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Echtzeitfähige energiereduzierte Pfadplanung für Mobile Roboter

  • Marcel Mitschke

    Marcel Mitschke hat das Double Degree M.Sc.-Programm Maschinenbau an den Universitäten Stuttgart und Toyohashi absolviert. Arbeitsgebiete: Pfadplanung und Sensorfusion im Bereich mobile Roboter.

    , Naoki Uchiyama

    Naoki Uchiyama ist Professor im Fachbereich Maschinenbau an der Toyohashi University of Technology. Hauptarbeitsgebiete: Regelung mechanischer Systeme und Robotik.

    and Oliver Sawodny

    Oliver Sawodny ist Direktor des Instituts für Systemdynamik der Universität Stuttgart. Hauptarbeitsgebiete: Modellbildung, Identifikation dynamischer Systeme, Systemanalyse und Systemsynthese.

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Published/Copyright: June 8, 2019

Zusammenfassung

Für Aufgaben wie dem Reinigen von Außenanlagen oder Rasenmähen benötigen mobile Roboter effiziente Algorithmen zur Lösung des Coverage Path Planning (CPP) Problems. Da stetig variierende Umgebungsbedingungen auf CPP-Probleme mit hoher Komplexität führen, wird häufig auf echtzeitfähige heuristische Ansätze mit Vorgaben an die maximale Rechenzeit ausgewichen. Darüber hinaus ist ein möglichst energieeffizienter Pfad erstrebenswert, da die Batteriekapazität von mobilen Robotern begrenzt ist. Diese Veröffentlichung stellt zwei CPP-Algorithmen vor, die den Energieverbrauch eines mobilen Roboters mit und ohne Kenntnis der Umgebung berücksichtigen. Der Energieverbrauch, die benötigte Fahrtzeit, die Pfadlänge und die Rechenzeit der vorgestellten Algorithmen werden simulativ mit zwei existierenden Methoden verglichen.

Abstract

For tasks such as outdoor cleaning or lawn mowing, mobile robots need efficient algorithms to solve the Coverage Path Planning (CPP) problem. Since constantly varying environmental conditions lead to CPP problems of high complexity, real-time heuristic approaches with maximum computing times are often used. In addition, an energy-efficient path is desirable as the battery capacity of mobile robots is limited. This contribution presents two CPP algorithms that consider the energy consumption of a mobile robot with and without knowledge of the environment. The energy consumption, the required travel time, the path length and the computing time of the presented algorithms are compared with two existing methods by means of simulations.

Funding statement: This work was partially supported by Toyohashi City, Japan, Toyometal Co., Ltd., Toyohashi, Japan, and Toyohashi University of Technology, Japan as one of the Cooperative Projects for Innovative Research. The authors would like to thank them for the support.

About the authors

Marcel Mitschke

Marcel Mitschke hat das Double Degree M.Sc.-Programm Maschinenbau an den Universitäten Stuttgart und Toyohashi absolviert. Arbeitsgebiete: Pfadplanung und Sensorfusion im Bereich mobile Roboter.

Naoki Uchiyama

Naoki Uchiyama ist Professor im Fachbereich Maschinenbau an der Toyohashi University of Technology. Hauptarbeitsgebiete: Regelung mechanischer Systeme und Robotik.

Oliver Sawodny

Oliver Sawodny ist Direktor des Instituts für Systemdynamik der Universität Stuttgart. Hauptarbeitsgebiete: Modellbildung, Identifikation dynamischer Systeme, Systemanalyse und Systemsynthese.

