A review of mobile robots: Concepts, methods, theoretical framework, and applications

[EN] Humanoid robots, unmanned rovers, entertainment pets, drones, and so on are great examples of mobile robots. They can be distinguished from other robots by their ability to move autonomously, with enough intelligence to react and make decisions based on the perception they receive from the environment. Mobile robots must have some source of input data, some way of decoding that input, and a way of taking actions (including its own motion) to respond to a changing world. The need to sense and adapt to an unknown environment requires a powerful cognition system. Nowadays, there are mobile robots that can walk, run, jump, and so on like their biological counterparts. Several fields of robotics have arisen, such as wheeled mobile robots, legged robots, flying robots, robot vision, artificial intelligence, and so on, which involve different technological areas such as mechanics, electronics, and computer science. In this article, the world of mobile robots is explored including the new trends. These new trends are led by artificial intelligence, autonomous driving, network communication, cooperative work, nanorobotics, friendly human-robot interfaces, safe human-robot interaction, and emotion expression and perception. Furthermore, these news trends are applied to different fields such as medicine, health care, sports, ergonomics, industry, distribution of goods, and service robotics. These tendencies will keep going their evolution in the coming years.The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Spanish Ministry of Economy and Competitiveness, which has funded the DPI2013-44227-R project.Rubio Montoya, FJ.; Valero Chuliá, FJ.; Llopis Albert, C. (2019). A review of mobile robots: Concepts, methods, theoretical framework, and applications. International Journal of Advanced Robotic Systems. 16(2):1-22. https://doi.org/10.1177/1729881419839596S122162Brunete, A., Ranganath, A., Segovia, S., de Frutos, J. P., Hernando, M., & Gambao, E. (2017). Current trends in reconfigurable modular robots design. International Journal of Advanced Robotic Systems, 14(3), 172988141771045. doi:10.1177/1729881417710457Bajracharya, M., Maimone, M. W., & Helmick, D. (2008). Autonomy for Mars Rovers: Past, Present, and Future. Computer, 41(12), 44-50. doi:10.1109/mc.2008.479Carsten, J., Rankin, A., Ferguson, D., & Stentz, A. (2007). Global Path Planning on Board the Mars Exploration Rovers. 2007 IEEE Aerospace Conference. doi:10.1109/aero.2007.352683Grotzinger, J. P., Crisp, J., Vasavada, A. R., Anderson, R. C., Baker, C. J., Barry, R., … Wiens, R. C. (2012). Mars Science Laboratory Mission and Science Investigation. Space Science Reviews, 170(1-4), 5-56. doi:10.1007/s11214-012-9892-2Khatib, O., Yeh, X., Brantner, G., Soe, B., Kim, B., Ganguly, S., … Creuze, V. (2016). Ocean One: A Robotic Avatar for Oceanic Discovery. IEEE Robotics & Automation Magazine, 23(4), 20-29. doi:10.1109/mra.2016.2613281Ceccarelli, M. (2012). Notes for a History of Grasping Devices. Mechanisms and Machine Science, 3-16. doi:10.1007/978-1-4471-4664-3_1Campion, G., & Chung, W. (2008). Wheeled Robots. Springer Handbook of Robotics, 391-410. doi:10.1007/978-3-540-30301-5_18Ferriere, L., Raucent, B., & Campion, G. (s. f.). Design of omnimobile robot wheels. Proceedings of IEEE International Conference on Robotics and Automation. doi:10.1109/robot.1996.509271Campion, G., Bastin, G., & Dandrea-Novel, B. (1996). Structural properties and classification of kinematic