Skip to main content
Springer Nature Link
Log in

A new fallback beetle antennae search algorithm for path planning of mobile robots with collision-free capability

  • Methodologies and Application
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

With the development of technology, mobile robots are becoming more and more common in industrial production and daily life. Various rules are set to ensure that mobile robots can move without collision. This paper proposes a novel intelligent optimization algorithm, named fallback beetle antennae search algorithm. Based on the analysis of biological habits, when the creature enters blind alley during the foraging process, it will retreat a distance and then restart the search process. We introduce a fallback mechanism in the traditional beetle antenna search algorithm. In addition, the proposed algorithm possesses the characteristic of low time complexity. It can plan a collision-free path in a short period of time. Moreover, the effectiveness and superiority of the algorithm are verified by simulations in different types of environments and comparisons with existing path planning algorithms.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  • Bandi S, Thalmann D (2000) Path finding for human motion in virtual environments. Comput Geom 15:103–127

    Article  Google Scholar 

  • Berglund T, Brodnik A, Jonsson H, Staffanson M, Soderkvist I (2010) Planning smooth and obstacle-avoiding B-spline paths for autonomous mining vehicles. IEEE Trans Autom Sci Eng 7:167–172

    Article  Google Scholar 

  • Chen D, Zhang Y (2016) Minimum jerk norm scheme applied to obstacle avoidance of redundant robot arm with jerk bounded and feedback control. IET Control Theory Appl 10(15):1896–1903

    Article  MathSciNet  Google Scholar 

  • Chen D, Zhang Y (2017) A hybrid multi-objective scheme applied to redundant robot manipulators. IEEE Trans Autom Sci Eng 14(7):1337–1350

    Article  Google Scholar 

  • Chen D, Zhang Y (2018) Robust zeroing neural-dynamics and its time-varying disturbances suppression model applied to mobile robot manipulators. IEEE Trans Neural Netw Learn Syst 29(9):4385–4397

    Article  Google Scholar 

  • Chen Y, Cheng L, Wu H, Zhao X, Han J (2015) Knowledge-driven path planning for mobile robots: relative state tree. Soft Comput 19:763–773

    Article  Google Scholar 

  • Chen Y, Gao J, Yang G, Liu Y (2018a) Solving equilibrium standby redundancy optimization problem by hybrid PSO algorithm. Soft Comput 22(17):5631–5645

    Article  Google Scholar 

  • Chen D, Zhang Y, Li S (2018b) Zeroing neural-dynamics approach and its robust and rapid solution for parallel robot manipulators against superposition of multiple disturbances. Neurocomputing 275:845–858

    Article  Google Scholar 

  • Chen D, Zhang Y, Li S (2018c) Tracking control of robot manipulators with unknown models: a Jacobian-matrix-adaption method. IEEE Trans Ind Inf 14(7):3044–3053

    Article  Google Scholar 

  • Chen D, Li S, Wu Q (2019) Rejecting chaotic disturbances using a super-exponential-zeroing neurodynamic approach for synchronization of chaotic sensor systems. Sensors 19(1):74

    Article  Google Scholar 

  • Cong YZ, Ponnambalam S (2009) Mobile robot path planning using ant colony optimization. In: IEEE/ASME international conference on advanced intelligent mechatronics, 2009. AIM 2009. 2009. IEEE, New York, pp 851–856

  • Dijkstra EW (1959) A note on two problems in connexion with graphs. Numer Math 1:269–271

    Article  MathSciNet  Google Scholar 

  • Eilers PH, Marx BD (1996) Flexible smoothing with B-splines and penalties. Stat Sci 11:89–102

    Article  MathSciNet  Google Scholar 

  • Elbanhawi M, Simic M, Jazar RN (2015) Continuous path smoothing for car-like robots using B-spline curves. J Intell Robot Syst 80:23–56

    Article  Google Scholar 

  • González D, Pérez J, Milanés V, Nashashibi F (2016) A review of motion planning techniques for automated vehicles. IEEE Trans Intell Transp Syst 17:1135–1145

    Article  Google Scholar 

  • Guo D, Zhang Y (2012) A new inequality-based obstacle-avoidance MVN scheme and its application to redundant robot manipulators. IEEE Trans Syst Man Cybern Part C (Appl Rev) 42(6):1326–1340

