Hostname: page-component-cd9895bd7-gbm5v Total loading time: 0 Render date: 2024-12-24T00:46:13.713Z Has data issue: false hasContentIssue false
  • English
  • Français

Evolutionary Algorithms-Based Multi-Objective Optimal Mobile Robot Trajectory Planning

Published online by Cambridge University Press:  07 March 2019

V. Sathiya Open the ORCID record for V. Sathiya [Opens in a new window]  and
M. Chinnadurai
V. Sathiya*
Affiliation:
Department of Electronics and Communication Engineering, E.G.S. Pillay Engineering College, Nagapattinam, Tamil Nadu 611002, India
M. Chinnadurai
Affiliation:
Department of Computer Science and Engineering, E.G.S. Pillay Engineering College, Nagapattinam, Tamil Nadu 611002, India E-mail: [email protected]
*
*Corresponding author. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Summary

In this research study, trajectory planning of mobile robot is accomplished using two techniques, namely, a new variant of multi-objective differential evolution (heterogeneous multi-objective differential evolution) and popular elitist non-dominated sorting genetic algorithm (NSGA-II). For this research problem, a wheeled mobile robot with differential drive is considered. A practical, feasible and optimal trajectory between two locations in the presence of obstacles is determined through the proposed algorithms. A safer path is obtained by optimizing certain objectives (travel time and actuators effort) taking into account the limitations of mobile robot’s geometric, kinematic and dynamic parameters. Robot motion is represented by a cubic NURBS trajectory curve. The capability of the proposed optimization techniques is analyzed through numerical simulations. Results ensure that the proposed techniques are more desirable for this problem.

Keywords

Wheeled mobile robotDifferential driveOptimal trajectoryHMODENSGA-IINURBS
Type
Articles
Information
Robotica , Volume 37 , Issue 8 , August 2019 , pp. 1363 - 1382
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

