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Sensor-based, time-critical mobility of autonomous robots in cluttered spaces: a harmonic potential approach

Published online by Cambridge University Press:  01 June 2018

Ahmad A. Masoud*
Affiliation:
Electrical Engineering Department, King Fahad University of Petroleum & Minerals, Dhahran, Saudi Arabia. E-mail: [email protected]
Ali Al-Shaikhi
Affiliation:
Electrical Engineering Department, King Fahad University of Petroleum & Minerals, Dhahran, Saudi Arabia. E-mail: [email protected]
*
*Corresponding author. E-mail: [email protected]

Summary

This paper suggests an integrated navigation system for an unmanned ground vehicle operating in an unknown cluttered environment. The navigator supports time-critical mobility making it possible for a mobile robot to reach a target from the first attempt without the need for a dedicated exploration and mapping stage. The robot only uses necessary and sufficient egocentric local sensory data collected on its way to the target. The construction of the navigation structure revolves around key properties of the harmonic potential field approach to motion planning. The robot's trajectory is well-behaved and direct-to-the-goal. It contains only the minimum number of detours necessary to accommodate the sensory data and maintain the robot in a safe, goal-oriented state. The navigation structure is developed and its theoretical basis is explained. Extensive experimental validation of its properties and performance is carried-out using the X80 robotic platform.

Type
Articles
Copyright
Copyright © Cambridge University Press 2018 

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References

1. Stormont, D. P. and Allan, V. H., “Managing risk in disaster scenarios with autonomous robots,” Syst., Cybern. Inform. 7 (4), 6671 (2009).Google Scholar
2. Woods, D. D., Tittle, J., Feil, M. and Roesler, A., “Envisioning human-robot coordination in future operations,” IEEE Trans. Syst., Man, Cybern. C: Appl. Rev. 34 (2), 210–218 (May 2004).Google Scholar
3. Murphy, R., Disaster Robotics, (The MIT Press, Cambridge, MA, Feb. 2014), ISBN: 9780262027359.Google Scholar
4. Jones, H. L., Rock, S. M., Burns, D. and Morris, S., “Autonomous Robots in SWAT Applications: Research, Design, and Operations Challenges,” Proceedings of the 2002 Symposium for the Association of Unmanned Vehicle Systems International (AUVSI '02), Orlando, Florida (Jul. 2002).Google Scholar
5. Kulich, M., Stopan, P. and Preucil, L., “Knowledge acquisition for mobile robot environment mapping,” In: Database and Expert Systems Applications, Lecture Notes in Computer Science (NBench-Capon, T., Soda, G., and Tjoa, A. M., eds.), vol. 1677, (1999), pp. 123–134.Google Scholar
6. Castejo, C., Boada, B., Blanco, D. and Moreno, L., “Traversable region modeling for outdoor navigation,” J. Intell. Robot. Syst. 43, 175216 (2005).Google Scholar
7. Wooden, D., “A guide to vision-based map building,” IEEE Robot. Autom. Mag. 13 (2), 94–98 (Jun. 2006).Google Scholar
8. Feng, L., Borenstein, J. and Everett, B., “Where am I? Sensors and Methods for Autonomous Mobile Robot Localization.” Tech. Rep., The University of Michigan UM-MEAM-94-21, (Dec. 1994).Google Scholar
9. Goerzen, C., Kong, Z. and Mettler, B., “A survey of motion planning algorithms from the perspective of autonomous UAV guidance,” J. Intell. Robot. Syst.: Theory Appl. 57 (1–4), 65100 (2010).Google Scholar
10. Rao, N., Kareti, S., Shi, W. and Iyengar, S., “Robot Navigation in Unknown Terrains: Introductory Survey of Non-Heuristic Algorithms,” Tech. Rep., Oak Ridge National Laboratory ORNL/TM-12410, (Jul. 1993).Google Scholar
11. Campion, G., Bastin, G. and D'Andrea-Novel, B., “Structural properties and classification of kinematic and dynamic models of wheeled mobile robots,” IEEE Trans. Robot. Autom. 12 (1), 4762 (1996).Google Scholar
