No CrossRef data available.
Article contents
Robot Mission Planning using Co-evolutionary Optimization
Published online by Cambridge University Press: 04 June 2019
Summary
Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the 'Save PDF' action button.
Mission planning is a complex motion planning problem specified by using Temporal Logic constituting of Boolean and temporal operators, typically solved by model verification algorithms with an exponential complexity. The paper proposes co-evolutionary optimization thus building an iterative solution to the problem. The language for mission specification is generic enough to represent everyday missions, while specific enough to design heuristics. The mission is broken into components which cooperate with each other. The experiments confirm that the robot is able to outperform the search, evolutionary and model verification techniques. The results are demonstrated by using a Pioneer LX robot.
Keywords
Information
- Type
- Articles
- Information
- Copyright
- © Cambridge University Press 2019
References
Choset, H., Lynch, K. M., Hutchinson, S., Kantor, G. A., Burgard, W., Kavraki, L. E. and Thrun, S., Principles of Robot Motion: Theory, Algorithms, and Implementations (MIT Press, Cambridge, MA, 2005).Google Scholar
Tiwari, R., Shukla, A. and Kala, R., Intelligent Planning for Mobile Robotics: Algorithmic Approaches (IGI Global Publishers, Hershey, PA, 2013).CrossRefGoogle Scholar
Baier, C. and Katoen, J. P., Principles of Model Checking (MIT Press, Cambridge, MA, 2008).Google Scholar
Fisher, M., An Introduction to Practical Formal Methods Using Temporal Logic (Wiley, West Sussex, UK, 2011).CrossRefGoogle Scholar
Potter, M. A. and DeJong, K. A., “A cooperative coevolutionary approach to function optimization,” Proceedings of the Third Conference on Parallel Problem Solving from Nature, Jerusalem, Israel (Springer-Verlag, Berlin, Germany, 1994) pp. 249–257.CrossRefGoogle Scholar
Potter, M. A. and De Jong, K. A., “Cooperative coevolution: An architecture for evolving coadapted subcomponents,” Evolutionary Comput. 8(1), 1–29 (2000).CrossRefGoogle ScholarPubMed
Kress-Gazit, H., Fainekos, G. E. and Pappas, G. J., “Temporal-logic-based reactive mission and motion planning,” IEEE Trans. Robotics 25(6), 1370–1381 (2009).CrossRefGoogle Scholar
Lahijanian, M., Andersson, S. B. and Belta, C., “Temporal logic motion planning and control with probabilistic satisfaction guarantees,” IEEE Trans. Robotics 28(2), 396–409 (2012).CrossRefGoogle Scholar
Svorenova, M., Tumova, J., Barnat, J. and Cerna, I., “Attraction-based receding horizon path planning with temporal logic constraints,” Proceedings of the 2012 IEEE 51st Annual Conference on Decision and Control, Maui, Hawaii (2012) pp. 6749–6754.CrossRefGoogle Scholar
Lyons, D. M., Arkin, R. C., Jiang, S., O’Brien, M., Tang, F. and Tang, P., “Performance verification for robot missions in uncertain environments,” Robotics Autonomous Syst. 98, 89–104 (2017).CrossRefGoogle Scholar
Bhatia, A., Kavraki, L. E. and Vardi, M. Y., “Sampling-based motion planning with temporal goals,” Proceedings of the 2010 IEEE International Conference on Robotics and Automation, Anchorage, AK (2010) pp. 2689–2696.CrossRefGoogle Scholar
Bhatia, A., Kavraki, L. E. and Vardi, M. Y., “Motion planning with hybrid dynamics and temporal goals,” Proceedings of the 49th IEEE Conference on Decision and Control, Atlanta, Georgia (2010) pp. 1108–1115.Google Scholar
McMahon, J. and Plaku, E., “Sampling-based tree search with discrete abstractions for motion planning with dynamics and temporal logic,” Proceedings of the 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, Chicago, IL (2014) pp. 3726–3733.CrossRefGoogle Scholar
Jeong, I. B., Ko, W. R., Park, G. M., Kim, D. H., Yoo, Y. H. and Kim, J. H., “Task intelligence of robots: Neural model-based mechanism of thought and online motion planning,” IEEE Trans. Emerging Topics Comput. Intell. 1(1), 41–50 (2017).CrossRefGoogle Scholar
Munoz, P., R-Moreno, M. D. and Barrero, D. F., “Unified framework for path-planning and task-planning for autonomous robots,” Robotics Autonomous Syst. 82, 1–14 (2016).CrossRefGoogle Scholar
ElMaraghy, H. A. and Rondeau, J. M., “Automated planning and programming environments for robots,” Robotica 10(1), 75–82 (1992).CrossRefGoogle Scholar
Wang, M. and Wu, T., “Cooperative co-evolution based distributed path planning of multiple mobile robots,” J. Zhejiang University Science 6A(7), 697–706 (2005).Google Scholar
Dongyong, Y., Jinyin, C., Matsumoto, N. and Yamane, Y., “Multi-robot Path Planning Based on Cooperative Co-evolution and Adaptive CGA,” Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology, Hong Kong (2006) pp. 547–550.Google Scholar
Kala, R., “Multi-Robot Path Planning using Co-Evolutionary Genetic Programming,” Expert Syst. Appl. 39(3), 3817–3831 (2012).CrossRefGoogle Scholar
Zhang, X., Guan, X., Hwang, I. and Cai, K., “A hybrid distributed-centralized conflict resolution approach for multi-aircraft based on cooperative co-evolutionary,” Science China Information Sci. 56(12), 1–16 (2013).Google Scholar
