Shumeet Baluja. Evolution of an artificial neural network based autonomous land vehicle controller. IEEE Transactions on Systems, Man and Cybernetics, Part B, 26(3):450--463, June 1996.  Home/Search   Document Not in Database   Summary   Related Articles   Check

.... to use of those evolutionary computation (EC) methods which have previously been applied to uni objective NN design, genetic algorithms (GAs) evolution strategies (ES) and particle swarm optimisation (PSO) GAs have previously be used for feature selection [8, 53] and topography selection [2, 5, 29, 35, 36, 38, 52] and ESs have been used for weight optimisation [21, 42, 45, 55] and adaptive topography selection [15, 37, 57] The recent EC technique of PSO [27] has also proved popular as a uni objective NN optimiser [10, 12, 13, 26, 48] 2 Multi objective evolutionary neural network flamework The use of ....

S. Baluja. Evolution of an Artificial Neural Network Based Autonomous Land Vehicle Controller. IEEE Transactions on Systems Man and Cybernetics - Part B: Cybernetics, 26(3):450-463, 1996.

....downloading each to the robot and testing it in the real world. Finally, the Sussex gantry robot [2] mentioned earlier has used evaluation on the real robot. They used a population size of 30, and found good solutions after 10 generations. The closest work to that reported here was done by Baluja [1], who evolves a neural controller that interprets a 15 x 16 pixel image from a camera mounted on a car. The network outputs are interpreted as a steering direction, the goal being to keep it on the road. Training data comes from recording human drivers. In summary, Evolutionary Robotics has used ....

S. Baluja, Evolution of an Artificial Neural Network Based Autonomous Land Vehicle Controller. IEEE Transactions on Systems, Man and Cybernetics Part B: Cybernetics. 26, 3, 450-463. (1996)

....downloading each to the robot and testing it in the real world. Finally, the Sussex gantry robot [2] mentioned earlier has used evaluation on the real robot. They used a population size of 30, and found good solutions after 10 generations. The closest work to that reported here was done by Baluja [1], who evolves a neural controller that interprets a 15 x 16 pixel image from a camera mounted on a car. The network outputs are interpreted as a steering direction, the goal being to keep it on the road. Training data comes from recording human drivers. In summary, Evolutionary Robotics has used ....

S. Baluja, Evolution of an Artificial Neural Network Based Autonomous Land Vehicle Controller. IEEE Transactions on Systems, Man and Cybernetics Part B: Cybernetics. 26, 3, 450-463. (1996)

....suite of standard problems may be found in [12] 6. 3 Applying PBIL to PolySAPIENT PBIL s domain independent qualities have enabled researchers to apply it to a variety of optimization problems including figure layout [11] registration in medical robotics [13] and neural network weight training [10]. Unlike many supervised learning methods, evolutionary algorithms like PBIL do not need explicit models (or examples) of correct behavior (i.e. competent tactical driving) since good solutions are automatically discovered through unguided, stochastic exploration. Furthermore, since PBIL treats ....

S. Baluja. Evolution of an artificial neural network based autonomous land vehicle controller. IEEE Transactions on Systems, Man and Cybernetics, 26(3), 1996.

....they are usually scarce and precious before. Furthermore, on line training, and or on line retraining, of ANNs is very important in many real time reactive environments. For example, when we require to control the steering direction of a autonomous vehicle system under various road conditions [5], or recognize, detect and extract objects in images and video sequences under variable perceptual conditions (shading, shadows, lighting conditions, and reflections) 4, 7, 13, 14, 27] Despite the abundance of methods for learning from examples, there are only few that can be used e#ectively ....

S. Baluja, "Evolution of an artificial neural network based autonomous land vehicle controller", IEEE Transactions on System, Man and Cybernetics-Part B, 26, 450--463, (1996).

