume 4, Issue 5)
DOI	10.11648/j.iotcc.20160405.11
Page(s)	45-54
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2017. Published by Science Publishing Group

Keywords

Self-Learning, Sliding-Mode Control, Obstacle Avoidance, Mobile Robots

References

[1]	Cammarata, Alessandro; Calio, Ivo; D'Urso, Domenico; Dynamic stiffness model of spherical parallel robots. Journal of Sound and Vibrtion, Vol. 384, 2016, pp. 312-324.
[2]	Brandl, Christopher; Mertens, Alexander; Schlick, Christopher M. Human-robot interaction in assisted personal services: factors influencing distances that humans will accept between themselves and an approaching service robot. Human Factors and Ergonomics in Manufacturing & Service Industries, Vol.26, No. 6, 2016, pp. 713-727.
[3]	Dupre, Rob; Argyriou, Vasileios; Tzimiropoulos, Georgios; Risk analysis for smart homes and domestic robots using robust shape and physics descriptors, and complex boosting techniques, Information Sciences, Vol.372, 2016, pp. 359-379.
[4]	Villarreal-Cervantes, Miguel G.; Alvarez-Gallegos, Jaime, Off-line PID control tuning for a planar parallel robot using,DE variants, Expert Systems with Applications, Vol. 64, 2016, pp. 444-454.
[5]	Rasheed, Nadia; Amin, Shamsudin H. M.; Sultana, U.; Theoretical accounts to practical models: grounding phenomenon for abstract words in cognitive robots. Cognitive Systems Research, Vol. 40, 2016, pp. 86-98.
[6]	Du, Guanglong; Shao, Hengkang; Chen, Yanjiao. An online method for serial robot self-calibration with CMAC and UKF. Robotics and Computer-integrated Manufacturing, Vol. 42, pp. 39-48, DEC 2016.
[7]	Montano, Andres; Suarez, Raul. Coordination of several robots based on temporal synchronization. Robotics and Computer-integrated Manufacturing, Vol. 42, 2016, pp. 73-85.
[8]	Zeng, Yuanfan; Tian, Wei; Liao, Wenhe. Positional error similarity analysis for error compensation of industrial robots, Robotics and Computer-Integrated Manufacturing, Vol. 42, 2016, pp. 113-120.
[9]	Rahmani, Mehran; Ghanbari, Ahmad; Ettefagh, Mir Mohammad. Hybrid neural network fraction integral terminal sliding mode control of an inchworm robot manipulator. Mechanical Systems and Signal Processing, Vol. 80, 2016, pp. 117-136.
[10]	Tjahjowidodo, Tegoeh; Zhu, Ke; Dailey, Wayne; Multi-source micro-friction identification for a class of cable-driven robots with passive backbone. Mechanical Systems and Signal Processing, Vol. 80, 2016, pp. 152-165.
[11]	Hachmon, Guy; Mamet, Noam; Sasson, Sapir. A non-newtonian fluid robot. Artificial Life, Vol. 22, No. 1, 2016, pp. 1-22.
[12]	Grubman, Tony; Sekercioglu, Y. Ahmet; Wood, David R. Partitioning de Bruijn graphs into fixed-length cycles for robot identification and tracking. Discrete Applied Mathematics, Vol. 213, 2016, pp. 101-113.
[13]	Duguleana, Mihai; Mogan, Gheorghe. Neural networks based reinforcement learning for mobile robots obstacle avoidance. Expert Systems with Applications, Vol. 62, 2016, pp. 104-115.
[14]	Gundeti, Mohan S.; Boysen, William R.;Shah, Anup. Robot-assisted laparoscopic extravesical ureteral reimplantation: technique modifications contribute to optimized outcomes. European Urology Vol. 70, No. 5, 2016, pp. 818-823.
[15]	Leow, Jeffrey J.; Chang, Steven L.;Meyer, Christian P. Robot-assisted versus open radical prostatectomy: a contemporary analysis of an all-payer discharge database. European Urology, Vol. 70, No. 5, 2016, pp. 837-845.
[16]	Sun, Weichao; Tang, Songyuan; Gao, Huijun. Two time-scale tracking control of nonholonomic wheeled mobile robots. IEEE Transactions on Control Systems Technology, Vol. 24, No. 6, 2016, pp. 2059-2069.
[17]	Tanaka, Motoyasu; Tanaka, Kazuo. Singularity analysis of a snake robot and an articulated mobile robot with unconstrained links. IEEE Transactions on Control Systems Technology, Vol. 24, No. 6, 2016, pp. 2070-2081.
[18]	Jiang, Jingjing; Di Franco, Pierluigi; Astolfi Alessandro. Shared control for the kinematic and dynamic models of a mobile robot. IEEE Transactions on Control Systems Technology, Vol. 24, No. 6, 2016, pp. 2112-2124.
[19]	Liu, Zhe; Chen, Weidong; Lu, Junguo. Formation control of mobile robots using distributed controller with sampled-data and communication delays. IEEE Transactions on Control Systems Technology, Vol. 24, No. 6, 2016, pp. 2125-2132.
[20]	Ostafew, Chris J.; Schoellig, Angela P.; Barfoot, Timothy D. Robust constrained learning-based NMPC enabling reliable mobile robot path tracking. International JournalL of Robotics Research, Vol. 35, No. 13, 2016, pp. 1547-1563.
[21]	Wang, Zijian; Schwager, Mac. Force-amplifying n-robot transport system (force-ants) for cooperative planar manipulation without communication. International Journal of Robotics Research, Vol. 35, No. 13, 2016, pp. 1564-1586.
[22]	Zhu, Jian-Hua; Deng, Jiang; Liu, Xiao-Jing. Prospects of robot-assisted mandibular reconstruction with fibula flap: comparison with a computer-assisted navigation system and freehand technique. Journal of Reconstructive Microsurgery, Vol. 32, No. 9, 2016, pp. 661-669.
[23]	Gao, Haibo; Jin, Ma; Ding, Liang; A real-time, high fidelity dynamic simulation platform for hexapod robots on soft terrain. Simulation Modelling Practice and Theory, Vol. 68, pp. 125-145, NOV 2016.
[24]	Hsu, Cheng-Chung; Yeh, Syh-Shiuh; Hsu, Pau-Lo. Particle filter design for mobile robot localization based on received signal strength indicator. Transactions of The Institute of Measurement AND Control, Vol. 38, No. 11, 2016, pp. 1311-1319.
[25]	Mathis, Frank B.; Mukherjee, Ranjan. Apex height control of a two-mass robot hopping on a rigid foundation. Mechanism and Machine Theory, Vol. 105, 2016, pp. 44-57.