and dynamic models of wheeled mobile robots. IEEE Transactions on Robotics and Automation, 12(1), 47-62. doi:10.1109/70.481750Bałchanowski, J. (2012). Mobile Wheel-Legged Robot: Researching of Suspension Leveling System. Mechanisms and Machine Science, 3-12. doi:10.1007/978-94-007-5125-5_1Williams, R. L., Carter, B. E., Gallina, P., & Rosati, G. (2002). Dynamic model with slip for wheeled omnidirectional robots. IEEE Transactions on Robotics and Automation, 18(3), 285-293. doi:10.1109/tra.2002.1019459Chan, R. P. M., Stol, K. A., & Halkyard, C. R. (2013). Review of modelling and control of two-wheeled robots. Annual Reviews in Control, 37(1), 89-103. doi:10.1016/j.arcontrol.2013.03.004Kim, H., & Kim, B. K. (2014). Online Minimum-Energy Trajectory Planning and Control on a Straight-Line Path for Three-Wheeled Omnidirectional Mobile Robots. IEEE Transactions on Industrial Electronics, 61(9), 4771-4779. doi:10.1109/tie.2013.2293706Carbone, G., & Ceccarelli, M. (2005). Legged Robotic Systems. Cutting Edge Robotics. doi:10.5772/4669Chestnutt, J., Lau, M., Cheung, G., Kuffner, J., Hodgins, J., & Kanade, T. (s. f.). Footstep Planning for the Honda ASIMO Humanoid. Proceedings of the 2005 IEEE International Conference on Robotics and Automation. doi:10.1109/robot.2005.1570188Arikawa, K., & Hirose, S. (s. f.). Development of quadruped walking robot TITAN-VIII. Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems. IROS ’96. doi:10.1109/iros.1996.570670Kurazume, R., Byong-won, A., Ohta, K., & Hasegawa, T. (s. f.). Experimental study on energy efficiency for quadruped walking vehicles. Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453). doi:10.1109/iros.2003.1250697Hirose, S., Fukuda, Y., Yoneda, K., Nagakubo, A., Tsukagoshi, H., Arikawa, K., … Hodoshima, R. (2009). Quadruped walking robots at Tokyo Institute of Technology. IEEE Robotics & Automation Magazine, 16(2), 104-114. doi:10.1109/mra.2009.932524Stoica, A., Carbone, G., Ceccarelli, M., & Pisla, D. (2010). Cassino Hexapod : Experiences and new leg design. 2010 IEEE International Conference on Automation, Quality and Testing, Robotics (AQTR). doi:10.1109/aqtr.2010.5520756Bares, J. E., & Wettergreen, D. S. (1999). Dante II: Technical Description, Results, and Lessons Learned. The International Journal of Robotics Research, 18(7), 621-649. doi:10.1177/02783649922066475Schiele, A., Romstedt, J., Lee, C., Henkel, H., Klinkner, S., Bertrand, R., … Michaelis, H. (2008). NanoKhod Exploration Rover - A Rugged Rover Suited for Small, Low-Cost, Planetary Lander Mission. IEEE Robotics & Automation Magazine, 15(2), 96-107. doi:10.1109/mra.2008.917888Takayama, T., & Hirose, S. (2003). Development of Souryu I & II -Connected Crawler Vehicle for Inspection of Narrow and Winding Space. Journal of Robotics and Mechatronics, 15(1), 61-69. doi:10.20965/jrm.2003.p0061Cuesta, F., & Ollero, A. (2005). Intelligent Mobile Robot Navigation. Springer Tracts in Advanced Robotics. doi:10.1007/b14079Ohya, I., Kosaka, A., & Kak, A. (1998). Vision-based navigation by a mobile robot with obstacle avoidance using single-camera vision and ultrasonic sensing. IEEE Transactions on Robotics and Automation, 14(6), 969-978. doi:10.1109/70.736780Desouza, G. N., & Kak, A. C. (2002). Vision for mobile robot navigation: a survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(2), 237-267. doi:10.1109/34.982903Borenstein, J., Everett, H. R., Feng, L., & Wehe, D. (1997). Mobile robot positioning: Sensors and techniques. Journal of Robotic Systems, 14(4), 231-249. doi:10.1002/(sici)1097-4563(199704)14:43.0.co;2-rBetke, M., & Gurvits, L. (1997). Mobile robot localization using landmarks. IEEE Transactions on Robotics and Automation, 13(2), 251-263. doi:10.1109/70.563647Kuffner, J., Nishiwaki, K., Kagami, S., Inaba, M., & Inoue, H. (2005). Motion Planning for Humanoid Robots. Robotics Research. The Eleventh International Symposium, 365-374. doi:10.1007/11008941_39Lee, Y.