....project, called ALVINN, is its use of actual video images, rather than derived terrain features, and that it used a real vehicle, rather than a simulation. However, the task being duplicated was not heavily cognitive in nature, as the requirement was that the vehicle remain on the road. Baluja [14] provides a more detailed description of the ALVINN system. Our approach is to develop a Situational Awareness Module (SAM see Figure 1) to provide a hybrid structure and therefore handle the complex reasoning required to associate expert actions to the current environmental state. It senses ....

Baluja, S., "Evolution of an Artificial Neural Network Based Autonomous Land Vehicle Controller", IEEE Transactions of Systems, Man and Cybernetics, Vol. 26, No. 3, June 1996, pp. 450-463.

....been applied to robot control is given by Prabhu and Garg [16] Most of these works, however, have concentrated on simulated systems and therefore have not had to deal with the ambiguities and constraints of the real world. The primary exception to this has been in the area of navigation (e.g. [1, 2, 3]) In 1996, Hougen et al. [5] presented a new connectionist system designed for task learning on a real robot. In the current paper, we present an extension of this learning system to a significantly more difficult task. Learning responses is generally classified into supervised and unsupervised ....

S. Baluja. Evolution of an artificial neural network based autonomous land vehicle controller. IEEE Trans. on Systems, Man, and Cybernetics, 26, Part B(3), 1996.

.... [45, 60, 117, 135, 232, 280, 376, 379, 418, 419, 426, 635, 688] IEEE Transactions on Pattern Analysis and Machine Intelligence, 389] IEEE Transactions on Power Systems, 545] IEEE Transactions on Semiconductor Manufacturing, 469, 546] IEEE Transactions on Systems, Man, and Cybernetics, [431, 455, 581] IEEE Transactions on Systems, Man, and Cybernetics, A, Systems Humans, 445] IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences, 47, 79] IEICE Transactions on Information and Systems, 847] Information Sciences, 81, 98, 136] Inform atica y Autom atica ....

....M. de, 273] Baba, N. 605] Baba, Norio, 535] Back, Barbro, 148, 318, 430] Badii, A. 606] Baerdemaeker, J. 268] Baidyk, Tatyana N. 191] Balakrishnan, Karthik, 150] Balasekar, S. 426] Balicki, J. 236] Ball, A. D. 537] Ball, N. R. 323, 607, 608] Baluja, Shumeet, [151, 431] Banzhaf, Wolfgang, 65] Barham, John, 602] Barnard, S. T. 620] Barone, Dante Augusto Couto, 292] Barreto, Jorge M. 273] Barton, S. A. 58, 234] Bartscht, E. 440] Baxter, J. 609] Bebis, George, 152, 432] Becks, K. H. 610] Beer, Randall D. 611, 612] Belew, Richard K. ....

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Shumeet Baluja. Evolution of an artificial neural network based autonomous land vehicle controller. IEEE Transactions on Systems, Man, and Cybernetics, 26(3):450--463, 1996. ga96bBaluja. 60 Genetic algorithms and neural networks

....[351] Comput. Ind. Eng. UK) 229] Control Engineering Practice, 90] IEE Colloq. Dig. 262] IEE Conf. Publ. ETSI konferenssi, 265] IEEE Transactions on Evolutionary Computation, 301] IEEE Transactions on Industrial Electronics, 244] IEEE Transactions on Systems, Man, and Cybernetics, [258, 264, 270, 278, 324, 325, 348] IEICE Transactions, 435] IEICE Transactions on Information and Systems, 408] Information Sciences, 311] International Journal of Vehicle Design, 290] J. Intell. Robot. Syst. Theory Appl. Netherlands) 246] J. Jpn. Soc. Precision Eng. Japan) 308] J. Robot. Syst. USA) 99, 259, ....