Literatur

1. Gabriely, Yoav and Rimon, Elon, Spanning-tree based coverage of continuous areas by a mobile robot, IEEE International Conference on Robotics and Automation, 1999, pp. 1927–1933.Search in Google Scholar

2. Gabriely, Yoav and Rimon, Elon, Competitive on-line coverage of grid environments by a mobile robot, Computational Geometry, 2003, vol. 24, no. 3, pp. 197–224.10.1016/S0925-7721(02)00110-4Search in Google Scholar

3. Zelinsky, Alexander et al., Planning paths of complete coverage of an unstructured environment by a mobile robot, International Conference on Advanced Robotics, 1993, vol. 13, pp. 533–538.Search in Google Scholar

4. Zhou, Peng et al., Complete Coverage Path Planning of Mobile Robot Based on Dynamic Programming Algorithm, 2nd International Conference on Electronic and Mechanical Engineering and Information Technology, 2012, pp. 1837–1841.10.2991/emeit.2012.407Search in Google Scholar

5. Wang, Zhongmin and Bo, Zhu, Coverage path planning for mobile robot based on genetic algorithm, IEEE Workshop on Electronics, Computer and Applications, 2014, pp. 732–735.10.1109/IWECA.2014.6845726Search in Google Scholar

6. Clausen, Jens, Branch and bound algorithms-principles and examples, Department of Computer Science, University of Copenhagen, 1999, pp. 1–30.Search in Google Scholar

7. Jimenez, Paulo A. et al., Optimal area covering using genetic algorithms, IEEE/ASME international conference on advanced intelligent mechatronics, 2007, pp. 1–5.10.1109/AIM.2007.4412480Search in Google Scholar

8. Ribes, Marcel Tresanchez et al., Optimization of Floor Cleaning Coverage Performance of a Random Path-Planning Mobile Robot, Universitat de Lleida, Escola Politecnica Superior, Enginyeria en Informatica, 2007.Search in Google Scholar

9. Gonzalez, Enrique et al., BSA: a complete coverage algorithm, IEEE International Conference on Robotics and Automation, 2005, pp. 2040–2044.Search in Google Scholar

10. Uchiyama, Naoki et al., Model-reference control approach to obstacle avoidance for a human-operated mobile robot, IEEE Transactions on Industrial Electronics, 2009, vol. 56, no. 10, pp. 3892–3896.10.1109/TIE.2009.2020715Search in Google Scholar

11. Simba, Kenneth Renny et al., Vision-based smooth obstacle avoidance motion trajectory generation for autonomous mobile robots using Bézier curves, Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 2015, vol. 231, no. 3, pp. 541–554.10.1177/0954406215616986Search in Google Scholar

12. Simba, Kenneth Renny et al., Real-Time Smooth Trajectory Generation for Nonholonomic Mobile Robots Using Bezier Curves, Robotics and Computer Integrated Manufacturing, 2016, vol. 41, pp. 31–42.10.1016/j.rcim.2016.02.002Search in Google Scholar

13. Schaefle, Tobias Reiner et al., Coverage Path Planning for Mobile Robots Using Genetic Algorithm with Energy Optimization, International Electronics Symposium, IEEE, 2016, pp. 99–104.10.1109/ELECSYM.2016.7860983Search in Google Scholar

14. Siddique, Nazmul, Intelligent control: a hybrid approach based on fuzzy logic, neural networks and genetic algorithms, Springer, 2013.10.1007/978-3-319-02135-5Search in Google Scholar

15. Kapanoglu, Muzaffer et al., Pattern-based genetic algorithm approach to coverage path planning for mobile robots, International Conference on Computational Science, 2009, pp. 33–42.10.1007/978-3-642-01970-8_4Search in Google Scholar

16. Zelinsky, Alexander, Environment exploration and path planning algorithms for mobile robot navigation using sonar, University of Wollongong, 1991.10.1002/rob.4620080502Search in Google Scholar

17. Mei, Yongguo et al., Energy-efficient motion planning for mobile robots, IEEE International Conference on Robotics and Automation, 2004, pp. 4344–4349.10.1109/ROBOT.2004.1302401Search in Google Scholar

18. Xuan, Wang, Li, Yuanxiang. Solving traveling salesman problem by using a local evolutionary algorithm. Granular Computing, 2005 IEEE International Conference on. IEEE, 2005. S. 318–321.10.1109/GRC.2005.1547294Search in Google Scholar

Received: 2018-10-20
Accepted: 2019-05-16
Published Online: 2019-06-08
Published in Print: 2019-06-26

© 2019 Walter de Gruyter GmbH, Berlin/Boston

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