-J., & Bien, Z. (2002). Path planning for a quadruped robot: an artificial field approach. Advanced Robotics, 16(7), 609-627. doi:10.1163/15685530260390746Petres, C., Pailhas, Y., Patron, P., Petillot, Y., Evans, J., & Lane, D. (2007). Path Planning for Autonomous Underwater Vehicles. IEEE Transactions on Robotics, 23(2), 331-341. doi:10.1109/tro.2007.895057P. Raja. (2012). Optimal path planning of mobile robots: A review. International Journal of the Physical Sciences, 7(9). doi:10.5897/ijps11.1745Hart, P., Nilsson, N., & Raphael, B. (1968). A Formal Basis for the Heuristic Determination of Minimum Cost Paths. IEEE Transactions on Systems Science and Cybernetics, 4(2), 100-107. doi:10.1109/tssc.1968.300136Lozano-Pérez, T., & Wesley, M. A. (1979). An algorithm for planning collision-free paths among polyhedral obstacles. Communications of the ACM, 22(10), 560-570. doi:10.1145/359156.359164Lozano-Perez. (1983). Spatial Planning: A Configuration Space Approach. IEEE Transactions on Computers, C-32(2), 108-120. doi:10.1109/tc.1983.1676196Brooks, R. A. (1983). Solving the find-path problem by good representation of free space. IEEE Transactions on Systems, Man, and Cybernetics, SMC-13(2), 190-197. doi:10.1109/tsmc.1983.6313112Schwartz, J. T., & Sharir, M. (1983). On the «piano movers» problem. II. General techniques for computing topological properties of real algebraic manifolds. Advances in Applied Mathematics, 4(3), 298-351. doi:10.1016/0196-8858(83)90014-3Kavraki LE. Random networks in configurations space for fast path planning. Doctoral dissertation, Department of Computer Science, Stanford University, Stanford, CA, 1994.Kavraki, L. E., Latombe, J.-C., Motwani, R., & Raghavan, P. (1998). Randomized Query Processing in Robot Path Planning. Journal of Computer and System Sciences, 57(1), 50-60. doi:10.1006/jcss.1998.1578Hsu, D., Kindel, R., Latombe, J.-C., & Rock, S. (2002). Randomized Kinodynamic Motion Planning with Moving Obstacles. The International Journal of Robotics Research, 21(3), 233-255. doi:10.1177/027836402320556421Kavraki, L. E., Svestka, P., Latombe, J.-C., & Overmars, M. H. (1996). Probabilistic roadmaps for path planning in high-dimensional configuration spaces. IEEE Transactions on Robotics and Automation, 12(4), 566-580. doi:10.1109/70.508439Rubio, F., Valero, F., Sunyer, J., & Mata, V. (2009). Direct step‐by‐step method for industrial robot path planning. Industrial Robot: An International Journal, 36(6), 594-607. doi:10.1108/01439910910994669Howard, T. M., & Kelly, A. (2007). Optimal Rough Terrain Trajectory Generation for Wheeled Mobile Robots. The International Journal of Robotics Research, 26(2), 141-166. doi:10.1177/0278364906075328Valero FJ. Planificación de trayectorias libres de obstáculos para un manipulador plano. Doctoral Thesis, UPV, Spain, 1990.Valero, F., Mata, V., Cuadrado, J. I., & Ceccarelli, M. (1996). A formulation for path planning of manipulators in complex environments by using adjacent configurations. Advanced Robotics, 11(1), 33-56. doi:10.1163/156855397x00038Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182-197. doi:10.1109/4235.996017Garcia, M. A. P., Montiel, O., Castillo, O., Sepúlveda, R., & Melin, P. (2009). Path planning for autonomous mobile robot navigation with ant colony optimization and fuzzy cost function evaluation. Applied Soft Computing, 9(3), 1102-1110. doi:10.1016/j.asoc.2009.02.014Miao, H., & Tian, Y.