....[86] Ashiru, I. 125, 140] Ashlock, Dan, 207] Aspragathos, N. A. 235] Aspragathos, Nikos A. 208, 307] Atmar, J. Wirt, 350] Aydin, K. K. 126] Baba, N. 101] Baba, Norio, 65] Baek, Seung Min, 314] Baffes, Paul T. 327, 328] Balakrishnan, Karthik, 210, 257, 286] Baluja, Shumeet, [258] Banzhaf, Wolfgang, 198, 202, 204, 237, 238, 287] Barnes, D. P. 88] Barrett, David, 437] Bartscht, E. 33] Beer, Randall D. 216] Bennett III, Forest H. 288] Bersano Begey, Tommaso F. 242] Bessi ere, Pierre, 160, 329, 416, 417, 418, 419, 420, 421, 422, 423, 424] Bikdash, M. 296] ....

[Article contains additional citation context not shown here]

Shumeet Baluja. Evolution of an artificial neural network based autonomous land vehicle controller. IEEE Transactions on Systems, Man, and Cybernetics, 26(3):450--463, 1996. ga96bBaluja.

.... IEEE Transactions on Power Systems, 89, 116, 129, 153, 161, 206, 228, 233, 247, 283, 343, 667] IEEE Transactions on Reliability, 88] IEEE Transactions on Semiconductor Manufacturing, 397, 854] IEEE Transactions on Signal Processing, 712] IEEE Transactions on Systems, Man, and Cybernetics, [112, 252, 594, 624, 643, 649, 742, 743, 797, 867] IEEE Transactions on Systems, Man, and Cybernetics Part B: Cybernetics, 248, 391, 505] IEEE Transactions on Systems, Man, and Cybernetics A: Syst. Humans. 522] IEEE Transactions on Systems, Man, and Cybernetics B, Cybernetics, 822, 858] IEEE Transactions on Systems, Man, and ....

....Haldun, 35, 587] Azvine, Behnam, 511] Baba, N. 592] Babu, G. Phanendra, 44] Back, Barbro, 590] Back, H. 724] Back, Thomas, 479] Bakirtzis, A. G. 89, 233] Bala, Jerzy W. 593] Balakrishnan, P. V. 344] Balas, G. J. 23] Balasekar, S. 584] Balogh, S. 629] Baluja, Shumeet, [594] Bandyopadhyay, Sanghamitra, 747] Bangalore, Arjun S. 24] Bangalore, Shanthamallikarjuna Shivappa, 425] Banzhaf, Wolfgang, 333, 445, 846, 880] Barnes, J. W. 75] Barron, M. 373] Bartels, Christian, 45] Basile, Luciano, 595] Baskaran, Subbiah, 100] Bastian, A. 46] Baumgarten, G. ....

[Article contains additional citation context not shown here]

Shumeet Baluja. Evolution of an artificial neural network based autonomous land vehicle controller. IEEE Transactions on Systems, Man, and Cybernetics, 26(3):450--463, 1996. ga96bBaluja.

....their acceptance angles and their positions relative to the longitudinal axis of the robot. Depending upon which variety of individual neurons is to be included in which architecture, the genotypes used in evolutionary robotics directly code synaptic weights (and neural biases) as in [11] and [2] for example or they also code additional characteristics, like time delays or neuron numbers as in [25] and [66] However, several research efforts ( 42] 7] 17] call upon an indirect encoding scheme, according to which the genotype is a developmental program that usually acts upon a ....

Baluja, S. Evolution of an Artificial Neural Network Based Autonomous Land Vehicle Controller. IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics. 26, 3, 450463, 1996.

....or even continuous since EAs do not depend on gradient information. Because EAs can treat large, complex, nondifferentiable and multimodal spaces, which are the typical case in the real world, considerable research and application has been conducted on the evolution of connection weights [24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112]. The evolutionary approach to weight training in ANNs consists of two major phases. The first phase is to decide the representation of connection weights, i.e. whether in the form of binary strings or not. The second one is the evolutionary process simulated by an EA, in which search operators ....

S. Baluja, "Evolution of an artificial neural network based autonomous land vehicle controller, " IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 26, no. 3, pp. 450--463, 1996.

