An Improved Dynamic Window Approach with Environment Awareness for Local Obstacle Avoidance of Mobile Robots

Authors: Baoshan Wei, Shuai Han, Xing Zhang

Abstract:

Local obstacle avoidance is critical for mobile robot navigation. It is a challenging task to ensure path optimality and safety in cluttered environments. We proposed an Environment Aware Dynamic Window Approach in this paper to cope with the issue. The method integrates environment characterization into Dynamic Window Approach (DWA). Two strategies are proposed in order to achieve the integration. The local goal strategy guides the robot to move through openings before approaching the final goal, which solves the local minima problem in DWA. The adaptive control strategy endows the robot to adjust its state according to the environment, which addresses path safety compared with DWA. Besides, the evaluation shows that the path generated from the proposed algorithm is safer and smoother compared with state-of-the-art algorithms.

Keywords: Adaptive control, dynamic window approach, environment aware, local obstacle avoidance, mobile robots.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.2643960

References:

[1] Hoy, Michael, Alexey S. Matveev, and Andrey V. Savkin, “Algorithms for collision-free navigation of mobile robots in complex cluttered environments: a survey,” Robotica, vol. 33, pp. 463-497, 2015.
[2] Borenstein, Johann, and Yoram Koren, “Real-time obstacle avoidance for fast mobile robots,” IEEE Transactions on systems, Man, and Cybernetics, vol. 19, pp. 1179-1187, 1989.
[3] Borenstein, Johann, and Yoram Koren, “The vector field histogram-fast obstacle avoidance for mobile robots,” IEEE transactions on robotics and automation, vol. 7, pp. 278-288, 1991.
[4] Ulrich, Iwan, and Johann Borenstein, “VFH+: Reliable obstacle avoidance for fast mobile robots,” in Proc. ICRA Conf., 1998, pp. 1572-1577.
[5] Weerakoon, Tharindu, Kazuo Ishii, and Amir Ali Forough Nassiraei, “An artificial potential field based mobile robot navigation method to prevent from deadlock,” Journal of Artificial Intelligence and Soft Computing Research, vol. 5, pp. 189-203, 2015.
[6] Demir, Mustafa, and Volkan Sezer, “Improved follow the gap method for obstacle avoidance,” in Advanced Intelligent Mechatronics (AIM), 2017, pp. 1435-1440.
[7] Simmons, Reid, “The curvature-velocity method for local obstacle avoidance,” in Proc. ICRA Conf., 1996, pp. 3375-3382.
[8] Fox, Dieter, Wolfram Burgard, and Sebastian Thrun, “The dynamic window approach to collision avoidance,” IEEE Robotics & Automation Magazine, vol. 4, pp. 23-33, 1997.
[9] Ulrich, Iwan, and Johann Borenstein, “VFH*: Local obstacle avoidance with look-ahead verification,” in Proc. ICRA Conf., 2000, pp. 2505-2511.
[10] Chou, Chih-Chung, Feng-Li Lian, and Chieh-Chih Wang, “Characterizing indoor environment for robot navigation using velocity space approach with region analysis and look-ahead verification,” IEEE Transactions on Instrumentation and Measurement, vol. 60, pp. 442-451, 2011.
[11] Blanco, Jose Luis, Mauro Bellone, and Antonio Gimenez-Fernandez, “TP-Space RRT: kinematic path planning of non-holonomic any-shape vehicles,” International Journal of Advanced Robotic Systems, vol. 12, pp. 55-63, 2015.
[12] Devaurs D, Simon T and Corts J, “Optimal path planning in complex cost spaces with sampling-based algorithms,” IEEE Transactions on Automation Science and Engineering, vol. 13, pp. 415-424, 2016.
[13] Samaniego, Ricardo, Joaquin Lopez, and Fernando Vazquez, “Path planning for non-circular, non-holonomic robots in highly cluttered environments,” Sensors, vol. 17, pp. 1876-1894, 2017.
[14] Napoli, Michael E., Harel Biggie, and Thomas M. Howard, “Learning models for predictive adaptation in state lattices,” Field and Service Robotics, vol. 5, pp. 285-300, 2018.
[15] Van Vliet, Lucas J., and Piet W. Verbeek, “Curvature and bending energy in digitized 2D and 3D images,” in Proceedings of the Scandinavian Conference on Image Analysis, 1993, pp. 1403-1410.