Autonomous Military Robotics by Vishnu Nath, Stephen E. Levinson (auth.)
By Vishnu Nath, Stephen E. Levinson (auth.)
This SpringerBrief unearths the most recent options in desktop imaginative and prescient and laptop studying on robots which are designed as exact and effective army snipers. Militaries worldwide are investigating this expertise to simplify the time, price and defense measures worthwhile for education human snipers. those robots are built by way of combining an important elements of laptop technology study components together with picture processing, robot kinematics and studying algorithms. The authors clarify how a brand new humanoid robotic, the iCub, makes use of high-speed cameras and laptop imaginative and prescient algorithms to trace the thing that has been labeled as a objective. The robotic adjusts its arm and the gun muzzle for max accuracy, because of a neural version that incorporates the parameters of its joint angles, the rate of the bullet and the approximate distance of the objective. a radical literature evaluate presents useful context for the experiments. Of functional curiosity to army forces all over the world, this short is designed for execs and researchers operating in army robotics. it's going to even be beneficial for complex point computing device technology scholars concerned with machine imaginative and prescient, AI and laptop studying issues.
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Additional resources for Autonomous Military Robotics
2013). Hough Transformation [PDF Document]. , & Natale, L. (2008). The iCub humanoid robot: an open platform for research in embodied cognition. 8th Workshop on performance metrics for intelligent systems. ACM. , & Levinson, S. (2013). Learning to Fire at Targets by an iCub Humanoid Robot. AAAI Spring Symposium. Palo Alto, CA: AAAI. , & Levinson, S. (2013). Usage of computer vision and machine learning to solve 3D mazes. Urbana, IL: University of Illinois at Urbana-Champaign. , & Levinson, S. (2014).
38 5 Computer Vision Fig. 2 Detection of target by applying Hough circle transform to concentric circles Fig. , & Karray, F. (2011). Visual attention for robotic cognition: A survey. IEEE Transactions on Autonomous Mental Development. , & Ponce, J. (2011). Computer vision: A modern approach. Upper Saddle River, NJ: Prentice Hall. Harnad, S. (1995). Grounding symbolic capacity in robotic capacity. New Haven, CT: Lawrence Erlbaum. , & Metta, G. (2010). Learning the skill of archery by a humanoid iCub.
2 provides us proof that the calculation of the transformation matrices are accurate. It needs to be 30 4 Robot Kinematics Fig. 2 Position vectors of the right arm of the iCub at the final position (Nath & Levinson 2013a, 2013b) pointed out that, at this stage, the system is only in an initial stage. In order to fire at the targets progressively better, the parameters of the joints need to be altered. This aspect of the problem would be taken care of by the core algorithm itself. , & Picard, R.