ABSTRACT
Multi-robotic systems of varying sizes and levels of complexity can effortlessly address real-world distributed tasks like exploration, catastrophic surveillance, logistics, and industrial manufacturing. To perform such tasks efficiently, individual robots should communicate and collaborate with each other to cooperatively map the area of interest. The primary advantage of multi-robot systems over a single stand-alone robot is the enhanced spatial coverage, significantly reducing the exploration time and energy required for unknown terrain mapping. However, a large number of robots can increase the overall cost as well as the complexity of the operation by introducing additional redundancy. Therefore, the objective of this research is to study the functionality of multi-robot systems by investigating map-merging and nearest-robot navigation using a case study of Turtlebot3. In this study, computational simulations are performed in the ROS framework where multiple robots are deployed to generate individual local maps which are further transformed to create a global world map. This global map is used to compute the Euclidean distance subsequently enabling the nearest member goal navigation. Indoor experimental tests are performed to verify the simulation study, demonstrating that map merging and localised nearest member navigation can cut down operational time. The overall operational time was reduced by 23.43 % with the deployment of two robots and 45.85 % with three robots in the simulation environment, hence verifying the reduction in mission completion time.
- Zhang, J., Yang, X., Wang, W., Guan, J., Ding, L., & Lee, V. C. (2023). Automated guided vehicles and autonomous mobile robots for recognition and tracking in civil engineering. Automation in Construction, 146, 104699.Google ScholarCross Ref
- Kuncolienkar, A., Panigrahi, S., & Thondiyath, A. (2022, May). Multibody dynamics framework for performance evaluation of an all-terrain rover. In 2022 IEEE International Conference on Advanced Robotics and Its Social Impacts (ARSO) (pp. 1-8). IEEE.Google ScholarDigital Library
- Panigrahi, S., Maski, P., & Thondiyath, A. (2022, April). Deep learning based real-time biodiversity analysis using aerial vehicles. In Robot Intelligence Technology and Applications 6: Results from the 9th International Conference on Robot Intelligence Technology and Applications (pp. 401-412). Cham: Springer International Publishing.Google Scholar
- Ray, D. N., Das, R., Sebastian, B., Roy, B., & Majumder, S. (2016). Design and analysis towards successful development of a tele-operated mobile robot for underground coal mines. In CAD/CAM, Robotics and Factories of the Future: Proceedings of the 28th International Conference on CARs & FoF 2016 (pp. 589-602). Springer India.Google ScholarCross Ref
- Panigrahi, S., Thondiyath, A., & Rohith, S. K. (2022). Characterisation of the Propulsion System for Submersible Multimedium Robotic Vehicles. IEEE Aerospace and Electronic Systems Magazine, 37(12), 14-32.Google ScholarCross Ref
- Bader, K., Lussier, B., & Schön, W. (2017). A fault tolerant architecture for data fusion: A real application of Kalman filters for mobile robot localisation. Robotics and Autonomous Systems, 88, 11-23.Brambilla, M., Ferrante, E., Birattari, M., & Dorigo, M. (2013). Swarm robotics: a review from the swarm engineering perspective. Swarm Intelligence, 7(1), 1-41.Google ScholarDigital Library
- Anand, A., Nithya, M., & Sudarshan, T. S. B. (2014, November). Coordination of mobile robots with master-slave architecture for a service application. In 2014 International Conference on Contemporary Computing and Informatics (IC3I) (pp. 539-543). IEEE.Google ScholarCross Ref
- Agrawal, N. K., Mahapatra, P. S., & Santra, T. S. (2020). Micro-Robots/Microswimmers for Biomedical Applications. Microfluidics and Bio-MEMS, 95-148.Google ScholarCross Ref
- Mirjalili, S. (2019). Particle swarm optimisation. In Evolutionary Algorithms and Neural Networks (pp. 15-31). Springer, Cham.Google ScholarCross Ref
- Fruggiero, F., Lambiase, A., & Fallon, D. (2008). Computer simulation and swarm intelligence organisation into an emergency department: a balancing approach across ant colony optimisation. International Journal of Services Operations and Informatics, 3(2), 142-161.Google ScholarCross Ref
- Niroui, F., Zhang, K., Kashino, Z., & Nejat, G. (2019). Deep reinforcement learning robot for search and rescue applications: Exploration in unknown cluttered environments. IEEE Robotics and Automation Letters, 4(2), 610-617.Google ScholarCross Ref
- E. Salinas-Avila, J., G. Gonzalez-Hernandez, H., Martinez-Chan, N., M. Bielma-Avendano, C., A. Salinas-Molar, X., & O. Garcia-Garcia, M. (2022, March). Assistant Delivery Robot for Nursing Home using ROS: Robotic prototype for medicine delivery and vital signs registration. In The 2022 5th International Conference on Electronics, Communications and Control Engineering (pp. 141-148).Google ScholarDigital Library
- Liu, Y., Fan, X., & Zhang, H. (2013). A fast map merging algorithm in the field of multirobot SLAM. The Scientific World Journal, 2013.Google ScholarCross Ref
- Ganesan, S., Natarajan, S. K., & Srinivasan, J. (2022). A Global Path Planning Algorithm for Mobile Robot in Cluttered Environments with an Improved Initial Cost Solution and Convergence Rate. Arabian Journal for Science and Engineering, 47(3), 3633-3647.sGoogle ScholarCross Ref
- Williams, C., & Schroeder, A. (2020). Utilising ROS 1 and the Turtlebot3 in a Multi-Robot System. arXiv preprint arXiv:2011.10488.Google Scholar
- Zhang, B., Liu, J., & Chen, H. (2013, August). Amcl based map fusion for multi-robot slam with heterogenous sensors. In 2013 IEEE International Conference on Information and Automation (ICIA) (pp. 822-827). IEEE.Google Scholar
Index Terms
- Swarm-based exploration in unknown environments: A case study of mobile-robots using ROS framework
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