In this paper we describe a control methodology for catching a fast moving object with a robot manipulator, where visual information is employed to track the trajectory of the target. Sensing, planning and control are performed in real-time to cope with possible unpredictable trajectory changes of the moving target, and prediction techniques are adopted to compensate the time delays introduced by visual processing and by the robot controller. A simple but reliable model of the robot controller has been taken into account in the control architecture for improving the performance of the system. Experimental results have shown that the robot system is capable of tracking and catching an object moving on a plane at velocities of up to 700 mm/s and accelerations of up to 1500 mm/s2.
标签： methodology describe catching control
上传时间： 2014-01-12
上传用户：qq521
The tca package is a Matlab program that implements the tree-dependent component analysis (TCA) algorithms that extends the independent component analysis (ICA), where instead of looking for a linear transform that makes the data components independent, we are looking for components that can be best fitted in a tree structured graphical model. The TCA model can be applied in any situation where the data can be assumed to have been transformed by an unknown linear transformation.
标签： tree-dependent implements component analysis
上传时间： 2016-09-17
上传用户：cazjing
At can be given its arguments in a file. You can comment out lines by preceding them with either # or - characters. This is an easy way to temporarily disable some commands. The CONTINUE-command is most useful at the end of the file. When this command is read, the file is started again from the beginning. You can use it situations where the machine is not shut down for the night and you want to run some commands every day.
标签： can arguments preceding comment
上传时间： 2013-12-11
上传用户：comua
simulating a convolutional encoder allows the user to input a source code to be encoded and also input the values of the generator polynomials. It outputs the encoded data bits, where 1/n is the code rate
标签： convolutional simulating encoder encoded
上传时间： 2013-12-21
上传用户：253189838
This paper presents a visual based localization mechanism for a legged robot. Our proposal, fundamented on a probabilistic approach, uses a precompiled topological map where natural landmarks like doors or ceiling lights are recognized by the robot using its on-board camera. Experiments have been conducted using the AIBO Sony robotic dog showing that it is able to deal with noisy sensors like vision and to approximate world models representing indoor ofce environments. The two major contributions of this work are the use of this technique in legged robots, and the use of an active camera as the main sensor
标签： localization mechanism presents proposal
上传时间： 2016-11-04
上传用户：dianxin61
The "GEE! It s Simple" package illustrates Gaussian elimination with partial pivoting, which produces a factorization of P*A into the product L*U where P is a permutation matrix, and L and U are lower and upper triangular, respectively. The functions in this package are accurate, but they are far slower than their MATLAB equivalents (x=A\b, [L,U,p]=lu(A), and so on). They are presented here merely to illustrate and educate. "Real" production code should use backslash and lu, not this package.
标签： illustrates elimination Gaussian pivoting
上传时间： 2016-11-09
上传用户：wang5829
The "GEE! It s Simple" package illustrates Gaussian elimination with partial pivoting, which produces a factorization of P*A into the product L*U where P is a permutation matrix, and L and U are lower and upper triangular, respectively. The functions in this package are accurate, but they are far slower than their MATLAB equivalents (x=A\b, [L,U,p]=lu(A), and so on). They are presented here merely to illustrate and educate. "Real" production code should use backslash and lu, not this package.
标签： illustrates elimination Gaussian pivoting
上传时间： 2014-01-21
上传用户：lxm
This demo shows the BER performance of linear, decision feedback (DFE), and maximum likelihood sequence estimation (MLSE) equalizers when operating in a static channel with a deep null. The MLSE equalizer is invoked first with perfect channel knowledge, then with an imperfect, although straightforward, channel estimation algorithm. The BER results are determined through Monte Carlo simulation. The demo shows how to use these equalizers seamlessly across multiple blocks of data, where equalizer state must be maintained between data blocks.
标签： performance likelihood decision feedback
上传时间： 2013-11-25
上传用户：1079836864
实现：SELECT [ALL|DISTINCT] <属性表达式>[，<属性表达式>…] FROM <表名或视图名>[，<表名或视图名>…] [where <条件>] [GROUP BY <属性1>[HAVING<条件>]] [ORDER BY <属性2> [ASC|DEC]
上传时间： 2014-01-19
上传用户：lvzhr
Problem B:Longest Ordered Subsequence A numeric sequence of ai is ordered if a1 < a2 < ... < aN. Let the subsequence of the given numeric sequence (a1, a2, ..., aN) be any sequence (ai1, ai2, ..., aiK), where 1 <= i1 < i2 < ... < iK <= N. For example, sequence (1, 7, 3, 5, 9, 4, 8) has ordered subsequences, e. g., (1, 7), (3, 4, 8) and many others. All longest ordered subsequences are of length 4, e. g., (1, 3, 5, 8).
标签： Subsequence sequence Problem Longest
上传时间： 2016-12-08
上传用户：busterman
