Robotic interception of moving objects (continued from last week)
Among the most efficient approaches is APPE (Active Prediction, Planning and Execution) system for robotic
interception of moving objects. The key feature of the system is the ability of a robot to perform a task autonomously without complete a priori information.
Kalman filtering has proved very useful in autonomous and assisted navigation and guidance systems, radar tracking of moving objects, etc. The Kalman filter is a set of mathematical equations that provides an efficient computational (recursive) solution of the least-squares method. The filter is very powerful in several aspects: it supports estimates of past, present, and even future states, and it can do so even when the precise nature of the modelled system is unknown. Kalman filtering is also a computationally efficient algorithm, which generates an optimal estimate from a sequence of noisy observations.
This paper discuses an implementation of a robotic interception (a shoot function in robot soccer) based on image capture/processing combined with the successful use of Kalman filtering aiming at substantial improvement in shooting accuracy both for a stationary and moving object (the ball).
That's all for this week, Phase Test is in the way but from what written here i had understand that the article state that it use Kalman filtering to solve shooting accuracy problem when the ball is moving.
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