Concealing Robots in Environments: Enhancing Navigation and Privacy through Stealth Integration
DOI:
https://doi.org/10.4108/airo.3547Keywords:
Swarm robotics, Robot navigation, Maximum hidden set, Path planning, Human-robot interaction, Stealth technologyAbstract
With the continuous advancement of robotics technology, the integration of robots into diverse human environments has become increasingly prevalent. However, the presence of robots in public spaces can often elicit discomfort or unease among individuals. To address this concern, the concept of concealing robots in various settings has emerged as an innovative approach to improve robot navigation and interaction while minimizing intrusion on human privacy. This paper explores the motivations, challenges, and potential benefits of hiding robots in different environments, particularly within the context of swarm robotics where multiple interconnected robots form a cohesive swarm. Equipped with onboard processing, communication, and sensing capabilities, these robots can autonomously interact with each other and adapt to the environment.
The paper investigates the problem of maximizing the number of hidden orthogonal swarm robots, considering scenarios in which robots need to navigate and operate within polygonal environments. Specifically, it presents a 4-approximation algorithm for computing a maximum hidden robot set in such environments. The algorithm offers a practical solution for determining an efficient arrangement of robots that minimizes their visibility while ensuring effective swarm operation.
By concealing robots in diverse environments, several benefits can be achieved. First, it helps to alleviate discomfort or unease among individuals, allowing for smoother integration of robots into public spaces. Additionally, concealing robots enhances their navigation capabilities by leveraging stealth techniques, allowing them to move seamlessly and unobtrusively within the environment. This approach also promotes improved human-robot interaction, as the reduced visibility of the robots can alleviate concerns and foster a more natural and comfortable interaction between humans and robots. The paper sheds light on the current state of the field, discussing the motivations behind concealing robots in different settings and highlighting the challenges that need to be addressed. Furthermore, it presents insights into future directions, including the development of more advanced stealth technologies, ethical frameworks for integrating hidden robots, and the potential impact on urban planning and infrastructure. In conclusion, hiding robots in diverse environments offers a promising approach to enhancing robot navigation, interaction, and privacy. The research presented in this paper contributes to the understanding of this emerging field and provides a foundation for further exploration and development of hiding strategies for swarm robotics in various settings.
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Hoorfar, H., Fathi, F., Moshtaghi Largani, S., Bagheri, A. Securing Pathways with Orthogonal Robots. The 21st International Conference on Scientific Computing, Las Vegas, USA, 2023.
Moshayedi, A. J., Khan, A. S., Shuxin, Y., Kuan, G., Jiandong, H., Soleimani, M., Razi, A. E-Nose design and structures from statistical analysis to application in robotic: a compressive review. EAI Endorsed Transactions on AI and Robotics, 2023.
KhazeniFard, A., Bahrami, F., Andani, M. E., Ahmad-abadi, M. N. An energy efficient gait trajectory planning algorithm for a seven linked biped robot using movement elements. In 2015 23rd Iranian Conference on Electrical Engineering, Tehran, Iran, 2015.
Moshayedi, A. J., Roy, A. S., Sambo, S. K., Zhong, Y., Liao, L. Review on: The service robot mathematical model. EAI Endorsed Transactions on AI and Robotics, 2022.
Shahparvari, S., Hassanizadeh, B., Mohammadi, A., Kiani, B., Lau, K. H., Chhetri, P., Abbasi, B. A decision support system for prioritized COVID-19 two-dosage vaccination allocation and distribution. Transportation Research Part E: Logistics and Transportation Review, 2022.
Maydanchi, M., Ziaei, A., Basiri, M., Azad, A. N., Pouya, S., Ziaei, M., Haji, F., Sargolzaei, S. Comparative Study of Decision Tree, AdaBoost, Random Forest, Naïve Bayes, KNN, and Perceptron for Heart Disease Prediction. In SoutheastCon 2023, Tampa, FL, USA, April 2023.
Moshayedi, A. J., Hosseinzadeh, M., Joshi, B. P., Emadi Andani, M. Recognition System for Ergonomic Mattress and Pillow: Design and Fabrication. IETE Journal of Research, 2023.
Toragay, O., Silva, D. F. Fast heuristic approach for control of complex authentication systems. Applied Stochastic Models in Business and Industry, 2021.
Sadeghi M, Andani ME, Parnianpour M, Fattah A. A bio-inspired modular hierarchical structure to plan the sit-to-stand transfer under varying environmental conditions. Neurocomputing. 2013 Oct 22;118:311-21.
Babajamali Z, Aghadavoudi F, Farhatnia F, Eftekhari SA, Toghraie D. Pareto multi-objective optimization of tandem cold rolling settings for reductions and inter stand tensions using NSGA-II. ISA transactions. 2022 Nov 1;130:399-408.