Cite This Article

Plain Text BibTeX RIS

APA Style

Tian Tian, Qiuyue Jiang, Zhengying Cai. (2017). A Sliding-Mode Control Algorithm of Self-Learning and Obstacle Avoidance for Mobile Robots. Internet of Things and Cloud Computing, 4(5), 45-54. https://doi.org/10.11648/j.iotcc.20160405.11

Copy | Download

ACS Style

Tian Tian; Qiuyue Jiang; Zhengying Cai. A Sliding-Mode Control Algorithm of Self-Learning and Obstacle Avoidance for Mobile Robots. Internet Things Cloud Comput. 2017, 4(5), 45-54. doi: 10.11648/j.iotcc.20160405.11

Copy | Download

AMA Style

Tian Tian, Qiuyue Jiang, Zhengying Cai. A Sliding-Mode Control Algorithm of Self-Learning and Obstacle Avoidance for Mobile Robots. Internet Things Cloud Comput. 2017;4(5):45-54. doi: 10.11648/j.iotcc.20160405.11

Copy | Download

@article{10.11648/j.iotcc.20160405.11,
  author = {Tian Tian and Qiuyue Jiang and Zhengying Cai},
  title = {A Sliding-Mode Control Algorithm of Self-Learning and Obstacle Avoidance for Mobile Robots},
  journal = {Internet of Things and Cloud Computing},
  volume = {4},
  number = {5},
  pages = {45-54},
  doi = {10.11648/j.iotcc.20160405.11},
  url = {https://doi.org/10.11648/j.iotcc.20160405.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.iotcc.20160405.11},
  abstract = {It is generally very difficult to make effective obstacle avoidance for mobile robots, especially in uncertain environments. This article models the obstacle avoidance problem as a nonlinear sliding mode, using the self-learning method of non-linear system. First, a sliding mode algorithm is proposed for state dependent layers of the mobile robots, in which two kinds of boundary layers are included in, namely a sector-shaped layer and a constant layer. Second, a multi-input algorithm based on sliding-mode for self-learning of mobile robots is discussed. Some control rules are built for the self-learning and obstacle avoidance of mobile robots, and the solving steps are also presented. Last, an experiment is designed to verify the proposed model and calculate the sliding- mode control for mobile robots. Some interesting conclusions and future work are indicated at the end of the paper.},
 year = {2017}
}

Copy | Download

TY  - JOUR
T1  - A Sliding-Mode Control Algorithm of Self-Learning and Obstacle Avoidance for Mobile Robots
AU  - Tian Tian
AU  - Qiuyue Jiang
AU  - Zhengying Cai
Y1  - 2017/01/14
PY  - 2017
N1  - https://doi.org/10.11648/j.iotcc.20160405.11
DO  - 10.11648/j.iotcc.20160405.11
T2  - Internet of Things and Cloud Computing
JF  - Internet of Things and Cloud Computing
JO  - Internet of Things and Cloud Computing
SP  - 45
EP  - 54
PB  - Science Publishing Group
SN  - 2376-7731
UR  - https://doi.org/10.11648/j.iotcc.20160405.11
AB  - It is generally very difficult to make effective obstacle avoidance for mobile robots, especially in uncertain environments. This article models the obstacle avoidance problem as a nonlinear sliding mode, using the self-learning method of non-linear system. First, a sliding mode algorithm is proposed for state dependent layers of the mobile robots, in which two kinds of boundary layers are included in, namely a sector-shaped layer and a constant layer. Second, a multi-input algorithm based on sliding-mode for self-learning of mobile robots is discussed. Some control rules are built for the self-learning and obstacle avoidance of mobile robots, and the solving steps are also presented. Last, an experiment is designed to verify the proposed model and calculate the sliding- mode control for mobile robots. Some interesting conclusions and future work are indicated at the end of the paper.
VL  - 4
IS  - 5
ER  -

