Skip to main content

Advertisement

Log in

FPGA Implementation of Genetic Algorithm for UAV Real-Time Path Planning

  • Published:
Journal of Intelligent and Robotic Systems Aims and scope Submit manuscript

Abstract

The main objective of an Unmanned-Aerial-Vehicle (UAV) is to provide an operator with services from its payload. Currently, to get these UAV services, one extra human operator is required to navigate the UAV. Many techniques have been investigated to increase UAV navigation autonomy at the Path Planning level. The most challenging aspect of this task is the re-planning requirement, which comes from the fact that UAVs are called upon to fly in unknown environments. One technique that out performs the others in path planning is the Genetic Algorithm (GA) method because of its capacity to explore the solution space while preserving the best solutions already found. However, because the GA tends to be slow due to its iterative process that involves many candidate solutions, the approach has not been actively pursued for real time systems. This paper presents the research that we have done to improve the GA computation time in order to obtain a path planning generator that can recompile a path in real-time, as unforeseen events are met by the UAV. The paper details how we achieved parallelism with a Field Programmable Gate Array (FPGA) implementation of the GA. Our FPGA implementation not only results in an excellent autonomous path planner, but it also provides the design foundations of a hardware chip that could be added to an UAV platform.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Orgen, P., Winstrand, M.: Combining path planning and target assignment to minimize risk in a SEAD mission. In: AIAA Guidance, Navigation, and Control Conf., pp. 566–572. San Francisco, USA (2005)

    Google Scholar 

  2. Ye, Y.-Y., Min, C.-P., Shen, L.-C., Chang, W.-S.: VORONOI diagram based spatial mission planning for UAVs. Journal of System Simulation, China 17(6), 1353–1355, 1359 (2005)

    Google Scholar 

  3. Yu, Z., Zhou, R., Chen, Z.: A mission planning algorithm for autonomous control system of unmanned air vehicle. In: 5th International Symposium on Instrumentation and Control Technology, pp. 572–576. Beijing, China (2003)

  4. Vaidyanathan, R., Hocaoglu, C., Prince, T.S., Quinn, R.D.: Evolutionary path planning for autonomous air vehicles using multiresolution path representation. In: IEEE International Conf. of Intelligent Robots and Systems, vol. 1, pp. 69–76. Maui, USA (2001)

  5. Rubio, J.C., Vagners, J., Rysdyk, R.: Adaptive path planning for autonomous UAV oceanic search missions. AIAA 1st Intelligent Systems Technical Conf., vol. 1, pp. 131–140. Chicago, USA (2004)

  6. Shim, D.H., Chung, H., Kim, H.J., Sastry, S.: Autonomous exploration in unknown environments for unmanned aerial vehicles. In: AIAA Guidance, Navigation, and Control Conf., vol. 8, pp. 6381–6388. San Francisco, USA (2005)

  7. Chaudhry, A., Misovec, K., D’Andrea, R.: Low observability path planning for an unmanned air vehicle using mixed integer linear programming. In: 43rd IEEE Conf. on Decision and Control. Paradise Island, USA (2004)

  8. Theunissen, E., Bolderheij, F., Koeners, G.J.M.: Integration of threat information into the route (re-) planning task. In: 24th Digital Avionics Systems Conf., vol. 2, pp. 14. Washington, DC, USA (2005)

  9. Rathinam, S., Sengupta, R.: Safe UAV navigation with sensor processing delays in an unknown environment. In: 43rd IEEE Conf. on Decision and Control, vol. 1, pp. 1081–1086. Nassau, USA (2004)

  10. AIAA: Collection of technical papers. In: AIAA 3rd “Unmanned-Unlimited” Technical Conf., Workshop, and Exhibit, vol. 1, p. 586. Chicago, USA (2004)

  11. Boskovic, J.D., Prasanth, R., Mehra, R.K.: A multi-layer autonomous intelligent control architecture for unmanned aerial vehicles. J. Aerosp. Comput. Inf. Commun., pp. 605–628. Woburn, USA (2004)

  12. Zheng, C., Ding, M., Zhou, C.: Real-time route planning for unmanned air vehicle with an evolutionary algorithm. Int. J. Pattern Recogn. Artif. Intell. Wuhan, China 17(1), 63–81 (2003)

    Article  Google Scholar 

  13. Boskovic, J.D., Prasanth, R., Mehra, R.K.: A multi-layer control architecture for unmanned aerial vehicles. In: American Control Conference, vol. 3, pp. 1825–1830. Anchorage, USA (2002)

  14. Rathbun, D., Kragelund, S., Pongpunwattana, A., Capozzi, B.: An evolution based path planning algorithm for autonomous motion of a UAV through uncertain environments. In: AIAA/IEEE Digital Avionics Systems Conf., vol. 2, pp. 8D21–8D212. Irvine, USA (2002)

  15. Shiller, I., Draper, J.S.: Mission adaptable autonomous vehicles. Neural Netw. Ocean Eng., pp. 143–150. Washignton DC, USA (1991)

  16. Fletcher, B.: Autonomous vehicles and the net-centric battlespace. In: International Unmanned Undersea Vehicle Symposium, San Diego, USA (2000)

