Hostname: page-component-586b7cd67f-rcrh6 Total loading time: 0 Render date: 2024-11-22T02:11:59.728Z Has data issue: false hasContentIssue false
  • English
  • Français

Time-variant artificial potential field (TAPF): a breakthrough in power-optimized motion planning of autonomous space mobile robots

Published online by Cambridge University Press:  15 August 2014

Matin Macktoobian  and
Mahdi Aliyari Shoorehdeli
Matin Macktoobian*
Affiliation:
Fault Detection and Identification Lab (FDI), Electrical and Computer Engineering Faculty, K. N. Toosi University of Technology, Tehran, Iran
Mahdi Aliyari Shoorehdeli
Affiliation:
Fault Detection and Identification Lab (FDI), Electrical and Computer Engineering Faculty, K. N. Toosi University of Technology, Tehran, Iran
*
*Corresponding author. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Summary

In this paper, a novel scheme is presented to conquer the motion-planning problem for autonomous space robots. Minimizing the consumed energy of atomic batteries within the daily planetary missions of robot on the planet is taken into account, i.e., utilization of the generated solar power by its embedded photocells leads to saving energy of batteries for night missions. Aforementioned objective could be acquired by appropriate interaction of motion planning paradigm with shadows of obstacles. Modeling of the shadow with the proposed artificial potential field leads to generalize the concept of potential fields not only for static and dynamic obstacles but also for being confronted with the intrinsic time-variant phenomena such as shadows. With due attention to the noticeable computational complexity of the introduced strategy, fuzzy techniques are applied to optimize the sampling times effectively. To accomplish this objective, a smart control scheme based on the fuzzy logic is mounted to the primitive version of algorithm. Regarding the need to identify some structural parameters of obstacles, PIONEER™ mobile robot is designed as a test bed for the verification of simulated results. Investigation on empirical accomplishments shows that the goal-oriented definition of Time–Variant Artificial Potential Fields is able to resolve the motion-planning problem in planetary applications.

Keywords

Artificial potential fieldMotion planningMobile robotNavigationSpace Robotics
Type
Articles
Information
Robotica , Volume 34 , Issue 5: RAAD , May 2016 , pp. 1128 - 1150
Copyright
Copyright © Cambridge University Press 2014 