Daniali OA, Toghraie D, Eftekhari SA. Thermo-hydraulic and economic optimization of Iranol refinery oil heat exchanger with Copper oxide nanoparticles using MOMBO. Physica A: Statistical Mechanics and its Applications. 2020 Feb 15;540:123010.
Azimi M, Kolahdooz A, Eftekhari SA. An optimization on the DIN1. 2080 alloy in the electrical discharge machining process using ANN and GA. Journal of Modern Processes in Manufacturing and Production. 2017 Feb 1;6(1):33-47.
Hoorfar, H., Moshtaghi Largani, S., Rahimi, R., Bagheri, A. Minimizing Turns in Watchman Robot Navigation: Strategies and Solutions. The 21st International Conference on Scientific Computing, Las Vegas, USA, 2023.
Moshayedi, A. J., Reza, K. S., Khan, A. S., Nawaz, A. Integrating Virtual Reality and Robotic Operation System (ROS) for AGV Navigation. EAI Endorsed Transactions on AI and Robotics, 2023.
Moshayedi, A. J., Roy, A. S., Taravet, A., Liao, L., Wu, J., Gheisari, M. A secure traffic police remote sensing approach via a deep learning-based low-altitude vehicle speed detector through UAVs in smart cities: Algorithm, implementation and evaluation. Future Transportation, 2023.
Liu, J., Prabuwono, A. S., Abulfaraj, A. W., Miniaoui, S., Taheri, N. Cognitive cloud framework for waste dumping analysis using deep learning vision computing in a healthy environment. Computers and Electrical Engineering, 2023.
Pourghorban A, Dorothy M, Shishika D, Von Moll A, Maity D. Target Defense against Sequentially Arriving Intruders. In2022 IEEE 61st Conference on Decision and Control (CDC) 2022 Dec 6 (pp. 6594-6601). IEEE.
Pourghorban A, Maity D. Target defense against period-ically arriving intruders. arXiv preprint arXiv:2303.05577. 2023 Mar 9.
Hoorfar H, Bagheri A. Minimum hidden guarding of histogram polygons. arXiv preprint arXiv:1708.05815. 2017 Aug 19.
Hoorfar H, Bagheri A. NP-completeness of chromatic orthogonal art gallery problem. The Journal of Supercom-puting. 2021 Mar;77(3):3077-109.
Hoorfar H, Mohades A. Special guards in chromatic art gallery. 31st European Conference on Computational Geometry, 2015. 16
Hoorfar H, Bagheri A. Guarding Path Polygons with Orthogonal Visibility. arXiv preprint arXiv:1709.01569. 2017 Sep 5.
Hoorfar H, Bagheri A. A linear-time algorithm for orthogonal watchman route problem with minimum bends. arXiv preprint arXiv:1708.01461. 2017 Aug 4.
Hoorfar H, Bagheri A. A New Optimal Algorithm for Computing the Visibility Area of a simple Polygon from a Viewpoint. arXiv preprint arXiv:1803.10184. 2018 Mar 27.
Ghosh, S. K.. Visibility algorithms in the plane. Cambridge university press; 2007.
Kahn, J., Klawe, M., Kleitman, D.. Traditional galleries require fewer watchmen. SIAM J Algebraic Discrete Methods. 1983;4(2):194-206.
Sack, J. R., Toussaint, G. T.. Guard placement in rectilinear polygons. In: Machine Intelligence and Pattern Recognition. Vol. 6. North-Holland; 1988. pp. 153-175.
Lee, D., Lin, A.. Computational complexity of art gallery problems. IEEE Trans Inf Theory. 1986; 32(2):276-282.
Avis, D., Toussaint, G. T.. An efficient algorithm for decomposing a polygon into star-shaped polygons. Pattern Recognit. 1981;13(6):395-398.
Shermer, T.. Hiding people in polygons. Computing. 1989;42(2-3):109-131.
Eidenbenz, S.. Inapproximability of finding maximum hidden sets on polygons and terrains. Comput Geom. 2002;21(3):139-153.
Hoorfar H, Taheri N, Kosarirad H, Bagheri A. Efficiently Guiding K-Robots Along Pathways with Minimal Turns. EAI Endorsed Transactions on AI and Robotics. 2023 Jul 12;2.
Hoorfar, H., Bagheri, A. Geometric Embedding of Path and Cycle Graphs in Pseudo-convex Polygons. arXiv preprint arXiv:1708.01457, 2017.
Mahmudi, F., Soleimani, M., Naderi, M. H. Some Properties of the Maximal Graph of a Commutative Ring. Southeast Asian Bulletin of Mathematics, 2019.
Soleimani M, Naderi MH, Ashrafi AR. Tensor Product of the Power Graphs of Some Finite Rings. Facta Universitatis, Series: Mathematics and Informatics. 2019 Mar 13:101-22.
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Copyright (c) 2023 Hamid Hoorfar, Houman Kosarirad, Nedasadat Taheri, Faraneh Fathi, Alireza Bagheri
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