Copy | Download

Author Information

Tian Tian

College of Internet of Things Engineering, China Three Gorges University, Yichang, China
Qiuyue Jiang

College of Mechanical-Electronic Engineering, China Three Gorges University, Yichang, China
Zhengying Cai

College of Internet of Things Engineering, China Three Gorges University, Yichang, China

Download PDF

Sections

Plain Text BibTeX RIS

APA Style

Tian Tian, Qiuyue Jiang, Zhengying Cai. (2017). A Sliding-Mode Control Algorithm of Self-Learning and Obstacle Avoidance for Mobile Robots. Internet of Things and Cloud Computing, 4(5), 45-54. https://doi.org/10.11648/j.iotcc.20160405.11

Copy | Download

ACS Style

Tian Tian; Qiuyue Jiang; Zhengying Cai. A Sliding-Mode Control Algorithm of Self-Learning and Obstacle Avoidance for Mobile Robots. Internet Things Cloud Comput. 2017, 4(5), 45-54. doi: 10.11648/j.iotcc.20160405.11

Copy | Download

AMA Style

Tian Tian, Qiuyue Jiang, Zhengying Cai. A Sliding-Mode Control Algorithm of Self-Learning and Obstacle Avoidance for Mobile Robots. Internet Things Cloud Comput. 2017;4(5):45-54. doi: 10.11648/j.iotcc.20160405.11

Copy | Download

@article{10.11648/j.iotcc.20160405.11,
  author = {Tian Tian and Qiuyue Jiang and Zhengying Cai},
  title = {A Sliding-Mode Control Algorithm of Self-Learning and Obstacle Avoidance for Mobile Robots},
  journal = {Internet of Things and Cloud Computing},
  volume = {4},
  number = {5},
  pages = {45-54},
  doi = {10.11648/j.iotcc.20160405.11},
  url = {https://doi.org/10.11648/j.iotcc.20160405.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.iotcc.20160405.11},
  abstract = {It is generally very difficult to make effective obstacle avoidance for mobile robots, especially in uncertain environments. This article models the obstacle avoidance problem as a nonlinear sliding mode, using the self-learning method of non-linear system. First, a sliding mode algorithm is proposed for state dependent layers of the mobile robots, in which two kinds of boundary layers are included in, namely a sector-shaped layer and a constant layer. Second, a multi-input algorithm based on sliding-mode for self-learning of mobile robots is discussed. Some control rules are built for the self-learning and obstacle avoidance of mobile robots, and the solving steps are also presented. Last, an experiment is designed to verify the proposed model and calculate the sliding- mode control for mobile robots. Some interesting conclusions and future work are indicated at the end of the paper.},
 year = {2017}
}

Copy | Download

TY  - JOUR
T1  - A Sliding-Mode Control Algorithm of Self-Learning and Obstacle Avoidance for Mobile Robots
AU  - Tian Tian
AU  - Qiuyue Jiang
AU  - Zhengying Cai
Y1  - 2017/01/14
PY  - 2017
N1  - https://doi.org/10.11648/j.iotcc.20160405.11
DO  - 10.11648/j.iotcc.20160405.11
T2  - Internet of Things and Cloud Computing
JF  - Internet of Things and Cloud Computing
JO  - Internet of Things and Cloud Computing
SP  - 45
EP  - 54
PB  - Science Publishing Group
SN  - 2376-7731
UR  - https://doi.org/10.11648/j.iotcc.20160405.11
AB  - It is generally very difficult to make effective obstacle avoidance for mobile robots, especially in uncertain environments. This article models the obstacle avoidance problem as a nonlinear sliding mode, using the self-learning method of non-linear system. First, a sliding mode algorithm is proposed for state dependent layers of the mobile robots, in which two kinds of boundary layers are included in, namely a sector-shaped layer and a constant layer. Second, a multi-input algorithm based on sliding-mode for self-learning of mobile robots is discussed. Some control rules are built for the self-learning and obstacle avoidance of mobile robots, and the solving steps are also presented. Last, an experiment is designed to verify the proposed model and calculate the sliding- mode control for mobile robots. Some interesting conclusions and future work are indicated at the end of the paper.
VL  - 4
IS  - 5
ER  -

Copy | Download