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

1.Koenig, S. and Likhachev, M., “Incremental A*,” Proceedings of the Neural Information Processing Systems (2002) pp. 1539–1546.Google Scholar
2.Stents, A., “Optimal and Efficient Path Planning for Partially Known Environments,” In: Proceedings of the IEEE International Conference on Robotics and Automation (1994) pp. 3310–3317.Google Scholar
3.Koenig, S. and Likhachev, M., “D* Lite,” Proceedings of the National Conference on Artificial Intelligence (AAAI) (2002).Google Scholar
4.Stents, A., “The Focussed D* Algorithm for Real-Time Replanning,” In: Proceedings of the International Joint Conference on Artificial Intelligence (1995) pp. 1625–1659.Google Scholar
5.Carsten, J., Ferguson, D. and Stentz, A., “3D Field D: Improved Path Planning and Replanning in Three Dimensions,” In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (2006) pp. 3381–3386.Google Scholar
6.Dakulovic, M. and Petrovic, I., “Two-way D* for path planning and replacing,” Robot. Auton. Syst. 52, 329–342 (2011).CrossRefGoogle Scholar
7.Khatib, O., “Real-time obstacle avoidance for manipulators and mobile robots,” Int. J. Robot. Res. 5 (1), 90–98 (1986).CrossRefGoogle Scholar
8.Sfeir, J.et al., “An Improved Artificial Potential Field Approach to Real-Time Mobile Robot Path Planning in an Unknown Environment,” In: Proceedings of the IEEE International Symposium on Robotic and Sensors Environments (ROSE), Canada (2011) pp. 208–2013.Google Scholar
9.Ge, S. S. and Cui, Y. J., “Dynamic motion planning for mobile robots using potential field method,” Auton. Robot. 13 (3), 207–222 (2002).CrossRefGoogle Scholar
10.Huang, L., “Velocity planning for a mobile robot to track a moving target – a potential field approach,” Robot. Auton. Syst. 57, 55–63 (2009).CrossRefGoogle Scholar
11.Yagnik, D.et al., “Motion Planning for Multi-Link Robots Using Artificial Potential Fields and Modified Simulated Annealing,” In: Proceedings of the IEEE/ASME International Conference on Mechatronics and Embedded Systems and Applications (MESA), Canada (2010) pp. 421–427.Google Scholar
12.Mekki, H. and Chtourou, M., “Variable Structure Neural Networks for Online Identification of Continuous-Time Dynamical Systems Using Evolutionary Artificial Potential Fields,” In: Proceedings of the 9th International Multi-Conference on Systems, Signals and Devices (SSD), Tunisia (2012) pp. 1–6.Google Scholar
13.Hong, Z.et al., “The Dynamic Path Planning Research for Mobile Robot Based on Artificial Potential Field,” In: Proceedings of the International Conference on Consumer Electronics, Communications and Networks (CECNet) China (2011) pp. 2736–2739.Google Scholar
14.Munasinghe, S. R., Oh, C., Lee, J.-J. and Khatib, O., “Obstacle Avoidance Using Velocity Dipole Field Method,” Proceedings of the International Conference on Control, Automation, and Systems (ICCAS), Korea (2005).Google Scholar
15.Zhang, T., Zhu, Y. and Song, J., “Real-time motion planning for mobile robots by means of artificial potential field method in unknown environment,” Ind. Robot. 37 (4), 384–400 (2010).CrossRefGoogle Scholar
16.Bing, H.et al., “A Route Planning Method Based on Improved Artificial Potential Field Algorithm,” In: Proceedings of the 3rd IEEE International Conference on Communication Software and Networks (ICCSN), China (2011) pp. 550–545.Google Scholar
17.Li, Q.et al., “An Improved Artificial Potential Field Method for Solving Local Minimum Problem,” In: Proceedings of the 2nd International Conference on Intelligent Control and Information Processing (ICICIP), China (2011) pp. 420–424.Google Scholar
18.Vadakkepat, P., Tan, K. C. and Wang, M. L., “Evolutionary Artificial Potential Fields and Their Application in Real Time Robot Path Planning,” In: Proceedings of the Congress on Evolutionary Computation (2000) pp. 256–263.Google Scholar
19.Zhang, M.et al., “Dynamic Artificial Potential Field Based Multi-Robot Formation Control,” In: Proceedings of the IEEE Conference on Instrumentation and Measurement Technology (I2MTC), China (2010) pp.1530–1534.Google Scholar
20.Fiorini, P. and Shiller, Z., “Motion Planning in Dynamic Environments Using the Relative Velocity Paradigm,” In: Proceedings of the IEEE/RSJ International Workshop on Intelligent Robot and Systems (1993) pp. 560–565.Google Scholar
21.Fiorini, P. and Shiller, Z., “Motion planning in dynamic environments using velocity obstacles,” Int. J. Robot. Res. 17 (17), 760–772 (1998).CrossRefGoogle Scholar
22.Guan-chen, L.et al., “Artificial Potential Field-Based Receding Horizon Control for Path Planning,” In: Proceedings of the 24th Chinese Control and Decision Conference (CCDC), China (2012) pp. 3665–3669.Google Scholar
23.Xiang, L.et al., “An Artificial Potential Field Model with Constraints,” In: Proceedings of the 31st Chinese Control Conference (CCC), China (2012), pp. 4680–4683.Google Scholar
24.Rezaee, H. and Abdollahi, F., “Adaptive Artificial Potential Field Approach for Obstacle Avoidance of Unmanned Aircrafts,” In: Proceedings of the IEEE/ASME Internatonal Conference on Advanced Intelligent Mechatronics (AIM), Iran (2012), pp. 1–6.Google Scholar
25.Bentes, C. and Saotome, O., “Dynamic Swarm Formation with Potential Fields and A* Path Planning in 3D Environment,” In: Proceedings of the Robotics Symposium and Latin American Robotics Symposium (SBR-LARS), Brazil (2012), pp. 74–78.Google Scholar
26.Fahimi, F., Autonomous Robots Modeling, Path Planning, and Control (Springer, Berlin, Germany, 2009), pp. 88–92.CrossRefGoogle Scholar
27.Macktoobian, M. and Moosavian, S. A. A., “Time-Variant Artificial Potential Fields: A New Power-Saving Strategy for Navigation of Autonomous Mobile Robots,” In: Proceedings of the 1st RSI/ISM International Conference on Robotics and Mechatronics (ICRoM), Iran (2013), pp. 121–127.Google Scholar
28.Macktoobian, M., “Smart Navigation of Smart Mobile Robots by Time-Variant Artificial Potential Fields,” Proceedings of Iranian Conference on Fuzzy Systems (IFSC), Iran (2013) pp. 1–6.Google Scholar