Ahmadi, S. M., Kebriaei, H. and Moradi, H., “Constrained coverage path planning: Evolutionary and classical approaches,” Robotica 36(6), 904–924 (2018).CrossRefGoogle Scholar
Paliwal, S. S. and Kala, R., “Maximum clearance rapid motion planning algorithm,” Robotica 36(6), 882–903 (2018).CrossRefGoogle Scholar
Kröger, T. and Wahl, F. M., “Online trajectory generation: Basic concepts for instantaneous reactions to unforeseen events,” IEEE Trans. Rob. 26(1), 94–111 (2010).CrossRefGoogle Scholar
Canali, F., Guarino Lo Bianco, C. and Locatelli, M., “Minimum jerk online planning by a mathematical programming approach,” Eng. Optim. 46(6), 763–783 (2014).CrossRefGoogle Scholar
Kim, Y. and Kim, B. K., “Time optimal trajectory planning based on dynamics for differential-wheeled mobile robots with a geometric corridor,” IEEE Trans. Ind. Electron. 64(7), 5502–5512 (2017).CrossRefGoogle Scholar
Walambe, R., Agarwal, N., Kale, S. and Joshi, V., “Optimal trajectory generation for car-type mobile robot using spline interpolation,” IFAC-Papers On Line 49(1), 601–606 (2016).CrossRefGoogle Scholar
Kim, C. H. and Kim, B. K., “Minimum-energy translational trajectory generation for differential-driven wheeled mobile robots,” J. Intell. Rob. Syst. 49, 367–383 (2007).CrossRefGoogle Scholar
Sgorbissa, A. and Zaccaria, R., “Planning and obstacle avoidance in mobile robotics,” Rob. Auton. Syst. 60, 628–638 (2012).CrossRefGoogle Scholar
Li, B. and Shao, Z., “Simultaneous dynamic optimization: A trajectory planning method for nonholonomic car-like robots,” Adv. Eng. Software 87, 30–42 (2015).CrossRefGoogle Scholar
Liu, S. and Sun, D., “Minimizing energy consumption of wheeled mobile robots via optimal motion planning,” IEEE ASME Trans. Mech. 19(2), 401–411 (2014).CrossRefGoogle Scholar
Han, S., Choi, B. S. and Lee, J. M., “A precise curved motion planning for a differential driving mobile robot,” Mechatronics 18, 486–494 (2008).CrossRefGoogle Scholar
Korayem, M. H., Nazemizadeh, M. and Azimirad, V., “Optimal trajectory planning of wheeled mobile manipulators in cluttered environments using potential functions,” Sci. Iran B 18(5), 1138–1147 (2011).CrossRefGoogle Scholar
Boryga, M., Graboś, A., Kołodziej, P., Gołacki, K. and Stropek, Z., “Trajectory planning with obstacles on the example of tomato harvest,” Agric. Agric. Sci. Procedia 7, 27–34 (2015).Google Scholar
Korayem, M. H., Shafei, A. M. and Shafei, H. R., “Dynamic modeling of nonholonomic wheeled mobile manipulators with elastic joints using recursive Gibbs–Appell formulation,” Sci. Iran B 19(4), 1092–1104 (2012).CrossRefGoogle Scholar
Korayem, M. H., Rahimi, H. N. and Nikoobin, A., “Mathematical modeling and trajectory planning of mobile manipulators with flexible links and joints,” Appl. Math. Model. 36, 3229–3244 (2012).CrossRefGoogle Scholar
Mirzaeinejad, H. and Shafei, A. M., “Modeling and trajectory tracking control of a two-wheeled mobile robot: Gibbs–Appell and prediction-based approaches,” Robotica 36(10), 1551–1570 (2018).CrossRefGoogle Scholar
Wang, B., Li, S., Guo, J. and Chen, Q., “Car-like mobile robot path planning in rough terrain using multi-objective particle swarm optimization algorithm,” Neurocomputing 282(22), 42–51 (2018).CrossRefGoogle Scholar
Plaku, E., Plaku, E. and Simari, P., “Clearance-driven motion planning for mobile robots with differential constraints,” Robotica 36(7), 971–993 (2018).CrossRefGoogle Scholar
Saravanan, R., Ramabalan, S. and Balamurugan, C., “Multiobjective trajectory planner for industrial robots with payload constraints,” Robotica 26(6), 753–765 (2008).CrossRefGoogle Scholar
Xue, Y. and Sun, J.-Q., “Solving the path planning problem in mobile robotics with the multi-objective evolutionary algorithm,” Appl. Sci. 8(9), 1425 (2018).CrossRefGoogle Scholar
Radhakrishna Prabhu, S. G., Seals, R. C., Kyberd, P. J. and Wetherall, J. C., “A survey on evolutionary-aided design in robotics,” Robotica 36(12), 1804–1821 (2018).CrossRefGoogle Scholar
Boudjellel, M. E. A. and Chettibi, T., “Optimal Trajectory Planning for a Mobile Robot in Presence of Obstacles Using Multi-objective Optimization Techniques,” 8th International Conference on Modelling, Identification and Control (ICMIC-2016), Algiers, Algeria (2016), pp. 509–514.CrossRefGoogle Scholar
Deb, K., Pratap, A., Agarwal, S. and Meyarivan, T., “A fast and elitist multiobjective genetic algorithm: NSGA-II,” IEEE Trans. Evol. Comput. 6(2), 182–197 (2002).CrossRefGoogle Scholar
Babu, B. V. and Anbarasu, B., Multi-Objective Differential Evolution (MODE): An Evolutionary Algorithm for Multi-Objective Optimization Problems (MOOPs), http://discovery.bitspilani.ac.in/discipline/chemical/BVb/publications/html, 2005.Google Scholar
Thangavelu, S. and Shunmuga Velayutham, C., “An investigation on mixing heterogeneous differential evolution variants in a distributed framework,” Int. J. Bio. Inspir. Comput. 7(5), 307–320 (2015).CrossRefGoogle Scholar
Patle, B. K., Parhi, D. R. K., Jagadeesh, A. and Kashyap, Sunil Kumar, “Matrix-binary codes based genetic algorithm for path planning of mobile robot,” Comput. Electr. Eng. 67, 708–728 (2018).CrossRefGoogle Scholar