Abstract
This paper studied several obstacle avoidance strategies proposed within recent state of the art for autonomous navigation of wheeled robots to efficiently deal with unknown environment. Some initial experiments have been performed in this paper to identify the most important features, (e.g., obstacle representation, efficient control of robot’s direction, etc.) that an obstacle avoidance strategy must include in order to guarantee an effective autonomous navigation system for wheeled robots in unknown environments.
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References
Mujahed, M., Fischer, D., Mertsching, B.: Admissible gap navigation: a new collision avoidance approach. Robot. Auton. Syst. 103, 93–110 (2018). https://doi.org/10.1016/j.robot.2018.02.008
Wang, C., Meng, L., She, S., Mitchell, I.M., Li, T., Tung, F., Wan, W., Meng, M.Q.H., de Silva, C.W.: Autonomous mobile robot navigation in uneven and unstructured indoor environments. In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 109–116 (2017). https://doi.org/10.1109/IROS.2017.8202145
Minguez, J., Montano, L.: Extending collision avoidance methods to consider the vehicle shape, kinematics, and dynamics of a mobile robot. IEEE Trans. Robot. 25(2), 367–381 (2009). https://doi.org/10.1109/TRO.2009.2011526
Pothal, J.K., Parhi, D.R.: Navigation of multiple mobile robots in a highly clutter terrains using adaptive neuro-fuzzy inference system. Robot. Auton. Syst. 72, 48–58 (2015). https://doi.org/10.1016/j.robot.2015.04.007
Antich Tobaruela, J., Ortiz, R.A.: Reactive navigation in extremely dense and highly intricate environments. PLoS ONE 12(12), e0189008 (2017). https://doi.org/10.1371/journal.pone.0189008
Duguleana, M., Mogan, G.: Neural networks based reinforcement learning for mobile robots obstacle avoidance. Expert. Syst. Appl. 62, 104–115 (2016). https://doi.org/10.1016/j.eswa.2016.06.021
Xia, C., El Kamel, A.: Neural inverse reinforcement learning in autonomous navigation. Robot. Auton. Syst. 84, 1–14 (2016). https://doi.org/10.1016/j.robot.2016.06.003
Asvadi, A., Premebida, C., Peixoto, P., Nunes, U.: 3D Lidar-based static and moving obstacle detection in driving environments: an approach based on voxels and multi-region ground planes. Robot. Auton. Syst. 83, 299–311 (2016). https://doi.org/10.1016/j.robot.2016.06.007
Hornung, A., Wurm, K.M., Bennewitz, M., Stachniss, C., Burgard, W.: OctoMap: an efficient probabilistic 3D mapping framework based on octrees. Auton. Robot. 34, 189–206 (2013). https://doi.org/10.1007/s10514-012-9321-0
LMS291 datasheet. https://cdn.sick.com/media/pdf/9/49/849/dataSheet_LMS291-S05_1018028_es.pdf
Michel, O.: Cyberbotics Ltd. Webots\(^{\rm {TM}}\): professional mobile robot simulation. Int. J. Adv. Rob. Syst. 1, 39–42 (2004)
Pioneer 3-AT datasheet. http://www.mobilerobots.com/Libraries/Downloads/Pioneer3AT-P3AT-RevA.sflb.ashx
Schwiegelshohn, F., Wehner, P., Werner, F., Gohringer, D., Hubner, M.: Enabling indoor object localization through Bluetooth beacons on the RADIO robot platform. In: 2016 International Conference on Embedded Computer Systems: Architectures, Modeling and Simulation (SAMOS), Agios Konstantinos, pp. 328–333 (2016). https://doi.org/10.1109/SAMOS.2016.7818366
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Cristiano, J., Rashwan, H.A., Puig, D. (2019). Study of Obstacle Avoidance Strategies for Efficient Autonomous Navigation of Wheeled Robots in Unknown Environments. In: Fuentetaja Pizán, R., García Olaya, Á., Sesmero Lorente, M., Iglesias Martínez, J., Ledezma Espino, A. (eds) Advances in Physical Agents. WAF 2018. Advances in Intelligent Systems and Computing, vol 855. Springer, Cham. https://doi.org/10.1007/978-3-319-99885-5_4
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DOI: https://doi.org/10.1007/978-3-319-99885-5_4
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