    Article  Google Scholar 

  • Guo D, Zhang Y (2014) Acceleration-level inequality-based MAN scheme for obstacle avoidance of redundant robot manipulators. IEEE Trans Ind Electron 61(12):6903–6914

    Article  Google Scholar 

  • Gupta M, Kumar S, Behera L, Subramanian VK (2017) A novel vision-based tracking algorithm for a human-following mobile robot. IEEE Trans Syst Man Cybern Syst 47:1415–1427

    Article  Google Scholar 

  • Guruji AK, Agarwal H, Parsediya D (2016) Time-efficient A* algorithm for robot path planning. Proc Technol 23:144–149

    Article  Google Scholar 

  • Jiang X, Li S (2018) BAS: beetle antennae search algorithm for optimization problems. Int J Robot Control. https://doi.org/10.5430/ijrc.v1n1p1

    Article  Google Scholar 

  • Koren Y, Borenstein J (1991) Potential field methods and their inherent limitations for mobile robot navigation. In: Proceedings, 1991 IEEE international conference on robotics and automation, 1991. IEEE, New York, pp 1398–1404

  • Koyuncu E, Inalhan G (2008) A probabilistic B-spline motion planning algorithm for unmanned helicopters flying in dense 3D environments. In: IROS 2008. IEEE/RSJ international conference on Intelligent Robots and Systems, 2008. IEEE, New York, pp 815–821

  • Lee MC, Park MG (2003) Artificial potential field based path planning for mobile robots using a virtual obstacle concept. In: 2003 IEEE/ASME international conference on advanced intelligent mechatronics, 2003. AIM 2003. Proceedings. IEEE, New York, pp 735–740

  • Li S, He J, Li Y, Rafique MU (2017) Distributed recurrent neural networks for cooperative control of manipulators: a game-theoretic perspective. IEEE Trans Neural Netw Learn Syst 28:415–426

    Article  MathSciNet  Google Scholar 

  • Liu J, Yang J, Liu H, Tian X, Gao M (2017) An improved ant colony algorithm for robot path planning. Soft Comput 21:5829–5839

    Article  Google Scholar 

  • Luo C, Yang SX (2008) A bioinspired neural network for real-time concurrent map building and complete coverage robot navigation in unknown environments. IEEE Trans Neural Netw 19:1279–1298

    Article  Google Scholar 

  • Masehian E, Sedighizadeh D (2010) A multi-objective PSO-based algorithm for robot path planning. In: 2010 IEEE international conference on industrial technology (ICIT). IEEE, New York, pp 465–470

  • Mavrovouniotis M, Yang S (2011) A memetic ant colony optimization algorithm for the dynamic travelling salesman problem. Soft Comput 15:1405–1425

    Article  Google Scholar 

  • Montiel O, Sepúlveda R, Orozco-Rosas U (2015) Optimal path planning generation for mobile robots using parallel evolutionary artificial potential field. J Intell Robot Syst 79:237–257

    Article  Google Scholar 

  • Mur-Artal R, Tardós JD (2017) Visual–inertial monocular SLAM with map reuse. IEEE Robot Autom Lett 2:796–803

    Article  Google Scholar 

  • Ni J, Wu L, Fan X, Yang SX (2016) Bioinspired intelligent algorithm and its applications for mobile robot control: a survey. Comput Intell Neurosci 2016:1

    Article  Google Scholar 

  • Pau G, Collotta M, Maniscalco V, Choo K-KR (2018) A fuzzy-PSO system for indoor localization based on visible light communications. Soft Comput 22:1–11

    Article  Google Scholar 

  • Rathbun D, Kragelund S, Pongpunwattana A, Capozzi B (2002) An evolution based path planning algorithm for autonomous motion of a UAV through uncertain environments. In: Digital avionics systems conference, 2002. Proceedings. The 21st. IEEE, New York, pp 8D2–8D2

  • Rezaee H, Abdollahi F (2014) A decentralized cooperative control scheme with obstacle avoidance for a team of mobile robots. IEEE Trans Ind Electron 61:347–354

    Article  Google Scholar 

  • Sariff N, Buniyamin N (2006) An overview of autonomous mobile robot path planning algorithms. In: SCOReD 2006. 4th student conference on research and development, 2006, IEEE, New York, pp 183–188

  • Stachniss C, Burgard W (2003) Exploring unknown environments with mobile robots using coverage maps. IJCAI 2003:1127–1134

    Google Scholar 

  • Stentz A (1994) Optimal and efficient path planning for partially-known environments. ICRA 1994:3310–3317

