Urban search and rescue robots are playing an increasingly important role during disasters and with their ability to search within hazardous and dangerous environments to assist the first respond teams. Surveying and remote sensing the hazardous areas are two of the urgent needs of the rescue team to identify the risks before the intervention of the emergency teams. With such time-critical missions, the path planning and autonomous navigation of the robot is one of the primary concerns due to the need of fast and feasible path that is comprehensive enough to assess the associated risks. This paper presents a path planning method for navigating an unmanned ground vehicle within in an indoor hazardous area with minimum priori information. The algorithm can be generalized to any given map and is based on probabilistic roadmap path planning method with spiral dynamics optimization algorithm to obtain the optimal navigating path. Simulations of the algorithm are presented in this paper, and the results promising results are illustrated using Matlab and Simulink simulation environments.
References
[1]
Lermen, Z., Bettinger, E., Drinks, D., Carter, C., Culp, T., Martinez, D. and Stafford, M. (2016) Search and Rescue Robot. Florida institute of Technology, Melbourne.
[2]
Horsch, C., Cuijpers, I.R. and Ham, J. (2012) Urban Search and Rescue Robots. Eindhoven University of Technology, Eindhoven.
[3]
Davids, A. (2002) Urban Search and Rescue Robots: From Tragedy to Technology. IEEE Intelligent Systems, 17, 81-83. https://doi.org/10.1109/MIS.2002.999224
[4]
Saddington, P. and Ramchurn, S.D. (2018) The Multimodal Speech and Visual Gesture (mSVG) Control Model for a Practical Patrol, Search, and Rescue Aerobot. Towards Autonomous Robotic Systems: 19th Annual Conference, TAROS 2018, Bristol, 25-27 July 2018, 423.
[5]
Sampedro, C., Rodriguez-Ramos, A., Bavle, H., Carrio, A., de la Puente, P. and Campoy, P. (2018) A Fully-Autonomous Aerial Robot for Search and Rescue Applications in Indoor Environments using Learning-Based Techniques. Journal of Intelligent & Robotic Systems, 91, 1-27.
[6]
Goldhoorn, A., Garrell, A., Alquézar, R. and Sanfeliu, A. (2018) Searching and Tracking People with Cooperative Mobile Robots. Autonomous Robots, 42, 739-759. https://doi.org/10.1007/s10514-017-9681-6
[7]
Wong, C., Yang, E., Yan, X.T. and Gu, D. (2018) Autonomous Robots for harsh Environments: A Holistic Overview of Current Solutions and Ongoing Challenges. Systems Science & Control Engineering, 6, 213-219. https://doi.org/10.1080/21642583.2018.1477634
[8]
Roy, N., Abedin, M.I., Sanila, S.K., Ahmed, S., Saumik, S.S. and Rahman, M.M. (2018) Robotic Unit Model Proposal for Rescue & Aiding: For Disasters in Bangladesh. 2018 International Conference on Computer, Communication, and Signal Processing (ICCCSP), Chennai, 22-23 February 2018, 1-5.
[9]
Colomina, I. and Molina, P. (2014) Unmanned Aerial Systems for Photogrammetry and Remote Sensing: A Review. ISPRS Journal of Photogrammetry and Remote Sensing, 92, 79-97. https://doi.org/10.1016/j.isprsjprs.2014.02.013
[10]
Rossi, M., Brunelli, D., Adami, A., Lorenzelli, L., Menna, F. and Remondino, F. (2014) Gas-Drone: Portable Gas Sensing System on UAVs for Gas Leakage Localization. SENSORS, 2014 IEEE, Valencia, 2-5 November 2014, 1431-1434. https://doi.org/10.1109/ICSENS.2014.6985282
[11]
Cook, G. (2011) Mobile Robots: Navigation, Control and Remote Sensing. John Wiley & Sons, New York City. https://doi.org/10.1002/9781118026403
[12]
Saha, M. (2006) Motion Planning with Probabilistic Roadmaps. Doctoral Dissertation, Stanford University, Stanford.
[13]
Bekris, K.E., Chen, B.Y., Ladd, A.M., Plaku, E. and Kavraki, L.E. (2003) Multiple Query Probabilistic Roadmap Planners using Single Query Primitives. IEEE, Piscataway.
[14]
Tamura, K. and Yasuda, K. (2011) Primary Study of Spiral Dynamics Inspired Optimization. IEEJ Transactions on Electrical and Electronic Engineering, 6, S98-S100. https://doi.org/10.1002/tee.20628
[15]
Almeshal, A.M., Goher, K.M., Nasir, A.N.K., Tokhi, M.O. and Agouri, S.A. (2013) Fuzzy Logic Optimized Control of a Novel Structure Two-Wheeled Robotic Vehicle using HSDBC, SDA and BFA: A Comparative Study. 2013 18th International Conference on Methods & Models in Automation & Robotics (MMAR), Miedzyzdroje, 26-29 August 2013, 656-661. https://doi.org/10.1109/MMAR.2013.6669988
[16]
Goher, K.M., Almeshal, A.M., Agouri, S.A., Nasir, A.N.K., Tokhi, M.O., Alenezi, M. R., Fadlallah, S.O., et al. (2017) Hybrid Spiral-Dynamic Bacteria-Chemotaxis Algorithm with Application to Control Two-Wheeled Machines. Robotics and Biomimetics, 4, 3. https://doi.org/10.1186/s40638-017-0059-1
