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Path Planning of UGV Based on Bézier Curves

Published online by Cambridge University Press:  21 January 2019

Yanming Hu
Affiliation:
State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110016, China University of Chinese Academy of Sciences, Beijing 100049, China
Decai Li
Affiliation:
State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110016, China
Yuqing He*
Affiliation:
State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110016, China
Jianda Han
Affiliation:
State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110016, China College of Artificial Intelligence, Nankai University, Tianjing 300071, China E-mails: [email protected], [email protected], [email protected]
*
*Corresponding author. E-mail: [email protected]

Summary

An effective path planner is critical for autonomous traversal of unmanned ground vehicles (UGVs) in harsh environments. This paper describes a novel path planning method considering Bézier curves and a two-layer planning framework. In the two-layer framework, a road centerline (RCL) estimator located on the upper layer works as a global planner to obtain the local target for the bottom local planner. The RCL is estimated from a series of candidate Bézier curves based on a safety criterion. In the bottom layer, an optimal trajectory planner and a speed planner make up the local planner to obtain the desired steering turning angle and linear speed. The criteria for optimal trajectory selection are designed for comfortable driving. Road safety is considered in the speed planner for robust driving. Three sets of simulations are used to evaluate and quantify the relative performance of variations of our path planning algorithm. The proposed path planning method is implemented on a modified Polaris RZR 800 UGV, too. Two experiments based on this UGV are set up in the country road environment to demonstrate the viability of the proposed method.

