By Vishnu Nath, Stephen E. Levinson (auth.)
This SpringerBrief unearths the newest concepts in computing device imaginative and prescient and computing device studying on robots which are designed as actual and effective army snipers. Militaries worldwide are investigating this expertise to simplify the time, expense and defense measures useful for education human snipers. those robots are built through combining the most important points of laptop technological know-how examine components together with photo 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 item that has been labeled as a goal. The robotic adjusts its arm and the gun muzzle for optimum accuracy, because of a neural version that incorporates the parameters of its joint angles, the speed of the bullet and the approximate distance of the objective. an intensive literature assessment offers necessary context for the experiments. Of useful 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 helpful for complicated point desktop technological know-how scholars concerned about computing device imaginative and prescient, AI and computing device studying issues.
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Extra info for Autonomous Military Robotics
In Fig. 2, the impact of the final NERF™ bullet right on the center of the target shows that even a simple approach like the least mean squares approach is a very accurate approach to hit stationery targets. In Fig. 2, the reader might be able to see only 3 bullets and not 6. This is because 3 bullets fell off after a few seconds because the sticky tape at their end wasn’t strong enough. However, this did not affect the experiment since we made sure that the robot was able to determine their point of impact and make necessary corrections before the bullets fell off.
Artificial intelligence, a modern approach. Upper Saddle River, NJ: Prentice Hall. , & Vernon, G. (2007). The iCub cognitive humanoid robot: An open-system research platform for enactive cognition. In 50 Years of artificial intelligence (pp. 358–369). Berlin: Springer. Spong, M. , & Vidyasagar, M. (2006). Robot modelling and control. Hoboken, NJ: John Wiley & Sons. Sutton, R. , & Barto, A. G. (1998). Reinforcement learning: An introduction. Cambridge, UK: MIT Press. , & Vernon, D. (2007). iCUb: The design and realization of an open humanoid platform for cognitive and neuroscience research.
The importance of this point cannot be stressed enough, since picking out a wrong target by a fully armed autonomous robot would result in the death or severe injury of (a) person (s) and damage to property. While picking out targets in an isolated environment is fairly trivial, the challenge would be to pick out targets in a crowded environment, where other entities with features similar to what the target possesses are in close proximity to the target. Another area that deserves a lot of research focus is in the combination of the environmental factors and optimizing the bullet trajectory’s probability of making impact with the target.