    Google Scholar 

  • Tan X, Chen D (2009) A hybrid approach of path planning for mobile robots based on the combination of ACO and APF algorithms. In: International workshop on intelligent systems and applications, 2009. ISA 2009. IEEE, New York, pp 1–4

  • Tsai C-C, Huang H-C, Chan C-K (2011) Parallel elite genetic algorithm and its application to global path planning for autonomous robot navigation. IEEE Trans Ind Electron 58:4813–4821

    Article  Google Scholar 

  • Van Den Berg J, Ferguson D, Kuffner J (2006) Anytime path planning and replanning in dynamic environments. In: Proceedings 2006 IEEE international conference on robotics and automation, 2006. ICRA 2006. IEEE, New York, pp 2366–2371

  • Velagic J, Lacevic B, Osmic N (2006) Efficient path planning algorithm for mobile robot navigation with a local minima problem solving. In: IEEE international conference on industrial technology, 2006. ICIT 2006. IEEE, New York, pp 2325–2330

  • Xiao L, Zhang Y (2014) A new performance index for the repetitive motion of mobile manipulators. IEEE Trans Cybern 44:280–292

    Article  Google Scholar 

  • Xiao L, Zhang Y (2016) Dynamic design, numerical solution and effective verification of acceleration-level obstacle-avoidance scheme for robot manipulators. Int J Syst Sci 47:932–945

    Article  MathSciNet  Google Scholar 

  • Xue T, Li R, Tokgo M, Ri J, Han G (2017) Trajectory planning for autonomous mobile robot using a hybrid improved QPSO algorithm. Soft Comput 21:2421–2437

    Article  Google Scholar 

  • Yang X, Gao J (2018) Linear quadratic uncertain differential game with application to resource extraction problem. IEEE Trans Fuzzy Syst 24(4):819–826

    Article  Google Scholar 

  • Zaki AM, Arafa O, Amer SI (2014) Microcontroller-based mobile robot positioning and obstacle avoidance. J Electr Syst Inf Technol 1:58–71

    Google Scholar 

  • Zhang Y, Li Z, Guo D, Li W, Chen P (2013) Z-type and G-type models for time-varying inverse square root (TVISR) solving. Soft Comput 17:2021–2032

    Article  Google Scholar 

  • Zhang Y, Yan X, Chen D, Guo D, Li W (2016a) QP-based refined manipulability-maximizing scheme for coordinated motion planning and control of physically constrained wheeled mobile redundant manipulators. Nonlinear Dyn 85:245–261

    Article  MathSciNet  Google Scholar 

  • Zhang Y, Qu L, Liu J, Guo D, Li M (2016b) Sine neural network (SNN) with double-stage weights and structure determination (DS-WASD). Soft Comput 20:211–221

    Article  Google Scholar 

  • Zhong X, Zhong X, Peng X (2016c) VCS-based motion planning for distributed mobile robots: collision avoidance and formation. Soft Comput 20:1897–1908

    Article  Google Scholar 

  • Zhu D, Yan M (2010) Survey on technology of mobile robot path planning. Control Decis 25:961–967

    MATH  Google Scholar 

Download references

Acknowledgements

This work is supported by the National Natural Science Foundation of China (with Nos. 61401385 and 61702146), by Hong Kong Research Grants Council Early Career Scheme (with No. 25214015), by Departmental General Research Fund of Hong Kong Polytechnic University (with No. G.61.37.UA7L) and also by PolyU Central Research Grant (with No. G-YBMU).

Author information

Authors and Affiliations

  1. Department of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China

    Qing Wu, Hao Lin, Zeyu Chen & Dechao Chen

  2. Department of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China

    Yuanzhe Jin

  3. Department of Computing, Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China

    Shuai Li

Authors
  1. Qing Wu
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Hao Lin
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Yuanzhe Jin
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Zeyu Chen
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Shuai Li
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Dechao Chen
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Dechao Chen.

Ethics declarations

Conflict of interest

The authors declare that there is no conflict of interest in preparing this article.

Additional information

Communicated by V. Loia.

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

Wu, Q., Lin, H., Jin, Y. et al. A new fallback beetle antennae search algorithm for path planning of mobile robots with collision-free capability. Soft Comput 24, 2369–2380 (2020). https://doi.org/10.1007/s00500-019-04067-3

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-019-04067-3

Keywords