12. Campion, G., 'Andrea-Novel, B. and Bastin, G., “Controllability and state feedback stabilizability of non holonomic mechanical systems,” In: Advanced Robot Control, Lecture Notes in Control and Information Sciences (Canudas de Wit, C., ed.), vol. 162, (1991), pp. 106–124.Google Scholar
13. von Neumann, J., “Probabilistic logics and synthesis of reliable organisms from unreliable components,” In: Automata Studies (Shannon, C. and McCarthy, J., eds.) (Princeton University Press, Princeton, New Jersey, USA, 1956) pp. 4398.Google Scholar
14. Christoforos, H., “Coding Approaches to Fault Tolerance in Combinational and Dynamic Systems,” In: The Springer International Series in Engineering and Computer Science, vol. 660, (Springer, USA, 2002), DOI 10.1007/978-1-4615-0853-3.Google Scholar
15. Rodney, A. Brooks, “Intelligence Without Reason,” Proceeding of the 12th International Joint Conference on Artificial Intelligence (IJCAI '91), vol. 1, (1991) pp. 569–595.Google Scholar
16. Brooks, R. A., “A robust layered control system for a mobile robot,” IEEE J. Robot. Autom. 2 (1), 1423 (Mar. 1986) also MIT AI Memo 864, September 1985.Google Scholar
17. Vamvoudakis, K. G. and Antsaklis, P. J., “Autonomy and Machine Intelligence in Complex Systems: A Tutorial,” Proceedings of the American Control Conference Palmer House Hilton, Chicago, IL, USA (Jul. 1–3, 2015) pp. 5062–5097Google Scholar
18. Kortenkamp, D., Bonasso, R. and Murphy, R., Artificial Intelligence and Mobile Robots, (The AAAI Press/The MIT Press, Menlo Park - California, Cambridge - Massachusetts, London - England, 1998).Google Scholar
19. Lumelsky, V. and Skewis, T., “Incorporating range sensing in the robot navigation function,” IEEE Trans. Syst., Man Cybern. 20 (5), 10581069.Google Scholar
20. Lumelsky, V. J. and Stepanov, A. A., “Path-planning strategies for a point mobile automaton moving amidst unknown obstacles of arbitrary shape,” Algorithmica 2 (1), 403430 (Nov. 1987).Google Scholar
21. Grisetti, G., Kümmerle, R., Stachniss, C. and Burgard, W., “A tutorial on graph-base SLAM,” IEEE Intell. Transportation Syst. IEEE Mag. 2 (4), 3143 (Winter 2010).Google Scholar
22. Censi, A., Nilsson, A. and Murray, R., “Motion Planning in Observations Space with Learned Diffeomorphism Models,” Proceedings of the IEEE International Conference on Robotics and Automation (ICRA '13), Karlsruhe, Germany (May 2013) pp. 2860–2867.Google Scholar
23. Masoud, A. A., “A harmonic potential approach for simultaneous planning and control of a generic UAV platform,” from the issue “special volume on unmanned aircraft systems,” J. Intell. Robot. Syst. 65 (1), 153173 (2012).Google Scholar
24. Thrun, S. and Mitchell, T., “Lifelong Robot Learning,” In: The Biology and Technology of Intelligent Autonomous Agents NATO ASI Series (Steels, L., ed.), vol. 144, (1995), pp. 165–196.Google Scholar
25. Stentz, A. and Hebert, M., “A complete navigation system for goal acquisition in unknown environments,” Autonomous Robots 2 (2), 127145 1995.Google Scholar
26. Latombe, J., Robot Motion Planning (Kluwer, Boston, MA, 1991).Google Scholar
27. Dunias, P., “Autonomous Robots Using Artificial Potential Fields,” Ph.D. Thesis (Technische Universiteit: Eindhoven, 1996), ISBN 90-386-0200-6, DOI http://dx.doi.org/10.6100/IR470384.Google Scholar
28. Hwang, Y. and Ahuja, N., “Gross motion planning,” ACM Comput. Surveys 24 (3), 291291 (Sep. 1992).Google Scholar
29. LaValle, S. M., Planning Algorithms. (Cambridge University Press, Cambridge, New York, Melbourne, Madrid, Cape Town, Singapore, São Paulo, Jul. 2006), ISBN: 9780521862059.Google Scholar
30. Coenen, S. A. M., “Motion Planning for Mobile Robots – A Guide,” M.Sc. Thesis (Eindhoven University of Technology, Department of Mechanical Engineering Control Systems Technology: Eindhoven, Nov. 2012).Google Scholar