....with genetic algorithms (Cliff, 1991; Harvey, Husbands Cliff, 1993; Floreano Mondada, 1994) with considerable success. Evolutionary algorithms for creating an artificial neural network which controlled an autonomous land vehicle have been tested in applications such as road following tasks (Baluja, 1996). A more general class of models is known under the heading selectionist neural networks. Selectionist approaches to neural networks have a decade old tradition starting from the seminal work of Edelman on Neural Darwinism (Edelman, 1987) Donnart and Meyer (Donnart Meyer, 1996) consider the ....

Baluja, S. (1996). Evolution of an Artificial Neural Network based Autonomous Land Vehicle Controller. IEEE Transactions Systems, Man and Cybernetics - Part B, Special Issue on Learning Autonomous Robots, 26, 450 --- 463.

....with a smaller population size [13] Baluja found evidence to support Fitpatrick s and 3 Other fitness options were also explored, such as averaging the results of the random starts or using the minimum of the random starts. Neither of these options improved performance. Grefenstette s claim [2]. Unlike the case for CRBP, in the GA, each type of reinforcement was given a different reinforcement value: ffl Accomplished a light goal: 50 ffl Not moving: Gamma4 ffl Any touch sensor triggered: Gamma2 ffl Not following light gradient correctly for goal: Gamma1 ffl Following light ....

S. Baluja, "Evolution of an artificial neural network based autonomous land vehicle controller," this issue.

.... (see [21, chapter 1] for a simple description of where and why evolutionary search outperforms other search methods) These two properties, generality and efficiency, are often exploited to optimise unknown parameters of a complex system so that it will exhibit a well defined behaviour (e.g. see [2]) To the extent in which behaviour generation is externally defined and driven to a specific goal, evolutionary optimisation is not very different from the supervised learning approach described in section 2.3. However, natural evolution does not have the notion of teleology which is so familiar ....

S. Baluja. Evolution of an Artificial Neural Network Based Autonomous Land Vehicle Controller. IEEE Transactions on Systems, Man, and Cybernetics-Part B, 26, 1996.

....the evolution is applied on the morphology of the visual system as well as on the structure of the neural network. The results showed very smart, economical and efficient solutions, but the fitness functions used were specifically and carefully designed and applied incrementally. Finally, Baluja [Baluja95] evolved neural networks for the control of an autonomous land vehicle (ALVINN) The evaluation of the neural network is based on an accurate knowledge of the response requested from the control system. When combined with gradient descent, the results are slightly better than those found by ....

S. Baluja. Evolution of an Artificial Neural Network Based Autonomous Land Vehicle Controller. IEEE Transactions on Systems, Man, and Cybernetics, In press, 1995.

....which employ evolutionary training of neural controllers, although both resort to a simulation for the training phase. Baluja shows that genetic algorithms can provide strategies to control an autonomous land vehicle (ALVINN) that are comparable to those found by a supervised learning algorithm [65]. The main difference from our work is that Baluja knows exactly what are the appropriate actions that the vehicle should take in the situations employed for training and can exploit this knowledge to assess the performance of the neural networks. Meeden compares the behavioral strategies ....

S. Baluja, "Evolution of an Artificial Neural Network Based Autonomous Land Vehicle Controller", IEEE Transactions on Systems, Man and Cybernetics, vol. This issue, 1996.

No context found.

Shumeet Baluja. Evolution of an artificial neural network based autonomous land vehicle controller. IEEE Transactions on Systems, Man and Cybernetics, Part B, 26(3):450--463, June 1996.

No context found.

S. Baluja, "Evolution of an Artificial Neural Network Based Autonomous Land Vehicle Controller," IEEE Transactions on Systems Man and Cybernetics - Part B: Cybernetics, vol. 26, no. 3, pp. 450--463, 1996.

No context found.

S. Baluja, "Evolution of an artificial neural network based autonomous land vehicle controller," IEEE Trans. Syst., Man, Cybern. B, vol. 26, pp. 450--463, Mar. 1996.

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