Hostname: page-component-586b7cd67f-dlnhk Total loading time: 0 Render date: 2024-11-30T01:05:38.542Z Has data issue: false hasContentIssue false
  • English
  • Français

Assessment of the Effect of Energy Consumption on Trajectory Improvement for a Car-like Robot

Published online by Cambridge University Press:  10 April 2019

Francisco Valero ,
Francisco Rubio Open the ORCID record for Francisco Rubio [Opens in a new window]  and
Carlos Llopis-Albert Open the ORCID record for Carlos Llopis-Albert [Opens in a new window]
Francisco Valero
Affiliation:
Centro de Investigación en Ingeniería Mecánica (CIIM), Universitat Politècnica de València, Camino de Vera s/n, 46022, Valencia, Spain E-mails: [email protected], [email protected]
Francisco Rubio*
Affiliation:
Centro de Investigación en Ingeniería Mecánica (CIIM), Universitat Politècnica de València, Camino de Vera s/n, 46022, Valencia, Spain E-mails: [email protected], [email protected]
Carlos Llopis-Albert
Affiliation:
Centro de Investigación en Ingeniería Mecánica (CIIM), Universitat Politècnica de València, Camino de Vera s/n, 46022, Valencia, Spain E-mails: [email protected], [email protected]
*
*Corresponding author. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Summary

Reducing the energy consumed by a car-like mobile robot makes it possible to move at a lower cost, yet it takes more working time. This paper proposes an optimization algorithm for trajectories with optimal times and analyzes the consequences of restricting the energy consumed on the trajectory obtained for a car-like robot. When modeling the dynamic behavior of the vehicle, it is necessary to consider its inertial parameters, the behavior of the motor, and the basic properties of the tire in its interaction with the ground. To obtain collision-free, minimum-time trajectories quadratic sequential optimization techniques are used, where the objective function is the time taken by the robot to move between two given configurations. This is subject to constraints relating to the vehicle and tires as well as the energy consumed, which is the basis for this paper. We work with a real random distribution of consumed energy values following a normal Gaussian distribution in order to analyze its influence on the trajectories obtained by the vehicle. The energy consumed, the time taken, the maximum velocity reached, and the distance traveled are analyzed in order to characterize the properties of the trajectories obtained. The proposed algorithm has been applied to 101 examples, showing that the computational times needed to obtain the solutions are always lower than those required to realize the trajectories. The results obtained allow us to reach conclusions about the energy efficiency of the trajectories.