-C. (2013). Dynamic robot path planning using an enhanced simulated annealing approach. Applied Mathematics and Computation, 222, 420-437. doi:10.1016/j.amc.2013.07.022Bobrow, J. E., Dubowsky, S., & Gibson, J. S. (1985). Time-Optimal Control of Robotic Manipulators Along Specified Paths. The International Journal of Robotics Research, 4(3), 3-17. doi:10.1177/027836498500400301Kang Shin, & McKay, N. (1985). Minimum-time control of robotic manipulators with geometric path constraints. IEEE Transactions on Automatic Control, 30(6), 531-541. doi:10.1109/tac.1985.1104009Kyriakopoulos, K. J., & Saridis, G. N. (s. f.). Minimum jerk path generation. Proceedings. 1988 IEEE International Conference on Robotics and Automation. doi:10.1109/robot.1988.12075Constantinescu, D., & Croft, E. A. (2000). Smooth and time-optimal trajectory planning for industrial manipulators along specified paths. Journal of Robotic Systems, 17(5), 233-249. doi:10.1002/(sici)1097-4563(200005)17:53.0.co;2-yGasparetto, A., & Zanotto, V. (2010). Optimal trajectory planning for industrial robots. Advances in Engineering Software, 41(4), 548-556. doi:10.1016/j.advengsoft.2009.11.001JIANGdagger, Z.-P., & NIJMEIJER, H. (1997). Tracking Control of Mobile Robots: A Case Study in Backstepping**This paper was not presented at any IFAC meeting. This paper was recommended for publication in revised form by Associate Editor Alberto Isidori under the direction of Editor Tamer Başar. Automatica, 33(7), 1393-1399. doi:10.1016/s0005-1098(97)00055-1Klosowski, J. T., Held, M., Mitchell, J. S. B., Sowizral, H., & Zikan, K. (1998). Efficient collision detection using bounding volume hierarchies of k-DOPs. IEEE Transactions on Visualization and Computer Graphics, 4(1), 21-36. doi:10.1109/2945.675649Mirtich B. V-Clip: fast and robust polyhedral collision detection. Technical Report TR97-05, Mitsubishi Electric Research Laboratory, 1997.Mohamed, E. F., El-Metwally, K., & Hanafy, A. R. (2011). An improved Tangent Bug method integrated with artificial potential field for multi-robot path planning. 2011 International Symposium on Innovations in Intelligent Systems and Applications. doi:10.1109/inista.2011.5946136Seder, M., & Petrovic, I. (2007). Dynamic window based approach to mobile robot motion control in the presence of moving obstacles. Proceedings 2007 IEEE International Conference on Robotics and Automation. doi:10.1109/robot.2007.363613Simmons, R. (s. f.). The curvature-velocity method for local obstacle avoidance. Proceedings of IEEE International Conference on Robotics and Automation. doi:10.1109/robot.1996.51102

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We consider the problem of reconstructing a 3-D scene from a moving camera with high frame rate using the affine projection model. This problem is traditionally known as Affine Structure from Motion (Affine SfM), and can be solved using an elegant low-rank factorization formulation. In this paper, we assume that an accelerometer and gyro are rigidly mounted with the camera, so that synchronized linear acceleration and angular velocity measurements are available together with the image measurements. We extend the standard Affine SfM algorithm to integrate these measurements through the use of image derivatives

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