Berenson, D., Kuffner, J. and Choset, H., “An Optimization Approach to Planning for Mobile Manipulation,” Proceedings of the 2008 IEEE International Conference on Robotics and Automation, Pasadena, CA, USA (2008) pp. 1187–1192.CrossRefGoogle Scholar
Curkovic, P. and Jerbic, B., “Dual-arm robot motion planning based on cooperative coevolution,” Emerging Trends in Technological Innovation, IFIP Advances in Information and Communication Technology, vol. 314 (Springer, Berlin, Heidelberg, 2010).Google Scholar
Xidias, E. K. and Azariadis, P. N., “Mission design for a group of autonomous guided vehicles,” Robotics Autonomous Syst. 59(1), 34–43 (2011).CrossRefGoogle Scholar
Xidias, E., Zacharia, P. and Nearchou, A., “Path planning and scheduling for a fleet of autonomous vehicles,” Robotica 34(10), 2257–2273 (2016).CrossRefGoogle Scholar
Kala, R., “Coordination in navigation of multiple mobile robots,” Cybernetics Syst. 45(1), 1–24 (2014).CrossRefGoogle Scholar
Xiao, J., Michalewicz, Z., Zhang, L. and Trojanowski, K., “Adaptive evolutionary planner/navigator for mobile robots,” IEEE Trans. Evolutionary Comput. 1(1), 18–28 (1997).CrossRefGoogle Scholar
Shirazi, A. R. and Jin, Y., “A strategy for self-organized coordinated motion of a swarm of minimalist robots,” IEEE Trans. Emerging Topics Comput. Intell. 1(5), 326–338 (2017).CrossRefGoogle Scholar
Kala, R., “Sampling based Mission Planning for Multiple Robots,” Proceedings of the IEEE Congress on Evolutionary Computation, Vancouver, BC, IEEE (2016) pp. 662–669.Google Scholar
Cefalo, M. and Oriolo, G., “A general framework for task-constrained motion planning with moving obstacles,” Robotica 37(3), 575–598 (2018). doi: 10.1017/S0263574718001182.CrossRefGoogle Scholar
Waheed, I. and Fotouhi, R., “Trajectory and temporal planning of a wheeled mobile robot on an uneven surface,” Robotica 27(4), 481–498 (2009).CrossRefGoogle Scholar
Tsiogkas, N. and Lane, D. M., “An evolutionary algorithm for online, resource-constrained, multivehicle sensing mission planning,” IEEE Robotics Automation Lett. 3(2), 1199–1206 (2018).CrossRefGoogle Scholar
Lamont, G. B., Slear, J. N. and Melendez, K., “UAV Swarm Mission Planning and Routing using Multi-Objective Evolutionary Algorithms,” Proceedings of the 2007 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making, Honolulu, HI (2007) pp. 10–20.CrossRefGoogle Scholar
Geng, L., Zhang, Y. F., Wang, J. J., Fuh, J. Y. H. and Teo, S. H., “Mission planning of autonomous UAVs for urban surveillance with evolutionary algorithms,” Proceedings of the 2013 10th IEEE International Conference on Control and Automation (ICCA), Hangzhou (2013) pp. 828–833.CrossRefGoogle Scholar
Yu, L. and Cai, Z., “Robot Exploration Mission Planning Based on Heterogeneous Interactive Cultural Hybrid Algorithm,” Proceedings of the 2009 Fifth International Conference on Natural Computation, Tianjin (2009) pp. 583–587.CrossRefGoogle Scholar
Baalbaki, H., Xie, X. and Delorme, X., “Mission assignment and scheduling for a team of service robots using evolutionary algorithms,” Proceedings of the 2010 IEEE Workshop on Health Care Management, Venice (2010) pp. 1–6.Google Scholar
Baker, J. E., “Reducing bias and inefficiency in the selection algorithm,” Proceedings of the Second International Conference on Genetic algorithms and their Application, Cambridge, MA (1987) pp. 14–21.Google Scholar
Baker, J. E., “Adaptive selection methods for genetic algorithms,” Proceedings of the International Conference on Genetic Algorithms and their Applications, Pittsburg, PA (1985) pp. 101–111.Google Scholar
Grefenstette, J. J. and Baker, J. E., “How genetic algorithms work: A critical look at implicit parallelism,” Proceedings of the Third International Conference on Genetic Algorithms, Morgan Kaufmann Publishers, San Mateo, CA (1989) pp. 20–27.Google Scholar
Spears, W. and de Jong, K., “On the virtues of parameterized uniform crossover,” Proceedings of the Fourth International Conference on Genetic Algorithms, Morgan Kaufman, San Mateo, CA, (1991) pp. 230–236.Google Scholar
Kavraki, L. E., Svestka, P., Latombe, J. C. and Overmars, M. H., “Probabilistic roadmaps for path planning in high-dimensional configuration spaces,” IEEE Trans. Robotics Automation 12(4), 566–580 (1996).CrossRefGoogle Scholar
Kala, R., “Homotopic roadmap generation for robot motion planning,” J. Intell. Robotic Syst. 82(3–4), 555–575 (2016).CrossRefGoogle Scholar
Kala, R., Video Results for the paper Robot Mission Planning using Co-evolutionary Optimization (2019). Available at: https://youtu.be/DHa-22PjEVA.Google Scholar
Kala, R., Video Results for the paper Robot Mission Planning using Co-evolutionary Optimization (2019). Available at: https://youtu.be/931JVTud_Mg.Google Scholar
Cimatti, A., Clarke, E. M., Giunchiglia, F. and Roveri, M., “NUSMV: A New Symbolic Model Verifier,” Proceeding of the 11th International Conference on Computer Aided Verification (Springer-Verlag, London, UK, 1999) pp. 495–499.CrossRefGoogle Scholar
You have
Access
- 10
- Cited by