  17. Ferguson, D., Likhachev, M., Stentz, A.: A guide to heuristic-based path planning. In: International Conf. on Automated Planning & Scheduling (ICAPS), vol. 6, pp. 9–18. Monterey, USA, Work Shop (2005)

  18. McKeever, S.D.: Path Planning for an Autonomous Vehicle. Massachusetts Institute of Technology, Massachusetts, USA (2000)

  19. Judd, K.B.: Trajectory Planning Strategies for Unmanned Air Vehicles. Dept. of Mech. Eng., Brigham Young Univ., Provo, USA (2001)

    Google Scholar 

  20. Chanthery, E.: Planification de Mission pour un Véhicule Aérien Autonome, pp. 15. École Nationale Supérieur de l’Aéronautique et de l’Espace, Toulouse, France (2002)

  21. Sugihara, K., Smith, J.: Genetic algorithms for adaptive planning of path and trajectory of a mobile robot in 2d terrains. IEICE Trans. Inf. Syst. E82-D(1), 309–317 (1999)

    Google Scholar 

  22. Pellazar, M.B.: Vehicle route planning with constraints using genetic algorithms. In: IEEE National Aerospace and Electronics Conf., pp. 111–119 (1998)

  23. Mostafa, H.E., Khadragi, A.I., Hanafi, Y.Y.: Hardware implementation of genetic algorithm on FPGA. In: Proc. of the 21st National Radio Science Conf., pp. 367–375. Cairo, Egypt (2004)

  24. Cocaud, C.: Autonomous Tasks Allocation and Path Generation of UAV’s. Dept. of Mech. Eng., Univ. of Ottawa, Ontario, Canada (2006)

  25. Tachibana, T., Murata, Y., Shibata, N., Yasumoto, K., Ito, M.: A hardware implementation method of multi-objective genetic algorithms. In: IEEE Congress on Evolutionary Computation, pp. 3153–3160. Vancouver, Canada (2006)

  26. Tachibana, T., Murata, Y., Shibata, N., Yasumoto, K., Ito, M.: General architecture for hardware implementation of genetic algorithm. In: 14th Annual IEEE Symposium on Field Programmable Custom Computing Machine, pp. 2–3. Napa, USA (2006)

  27. Tang, W., Yip, L.: Hardware implementation of genetic algorithms using FPGA. In: 47th Midwest Symposium on Circuits and Systems, vol. 1, pp. 549–552. Hiroshima, Japan (2004)

  28. Hamid, M.S., Marshall, S.: FPGA realisation of the genetic algorithm for the design of grey-scale soft morphological filters. In: International Conf. on Visual Information Eng., pp. 141–144. Guildford, UK (2003)

  29. Karnik, G., Reformat, M., Pedrycz, W.: Autonomous genetic machine. In: Canadian Conf. on Elect. and Comp. Eng., vol. 2, pp. 828–833. Winnipeg, Canada (2002)

  30. Skliarova, I., Ferrari, A.B.: FPGA-based implementation of genetic algorithm for the traveling salesman problem and its industrial application. In: 15th International Conf. on Industrial and Eng. Application of Artificial Intelligence and Expert Syst. IEA/AIE, pp. 77–87. Cairn, Australia (2002)

  31. Lei, T., Ming-cheng, Z., Jing-xia, W.: The hardware implementation of a genetic algorithm model with FPGA. In: Proc. 2002 IEEE International Conf. on Field Programmable, pp. 374–377. Hong Kong, China (2002)

  32. Hailim, J., Dongming, J.: Self-adaptation of fuzzy controller optimized by hardware-based GA. In: Proc. of 6th International Conf. on Solid-State and IC Technology, vol. 2, pp. 1147–1150. Shanghai, China (2001)

  33. Aporntewan, C., Chongstitvatana, P.: A hardware implementation of the compact genetic algorithm. In: Proc. of the IEEE Conf. on Evolutionary Computation, vol. 1, pp. 624–625. Soul (2001)

  34. Shackleford, B., Snider, G., Carter, R.J., Okushi, E., Yasuda, M., Seo, K., Yasuura, H.: A high-performance, pipelined, FPGA-based genetic algorithm machine. Genetic Programming and Evolvable Machine 2, 33–60 (2001)

    Article  MATH  Google Scholar 

  35. Scott, S.S., Samal, A., Seth, S.: HGA: a hardware-based genetic algorithm. In: ACM 3rd International Symp. on FPGA, pp. 53–59. Monterey, USA (1995)

  36. Emam, H., Ashour, M.A., Fekry, H., Wahdan, A.M.: Introducing an FPGA based genetic algorithms in the applications of blind signals separation. In: Proc. 3rd IEEE International Workshop on Syst.-on-Chip for Real-Time Appl., pp. 123–127. Calgary, Canada (2003)

  37. Thiébaut, D.: Sorting algorithms. Dept. of Comp. Science, Smith College, Northampton, MA, USA (1997) Available: http://maven.smith.edu/~thiebaut/java/sort/demo.html (1997). Accessed 23 April 2008

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to François C. J. Allaire.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Allaire, F.C.J., Tarbouchi, M., Labonté, G. et al. FPGA Implementation of Genetic Algorithm for UAV Real-Time Path Planning. J Intell Robot Syst 54, 495–510 (2009). https://doi.org/10.1007/s10846-008-9276-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10846-008-9276-8

Keywords

Navigation