31. Farber, Michael, “Topology of Robot Motion Planning,” In: Morse Theoretic Methods in Nonlinear Analysis and in Symplectic Topology (Biran, P., Cornea, O., Lalonde, F., eds.) vol. 217 of the Series NATO Science Series II: Mathematics, Physics and Chemistry pp. 185–230.Google Scholar
32. Schwartz, J. T. and Sharir, M., “A survey of motion planning and related geometric algorithms,” J. Artif. Intell. – Special Issue Geometric Reasoning 37 (1–3), 157169 (Dec. 1988).Google Scholar
33. Masoud, S. A. and Masoud, A. A., “Constrained motion control using vector potential fields,” IEEE Trans. Syst., Man, Cybern.–-A: Syst. Humans 30 (3), 251272 (May 2000).Google Scholar
34. Masoud, A. A., “A Harmonic Potential Field Approach for Planning Motion of a UAV in a Cluttered Environment with a Drift Field,” Proceedings of the 50th IEEE Conference on Decision and Control and European Control Conference, Hilton Orlando Bonnet Creek Hotel Orlando FL, USA (Dec. 12–15, 2011) pp. 7665–7671.Google Scholar
35. Min, B., Cho, D., Lee, S. and Park, Y., “Sonar mapping of a mobile robot considering position uncertainty,” Robot. Comput.-Integrated Manuf. 13 (1), 4149 (Mar. 1997).Google Scholar
36. Sheldon, A., Paul, B. and Ramey, W., “Harmonic Function Theory,” Gradate Texts in Mathematics (Springer, Springer-Verlag, New York, Inc., 2001), ISBN 978-1-4757-8137-3.Google Scholar
37. McClelland, J. L., Rumelhart, D. E. and the PDP Research Group. Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Vol. 2: Psychological and Biological Models (The MIT Press, Cambridge - MA, London - England, 1986).Google Scholar
38. Rumelhart, D. E., McClelland, J. L. and the PDP Research Group Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Vol. 1: Foundations (The MIT Press, Cambridge - MA, London - England, 1986).Google Scholar
39. Miguel-Toméa, S. and Fernández-Caballero, A., “On the identification and establishment of topological spatial relations by autonomous systems,” Connection Science Archive 26 (3), 261292 (Sep. 2014).Google Scholar
40. Keymeulen, D. and Decuyper, J., “A reactive robot navigation system based on a fluid dynamics metaphor,” Artif. Intell. Lab., Vrije Univ. Brussel, Brussels, Belgium, AI MEMO # 90-5, 1990Google Scholar
41. Keymeulen, D. and Decuyper, J., “The Fluid Dynamics Applied to Mobile Robot Motion: The Stream Field Method,” Proceedings of the IEEE International Conference on Robotics and Automation, San Diego, CA (May 8–13) pp. 378–85Google Scholar
42. Louste, C. and Liégeois, A., “Path planning for non-holonomic vehicles: A potential viscous fluid field method,” Robotica 20, 291298 (2002). DOI: 10.1017/S0263574701003691Google Scholar
43. Tarassenko, I. and Blake, A., “Analogue Computation of Collision- Free Paths,” Proceedings of the IEEE International Conference on Robotics and Automation, Sacramento, CA (Apr. 1991) pp. 540–545.Google Scholar
44. Prassler, E., “Electrical Networks and a Connectionist Approach to Pathfinding,” In: Connectionism in Perspective, (Pfeifer, R., Schreter, Z., Fogelman, F. and Steels, L., eds.) (Elsevier, North-Holland, Amsterdam, 1989) pp. 421428.Google Scholar
45. Masoud, A. A., Masoud, S. A. and Bayoumi, M. M., “Robot Navigation Using a Pressure Generated Mechanical Stress Field: “The Biharmonic Potential Approach,” Proceedings of the IEEE International Conference on Robotics and Automation, vol. 1, San Diego, CA, (May 8–13, 1994) pp. 124–129, DOI: 10.1109/ROBOT.1994.351000Google Scholar
46. Connolly, C., Weiss, R. and Burns, J., “Path Planning Using Laplace Equation,” Proceedings of the IEEE International Conference on Robotics and Automation, Cincinnati, OH (May 13–18, 1990) pp. 2102–2106.Google Scholar
47. Masoud, A, A., “Evolutionary Action Maps for Navigating a Robot in an Unknown, Multidimensional, Stationary Environment, Part II: Implementation and Results,” Proceedings of the IEEE International Conference on Robotics and Automation, Albuquerque, New Mexico (Apr. 1997) pp. 2090–2096.Google Scholar
