By Vishnu Nath, Stephen E. Levinson (auth.)
This SpringerBrief finds the most recent recommendations in machine imaginative and prescient and computing device studying on robots which are designed as exact and effective army snipers. Militaries all over the world are investigating this know-how to simplify the time, expense and defense measures helpful for education human snipers. those robots are built via combining the most important elements of desktop technological know-how learn components together with snapshot 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 categorized as a aim. The robotic adjusts its arm and the gun muzzle for max accuracy, because of a neural version that comes with the parameters of its joint angles, the speed of the bullet and the approximate distance of the objective. a radical literature evaluate offers necessary context for the experiments. Of sensible curiosity to army forces around the globe, this short is designed for execs and researchers operating in army robotics. it's going to even be invaluable for complex point computing device technology scholars excited about computing device imaginative and prescient, AI and computing device studying issues.
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Additional resources for Autonomous Military Robotics
From MathWorld—A Wolfram Web Resource. http://mathworld. html Chapter 4 Robot Kinematics Abstract The robotic platform is the physical hardware on which the experiments have been conducted. All algorithms, by definition, should be replicable on any physical machine, irrespective of the individual hardware components. However, all other things being constant, there is no denying that algorithms perform better on more capable hardware. In this chapter, we provide an introduction to the physical characteristics of the iCub robot platform that was used to perform the experiments and benchmark it using parameters that are relevant to the domain of robotics.
The reinforcement learning algorithms being used in this book are Q-learning and SARSA, both of which are discussed in detail in Sect. 3. As aforementioned, unsupervised learning primarily includes clustering because of the lack of any labels in the data. There are other methods that also employ unsupervised learning like hidden Markov models. A detailed discussion of these topics is beyond the scope of this book and is not discussed. Supervised learning, on the other hand, creates a mapping function from input to output that is constantly being improvised by the labeled training data.
1983). Machine learning. Palo Alto, CA: Tioga Publishing Company. Michie, D. (1986). On machine intelligence. New York, NY: Wiley. , & Levinson, S. (2013). Learning to Fire at Targets by an iCub Humanoid Robot. AAAI Spring Symposium. Palo Alto: 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). Solving 3D mazes with machine learning: A prelude to deep learning using the iCub Humanoid Robot.
Autonomous Military Robotics by Vishnu Nath, Stephen E. Levinson (auth.)