Açısal Duruş Kontrolü Destekli Özgün bir Dinamik Pencere Yaklaşımı

Year 2020, , 184 - 200, 28.06.2020

Suat Karakaya , Hasan Ocak

https://doi.org/10.35193/bseufbd.705765

Cited By: 2

Abstract

Bu çalışmada mobil robot sistemleri için hareket planlama probleminde sıklıkla uygulanan Dinamik Pencere Yaklaşımı (DWA) metoduna bir açısal son duruş kontrolü önerilmiştir. Standart uygulamada bir başlangıç ve hedef konumu arasında yol planlama işlevi gören metot hedef konumu için bir açısal duruş kontrolü veya noktasal stabilizasyon sağlamamaktadır. Literatürdeki bu boşluğu doldurmak için çalışılan harita üzerinde bir “sanal garaj” tanımlanarak nihai hedefe yakınsadıkça değişen, adaptif bir yörünge takip prosedürü tanımlanmış, nihai konumda sistemin belli bir duruş açısında konumlanması sağlanmıştır. Yapılan testler sonucunda hesaplanan açısal duruş hataları tatmin edici sonuçlar elde edildiğini göstermiştir.

Keywords

Dinamik Yol Planlama, Pozisyon Kontrolü, Dinamik Pencere Yaklaşımı

A Novel Dynamic Window Approach Supported by Posture Control

Abstract

In this study, a final posture control method is proposed to the Dynamic Window Approach (DWA) which is frequently used in the motion planning problem for mobile robot systems. In common DWA applications, the method that performs the path planning task between a start and target position does not provide an angular posture control or point stabilization for the target position. In order to solve this problem in the literature, a “virtual garage” was defined on the map, and an adaptive trajectory tracking procedure that varies as the robot converged to the target pose was defined, and the system was positioned at a certain heading angle in the target position. The angular posture errors calculated as a result of the tests proved that satisfactory results were obtained.

Keywords

Dynamic Path Planning, Posture Control, Dynamic Window Approach

References

• Dugarjav, B., Kim, H. & Lee, H. (2015). Online Cell Decomposition with a Laser Range Finder for Coverage Path in an Unknown Workspace. International Journal of Mechanical And Production Engineering, 3, 18-24.
• Fahad, I., Jauwairia, N., Usman, M., Yasar, A. & Osman, H. (2012). RRT-Smart: Rapid Convergence Implementation of RRT* Towards Optimal Solution. 2. IEEE International Conference on Mechatronics and Automation (ICMA). Ağustos, Çin, 1651-1656.
• Wang, J., Wu, S., Li, H. & Zou, J. (2018). Path Planning Combining Improved Rapidly-Exploring Random Trees with Dynamic Window Approach in ROS. IEEE Conference on Industrial Electronics and Applications (ICIEA). Temmuz, Çin, 1296-1301.
• Firas, R. & Mustafa, M. (2017). Development of Modified Path Planning Algorithm Using Artificial Potential Field (APF) Based on PSO for Factors Optimization. American Scientific Research Journal for Engineering, Technology, and Sciences, 37, 316-328.
• Siméon, T., Laumond, J. & Nissoux, C. (2000). Visibility-Based Probabilistic Roadmaps for Motion Planning. Journal of Advanced Robotics, Technology, 14, 477-493.
• Özdemir, A. & Sezer, V. (2018). Follow the Gap with Dynamic Window Approach. International Journal of Semantic Computing, 12, 43-57.
• Borenstein, J., Koren, Y. (1991). The Vector Field Histogram-Fast Obstacle Avoidance for Mobile Robots. IEEE Trans. Robot. Autom, 7, 278-288.
• Sezer, V. & Gokasan, M. (2012). A Novel Obstacle Avoidance Algorithm: Follow the Gap Method. Robot. Auton. Syst, 60, 1123-1134.
• Marin, P., Hussein, A., Martin, D. & Escalera, A. (2018). Global and Local Path Planning Study in a ROS-Based Research Platform for Autonomous Vehicles. Journal of Advanced Transportation, 60, 1-10.
• McNaughton, M., Urmson, C., Dolan, M. & Lee, J. (2018). Motion Planning for Autonomous Driving with a Conformal Spatiotemporal Lattice. IEEE International Conference on Robotics and Automation. Mayıs, Çin, 4889-8995.
• Guoyang, L., Genxia, W. & Wei, W. (2006). ND-DWA: A Reactive Method for Collision Avoidance in Troublesome Scenarios. World Congress on Intelligent Control and Automation. Haziran, Çin, 9307-9311.
• Fox, D., Burgard, W. & Thrun, S. (1997). The Dynamic Window Approach to Collision Avoidance. IEEE Robotics and Automation Magazine, 4, 23-33.
• Furrer, F., Burri, M., Achtelik, M. & Siegwart, R. (2016). Robot Operating System (ROS). Springer International Publishing, İsviçre, 74-78.
• Dongkai, F. & Shi, P. (2010). Improvement of Dijkstra's algorithm and Its Application in Route Planning. IEEE Journal of Oceanic Engineering, 13, 1901-1904.
• Stephen, B. & Lieven, V (2004). Unconstrained Minimization Convex Optimization. Cambridge University Press, New York, 457-458.
• Karakaya, S., Küçükyıldız, G. & Ocak, H (2017). A New Mobile Robot Toolbox for MATLAB. J Intell Robot Syst, 87, 125-140.
• Karakaya S. & Ocak, H. (2019). Design and Implementation of a Wheeled Mobile Robot Platform. International Conference on Image Processing, Wavelet and Applications. Ekim, Türkiye, 1-9.
• Gopikrishnan, S., Shravana, S., Harshit G., Barve, P. & Ravikumar L (2011). Path Planning Algorithms: A Comparative Study. National Conference on Space Transportation Systems. Aralık, Hindistan, 1-8.