Type
Articles
Copyright
Copyright © Cambridge University Press 2019 

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References

Moreno, L., “Navigation of mobile robots: Open questions,” Robotica 18, 227234 (2000).Google Scholar
Fankhauser, P., Bloesch, M., Gehring, C., Hutter, M. and Siegwart, R., “Robot-Centric Elevation Mapping with Uncertainty Estimates,” in CLAWAR, 18 (2014).Google Scholar
Parkinson, B. W., Enge, P., Axelrad, P. and Jr, J. J. S., Global Positioning System: Theory and Applications, vol. II (American Institute of Aeronautics and Astronautics, 1996) pp. 121175.CrossRefGoogle Scholar
Qi, H. and Moore, J. B., “Direct Kalman filtering approach for GPS/INS integration,” IEEE Trans. Aerosp. Electron. Syst. 38, 687693 (2002).Google Scholar
Noureldin, A., El-Shafie, A. and Bayoumi, M., “GPS/INS integration utilizing dynamic neural networks for vehicular navigation,” Inf. Fusion 12, 4857 (2011).CrossRefGoogle Scholar
Chand, P. and Carnegie, D. A., “A two-tiered global path planning strategy for limited memory mobile robots,” Rob. Auton. Syst. 60, 309321 (2012).CrossRefGoogle Scholar
Yu, F., Tien-Ruey, H. and Sheng-Luen, C., “Multi-waypoint visual homing in piecewise linear trajectory,” Robotica 31, 479491 (2013).Google Scholar
Ma, Y., Zheng, G., Perruquetti, W. and Qiu, Z., “Local path planning for mobile robots based on intermediate objectives,” Robotica 33, 10171031 (2015).CrossRefGoogle Scholar
Hank, M. and Haddad, M., “A hybrid approach for autonomous navigation of mobile robots in partially-known environments,” Rob. Auton. Syst. 86, 113127 (2016).CrossRefGoogle Scholar
Bajracharya, M., Howard, A., Matthies, L. H., Tang, B. and Turmon, M., “Autonomous off-road navigation with end-to-end learning for the LAGR program,” J. Field Rob. 26, 325 (2009).CrossRefGoogle Scholar
Buehler, M., Iagnemma, K. and Singh, S., The 2005 DARPA Grand Challenge: The Great Robot Race (Springer Publishing Company, Incorporated, 2007) pp. 145.CrossRefGoogle Scholar
Buehler, M., Iagnemma, K. and Singh, S., The DARPA Urban Challenge: Autonomous Vehicles in City Traffic vol. 56. (Springer Publishing Company, Incorporated, 2009) pp. 11.CrossRefGoogle Scholar
Thrun, S., Montemerlo, M., Dahlkamp, H., Stavens, D., Aron, A., Diebel, J., Fong, P., Gale, J., Halpenny, M., Hoffmann, G., Lau, K., Oakley, C., Palatucci, M., Pratt, V., Stang, P., Strohband, S., Dupont, C., Jendrossek, L.-E., Koelen, C., Markey, C., Rummel, C., vanNiekerk, J., Jensen, E., Alessandrini, P., Bradski, G., Davies, B., Ettinger, S., Kaehler, A., Nefian, A. and Mahoney, P., “Stanley: the robot that won the DARPA grand challenge,” J. Field Rob. 23, 661692 (2006).CrossRefGoogle Scholar
Urmson, C., Anhalt, J., Bagnell, D., Baker, C., Bittner, R., Clark, M. N., Dolan, J., Duggins, D., Galatali, T., Geyer, C., Gittleman, M., Harbaugh, S., Hebert, M., Howard, T. M., Kolski, S., Kelly, A., Likhachev, M., McNaughton, M., Miller, N., Peterson, K., Pilnick, B., Rajkumar, R., Rybski, P., Salesky, B., Seo, Y.-W., Singh, S., Snider, J., Stentz, A., “Red” Whittaker, W., Wolkowicki, Z., Ziglar, J., Bae, H., Brown, T., Demitrish, D., Litkouhi, B., Nickolaou, J., Sadekar, V., Zhang, W., Struble, J., Taylor, M., Darms, M. and Ferguson, D., “Autonomous driving in urban environments: Boss and the urban challenge,” J. Field Rob. 25, 425466 (2008).CrossRefGoogle Scholar
Montemerlo, M., Bhat, S., Bhat, S., Dahlkamp, H., Dolgov, D., Ettinger, S., Haehnel, D., Hilden, T., Hoffmann, G., Huhnke, B., Johnston, D., Klumpp, S., Langer, D., Levandowski, A., Levinson, J., Marcil, J., Orenstein, D., Paefgen, J., Penny, I., Petrovskaya, A., Pflueger, M., Stanek, G., Stavens, D., Vogt, A. and Thrun, S., “Junior: The stanford entry in the urban challenge,” J. Field Rob. 25, 569597 (2009).CrossRefGoogle Scholar
Bacha, A., Bauman, C., Faruque, R., Fleming, M., Terwelp, C., Reinholtz, C., Hong, D., Wicks, A., Alberi, T., Anderson, D., Cacciola, S., Currier, P., Dalton, A., Farmer, J., Hurdus, J., Kimmel, S., King, P., Taylor, A., Van Covern, D. and Webster, M., “Odin: Team victortango’s entry in the DARPA urban challenge,” J. Field Rob. 25, 467492 (2010).CrossRefGoogle Scholar
Wang, Y., Mulvaney, D., Sillitoe, I. and Swere, E., “Robot navigation by waypoints,” J. Intell. Rob. Syst. 52, 175207 (2008).CrossRefGoogle Scholar
Ratsamee, P., Thiemjarus, S. and Kondo, T., “An enhancement of waypoint navigation system for intelligent vehicle using Google Earth,” The International Conference on Information and Communication Technology for Embedded Systemsthe International Conference on Information and Communication Technology for Embedded Systems (2010) pp. 10431047.Google Scholar
Park, M. C. and Ha, S. W., “The visualization tool of the open-source based for flight waypoint tracking,” International Conference on Ubiquitous Computing and Multimedia Applications (2011) pp. 153161.CrossRefGoogle Scholar
Olszewska, J. I. and Toman, J., OPEN: New Path-Planning Algorithm for Real-World Complex Environment (Springer International Publishing, 2016) pp. 237244.Google Scholar
Ratsamee, P., Thiemjarus, S. and Kondo, T., “A multi-sensor-based navigation framework for intelligent vehicle,” Suranaree J. Sci. Technol. 17, 114 (2010).Google Scholar
Google. Google Earth. Available (2018): https://earth.google.com/web/Google Scholar
Hart, P. E., Nilsson, N. J. and Raphael, B., “A formal basis for heuristic determination of minimum path cost,” IEEE Transactions on Systems Science & Cybernetics, 4, 2829 (1972).Google Scholar
Kelly, A. and Stentz, A., “Rough terrain autonomous mobility—Part 1: A theoretical analysis of requirements,” Auton. Rob. 5, 129161 (1998).CrossRefGoogle Scholar
Kelly, A. and Stentz, A., “Rough terrain autonomous mobility—Part 2: An active vision, predictive control approach,” Auton. Rob. 5, 163198 (1998).CrossRefGoogle Scholar
Howard, T. M., Green, C. J. and Kelly, A., “State space sampling of feasible motions for high performance mobile robot navigation in highly constrained environments,” J. Field Rob. 25, 325345 (2008).CrossRefGoogle Scholar
Xu, W., Wei, J., Dolan, J. M., Zhao, H. and Zha, H., “A real-time motion planner with trajectory optimization for autonomous vehicles,” IEEE International Conference on Robotics and Automation (2012) pp. 20612067.Google Scholar
Chen, C., He, Y., Bu, C. and Han, J., “Quartic Bézier curve based trajectory generation for autonomous vehicles with curvature and velocity constraints,” IEEE International Conference on Robotics and Automation (2014) pp. 61086113.Google Scholar
Zhang, L., Sun, L., Zhang, S. and Liu, J., “Trajectory planning for an indoor mobile robot using quintic Bezier curves,” IEEE International Conference on Robotics and Biomimetics (2015) pp. 757762.Google Scholar
Chen, C., Yu-Qing, H. E., Chun-Guang, B. U. and Han, J. D., “Feasible trajectory generation for autonomous vehicles based on quartic Bézier curve,” Acta Autom. Sin. 41, 486496 (2015).Google Scholar
Souza, A. and Gonçalves, L. M. G., “Occupancy-elevation grid: An alternative approach for robotic mapping and navigation,” Robotica 34, 25922609 (2016).CrossRefGoogle Scholar
Elfes, A., “Using occupancy grids for mobile robot perception and navigation,” Computer 22, 4657 (2002).CrossRefGoogle Scholar
Abbeel, P. and Ng, A. Y., “Apprenticeship learning via inverse reinforcement learning,” International Conference on Machine Learning, Banff, Canada (2004) p. 1.Google Scholar
Ramachandran, D. and Amir, E., “Bayesian inverse reinforcement learning,” International Joint Conference on Artifical Intelligence (2007) pp. 25862591.Google Scholar
Ziebart, B. D., Maas, A., Bagnell, J. A. and Dey, A. K., “Maximum entropy inverse reinforcement learning,” National Conference on Artificial Intelligence vol. 3 (2008) pp. 14331438.Google Scholar
Wulfmeier, M., Ondruska, P. and Posner, I., “Maximum entropy deep inverse reinforcement learning” (2015).Google Scholar
Finn, C., Levine, S. and Abbeel, P., “Guided cost learning: Deep inverse optimal control via policy optimization,” International Conference on International Conference on Machine Learning (2016) pp. 4958.Google Scholar
Foote, T., “Tf: The transform library,” IEEE International Conference on Technologies for Practical Robot Applications (2013) pp. 16.Google Scholar