48. Keymeulen, D. and Decuyper, J., “The Stream Field Method Applied to Mobile Robot Navigation: A Topological Perspective,” Proceedings of 11th European Conference on Artificial Intelligence (ECAI '94) (1994), 699–703.Google Scholar
49. Ahmad, A. Masoud, “An Informationally-Open, Organizationally-Closed Control Structure for Navigating a Robot in an Unknown, Stationary Environment,” Proceedings of the IEEE International Symposium on Intelligent Control, Houston, Texas, USA (Oct. 5–8, 2003) pp. 614–619.Google Scholar
50. Masoud, A. A., “Kinodynamic motion planning: A novel type of nonlinear, passive damping forces and advantages,” IEEE Robot. Autom. Mag. 17 (1), 8599 (Mar. 2010).Google Scholar
51. Milnor, J., Morse Theory (Princeton Univ. Press, Princeton, NJ, 1963).Google Scholar
52. Koditschek, D., “Exact Robot Navigation by Means of Potential Functions: Some Topological Considerations,” Proceedings of the IEEE International Conference on Robotics and Automation, Raleigh, NC (Mar. 1987) pp. 1–6.Google Scholar
53. Langton, C., “Artificial Life,” In: Artificial Life SFI Studies in the Science of Complexity, (Langton, C., ed.) (Addison-Wesley, Reading, MA, 1988) pp. 147.Google Scholar
54. Thorn, R., Structural Stability and Morphogenesis (W. A. Benjamin Inc., Advanced Book Program, Reading, Massachusetts, London, Amsterdam, Don Mills, Sydney, Tokyo, 1975).Google Scholar
55. Masoud, S. A. and Masoud, A. A., “Motion planning in the presence of directional and obstacle avoidance constraints using nonlinear anisotropic, harmonic potential fields: A physical metaphor,” IEEE Trans. Syst., Man, Cybern., A: Syst. Humans 32 (6), 705723 (Nov. 2002).Google Scholar
56. Masoud, A. A., “Motion planning with gamma-harmonic potential fields,” IEEE Trans. Aerosp. Electronic Syst. 48 (4), 27862801 (2012).Google Scholar
57. Masoud, A. A., “A Discrete Harmonic Potential Field for Optimum Point-to-Point Routing on a Weighted Graph,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Beijing (Oct. 9–15, 2006) pp. 1779–1784, DOI: 10.1109/IROS.2006.28221.Google Scholar
58. Afzal, W. and Masoud, A. A., “Harmonic Potential-Based Communication-Aware Navigation and Beamforming in Cluttered Spaces with Full Channel-State Information,” IEEE Proceedings of the International Conference on Robotics and Automation (ICRA '17), Singapore (May 29–Jun. 3, 2017) pp. 6198–6203.Google Scholar
59. Sancho-Pradel, D. L. and Saaj, C. M., Senior, “Assessment of Artificial Potential Field Methods for Navigation of Planetary Rovers,” Proceedings of the European Control Conference , Budapest, Hungary (Aug. 23–26, 2009) pp. 3027–3032.Google Scholar
60. Gupta, R. A., Masoud, A. A. and Chow, M.-Y., “A delay-tolerant, potential field-based, network implementation of an integrated navigation system,” IEEE Trans. Ind. Electron. 57 (2), 769783 (Feb. 2010).Google Scholar
61. Masoud, A. A., “A Hybrid, PDE-ODE Control Strategy For Intercepting An Intelligent, Well-Informed Target In A Stationary, Cluttered Environment,” In: Applied Mathematical Sciences (Colantoni, A., ed.), vol. 1, 48 (HIKARI Ltd., 2007) pp. 23452371.Google Scholar
62. Howen, V. P., Finite Difference Method for Solving Partial Differential Equations (Mathematical Centre, Amsterdam, 1968).Google Scholar
63. Zienkiewicz, O. and Morgan, K., Finite Element and Approximation (Wiley, New York, NY, 1983).Google Scholar
64. Brebbia, C., Telles, J. and Worbel, L., Boundary Element Techniques, Theory and Applications in Engineering (Springer-Verlag, Berlin, 1984).Google Scholar
65. Plumer, E., Cascading a systolic and a feedforward neural network for navigation and obstacle avoidance using potential fields, prepared for Ames Research Center, Contract NGT–50 642, NASA Contractor Rep. 177 575, (Feb. 1991).Google Scholar
66. Lei, G., “A neuron model with fluid properties for solving labyrinthian puzzle,” Biolog. Cybern. 64, 6167 (1990).Google Scholar