Keywords

Energy consumedCar-like mobile robotRobot dynamicsTire interactionMinimum-time trajectoryCollision-free trajectoryWorking parameters
Type
Articles
Information
Robotica , Volume 37 , Issue 11 , November 2019 , pp. 1998 - 2009
Copyright
© Cambridge University Press 2019 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Sanchez, C. M., Sanchez, J. R. G., Cervantes, C. Y. S., Ortigoza, R. S., Guzman, V. M. H., Juarez, J. N. A. and Aranda, M. M., “Trajectory generation for wheeled mobile robots via Bézier polynomials,” IEEE Latin Am. Trans. 14(11), 4482–4490 (2016). doi:10.1109/TLA.2016.7795818.CrossRefGoogle Scholar
López, D. A., “Nuevas aportaciones en algoritmos de planificación para la ejecución de maniobras en robots autónomos no holónomos,” Tesis doctoral (Universidad de Huelva. Departamento de Ingeniería Electrónica, de Sistemas Informáticos y Automática, 2011). URL: http://hdl.handle.net/10272/5501.Google Scholar
Choset, H., Lynch, K. M., Hutchinson, S., Kantor, G., Burgard, W., Kavraki, L. E. and Thrun, S., Principles of Robot Motion: Theory, Algorithms, and Implementations (MIT Press, Boston, MA, 2005).Google Scholar
Katrakazas, C.,Quddus, M.,Chen, W.-H. and Deka, L., “Real-time motion planning methods for autonomous on-road driving: State-of-the-art and future research directions,” Transp. Res. 60, 416–442 (2015). doi:10.1016/j.trc.2015.09.011.Google Scholar
Egerstedt, M., Hu, X. and Stotsky, A., “Control of a car-like robot using a dynamic model,” IEEE Int. Conf. Rob. Autom. 4, 3273–3278 (1998). doi:10.1109/ROBOT.1998.680943.Google Scholar
Jeon, J. H., Cowlagi, R. V., Peters, S. C., Karaman, S., Frazzoli, E., Tsiotras, P. and Iagnemma, K., “Optimal Motion Planning with the Half-car Dynamical Model for Autonomous High-speed Driving,” American Control Conference (ACC), Washington, DC, USA (2013, June 17–19).Google Scholar
Ding, L., Deng, Z., Gao, H., Nagatani, K. and Yoshida, K., “Planetary rovers’ wheel–soil interaction mechanics: New challenges and applications for wheeled mobile robots,” Intell. Serv. Rob. 4(1), 17–38 (2011). doi:10.1007/s11370-010-0080-5.CrossRefGoogle Scholar
Valero, F., Rubio, F., Llopis-Albert, C. and Cuadrado, J. I., “Influence of the friction coefficient on the trajectory performance for a car-like robot,” Math. Prob. Eng. 2017, (2017). doi:10.1155/2017/4562647.CrossRefGoogle Scholar
Kim, C. and Kim, B., “Minimum-energy Rotational Trajectory Planning for Differential-Driven Wheeled Mobile Robots,” Proceedings of 13th International Conference on Advanced Robotics, Jeju, South Korea (2007, 21–24 August) pp. 265–270.Google Scholar
Tokekar, P., Karnad, N. and Isler, V., “Energy-optimal trajectory planning for car-like robots,” Autonom. Rob. 37(3), 279–300 (2014). doi:10.1007/s10514-014-9390-3.CrossRefGoogle Scholar
Broderick, J. A., Tilbury, D. M. and Atkins, E. M., “Optimal coverage trajectories for a UGV with tradeoffs for energy and time,” Autonom. Rob. 36, 257 (2014). doi:10.1007/s10514-013-9348-x.CrossRefGoogle Scholar
Wong, J., Theory of Ground Vehicles (Wiley, New York, 2001).Google Scholar
Broderick, J., Tilbury, D. and Atkins, E., “Maximizing Coverage for Mobile Robots While Conserving Energy,” Proceedings of the ASME 2012 IDETC/CIE, Chicago, Illinois, USA, vol. 4 (2012) pp. 791–798. doi:10.1115/DETC2012-70443.CrossRefGoogle Scholar
Oukacha, O. and Boizot, N., “Consumption Minimisation for a Car-like Robot: Case Study for a Nonflat Road Profile,” 2017 6th International Conference on Systems and Control (ICSC), Batna (2017) pp. 334–341. doi:10.1109/ICoSC.2017.7958672.CrossRefGoogle Scholar
Salazar, M., Alessandretti, A., Aguiar, A. P. and Jones, C. N., “An Energy Efficient Trajectory Tracking Controller for Car-like Vehicles Using Model Predictive Control,” 2015 54th IEEE Conference on Decision and Control (CDC), Osaka (2015) pp. 3675–3680. doi:10.1109/CDC.2015.7402789.CrossRefGoogle Scholar
Liu, S. and Sun, D., “Minimizing energy consumption of wheeled mobile robots via optimal motion planning,” Mechatron. IEEE/ASME Trans. 19(2), 401–411 (2014).CrossRefGoogle Scholar
Luo, X., Li, S., Liu, S. and Liu, G., “An optimal trajectory planning method for path tracking of industrial robots,” Robotica 37(3), 502–520 (2019). doi:10.1017/S0263574718001145.CrossRefGoogle Scholar
Llopis-Albert, C., Rubio, F. and Valero, F., “Optimization approaches for robot trajectory planning,” Multi. J. Educ. Social Technol. Sci. 5(1), 1–16 (2018). doi:10.4995/muse.2018.9867.CrossRefGoogle Scholar
Simba, K. R., Uchiyama, N. and Sano, S., “Real-time smooth trajectory generation for nonholonomic mobile robots using Bézier curves,” Rob. Comput. Integr. Manuf. 41, 31–42 (2016).CrossRefGoogle Scholar
Rubio, F., Valero, F., Suner, J. L. and Garrido, A., “The simultaneous algorithm and the best interpolation function for trajectory planning,” Ind. Rob. 37(5), 441–451 (2010).CrossRefGoogle Scholar
Valero, F., Rubio, F., Llopis-Albert, C. and Cuadrado, J. I., “Influence of the friction coefficient on the trajectory performance for a car-like robot,” Math. Prob. Eng. 2017, 9 (2017) Article ID 4562647. doi:10.1155/2017/4562647.CrossRefGoogle Scholar
Suñer, J. L., Valero, F., Ródenas, J. J. and Besa, A., “Comparación Entre Procedimientos de Solución de La Interpolación Por Funciones Splines Para La Planificación de Trayectorias de Robots Industriales,” 8th Iberoamerican Congress of Mechanical Engineering, Cusco, Perú (2007, 23–35 October).Google Scholar