67. Girau, B. and Boumaza, A., “Embedded harmonic control for dynamic trajectory planning on FPGA,” Proceedings of the International Conference on Artificial Intelligence and Applications (AIA '07) (2007) pp. 244–249.Google Scholar
68. Stan, M., Burleson, W., Connolly, C. and Grupen, R., “Analog VLSI for path planning,” J VLSI Signal Process. 8, 6173 (1994).Google Scholar
69. Althofer, K., Fraser, D. and Bugmann, G., “Rapid path planning for robotic manipulators using an emulated resistive grid,” Electron. Lett. 31 (22), 19601961 (1995).Google Scholar
70. Koziol, S. and Hasler, P., “Reconfigurable analog VLSI circuits for robot path planning,” Proceedings of the NASA/ESA Conference on Adaptive Hardware and systems (AHS '11), San Diego, CA, USA (Jun. 6–9, 2011) San Diego Convention Center, pp. 36–43.Google Scholar
71. Koziol, Scott, Hasler, Paul and Stilman, Mike, “Robot Path Planning Using Field Programmable Analog Arrays,” Proceedings of the IEEE International Conference on Robotics and Automation, River Centre, Saint Paul, MN, USA (May 14–18, 2012) pp. 1747–1752.Google Scholar
72. Pershin, Y. V. and Ventra, M. D., “Solving mazes with memristors: A massively parallel approach,” Phys. Rev. E 84, 046703 – Published 046703-1–046703–6 (Oct. 14, 2011).Google Scholar
73. Vourkas, I. and Ch. Sirakoulis, G., Memristor-Based Nanoelectronic Computing Circuits and Architectures (Springer International Publishing, Switzerland, 2016).Google Scholar
74. Murarka, A., Building Safety Maps Using Vision for Safe Local Mobile Robot Navigation Ph.D.Thesis (Austin: The University of Texas at Austin, Aug. 2009).Google Scholar
75. Connolly, C. I., “Harmonic functions and collision probabilities,” Int. J. Robot. Res. 16 (4), 497507 (Aug. 1997). doi: 10.1177/027836499701600404Google Scholar
76. Ok, Kyel, Ansari, Sameer, Gallagher, Billy, Sica, William, Dellaert, Frank and Stilman, Mike, “Path Planning with Uncertainty: Voronoi Uncertainty Fields,” Proceedings of the IEEE International Conference on Robotics and Automation (ICRA ‘13), Karlsruhe, Germany (May 6–10, 2013) pp. 4596–4601Google Scholar
77. Bergman, S. and Schiffer, M., Kernel Functions and Elliptic Differential Equations in Mathematical Physics (Academic Press Inc., New York, NY, 1953).Google Scholar
78. Kim, C. H. and Kim, B. K., “Minimum-energy translational trajectory generation for differential-driven wheeled mobile robots,” J. Intell. Robot. Syst. 49 (4), 367383 (Aug. 2007).Google Scholar
79. Mei, Y.. Energy-Efficient Mobile Robots. Ph.D. Thesis (Purdue University, May 2007).Google Scholar
80. Masoud, A., “A harmonic potential field approach for joint planning & control of a rigid, seprable, nonholonomic, mobile robot,” Robot. Autonomous Syst. 61 (6), 593615 (Jun. 2013).Google Scholar
82. Iñiguez, P. and Rosell, J., “Efficient Path Planning Using Harmonic Functions Computed on a Non-regular Grid,” In: (Toledo, M. Teresa Escrig Francisco, Golobardes, E., eds.), Proceedings of the 5th Catalonian Conference on Topics in Artificial on Intelligence (AICCIA '02), Spain (Springer, Oct. 24–25, 2002) pp. 345–354.Google Scholar
83. Banta, L., “Advanced Dead-Reckoning Navigation for Mobile Robots,” Ph.D. Thesis (Mechanical Engineering, Georgia Institute of Technology, 1987).Google Scholar
84. Sekimori, D. and Miyazaki, F., “Precise Dead-Reckoning for Mobile Robots Using Multiple Optical Sensors,” Inform. in Control, Autom. and Robot. II, 145151 (2007), Springer.Google Scholar
85. Marder-Eppstein, E., Berger, E., Foote, T., Gerkey, B. and Konolige, K., “The Office Marathon: Robust Navigation in an Indoor Office Environment,” Proceedings of the IEEE International Conference on Robotics and Automation Anchorage Convention District, Anchorage, AK, USA (May 3–8, 2010) pp. 300–307.Google Scholar
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