D. Floreano and F. Mondada. Evolution of homing navigation in a real mobile robot. IEEE Transactions on Systems, Man, and Cybernetics--Part B: Cybernetics, 26(3), pages 396--407, 1996. 75 76 REFERENCES  Home/Search   Document Details and Download   Summary   Related Articles   Check

....scale to many control parameters, as then the search spaces can become unreasonably large. As described earlier, genetic algorithms 19 require much performance data to function, and even though other investigators have interleaved simulated and real world performance evaluation with some success [29], 60] accurate simulation is required to produce working controllers (which becomes more di#cult as task complexity increases) Also, control heterogeneity was permitted in all cases, which meant that public policies su#ered severely from the state space size problem, as parameter values for ....

....systems depend heavily on sensitive agent to agent and agent to environment interactions, performance is often stochastic, hence evaluative, rather than gradient based, search methods are appropriate. This type of control optimization has been extensively studied for the case of a single agent [93, 29, 111], as well as for multiple agents [82, 70] This design methodology assumes that all agents follow the same control policy and that the only metric used for system evaluation is the global task metric. The use of homogeneous controllers with a global reward signal provides a way of addressing the ....

D. Floreano and Mondada F. Evolution of homing navigation in a real mobile robot. IEEE Transactions on Systems, Man and Cybernetics, 26:396--407, June 1996.

....to this end. Here we brie y review a few of these studies, each demonstrating the use of a di erent technique. The activity of the 13 internal (hidden layer) neurons in the network as a function of a robot s location and orientation was charted via a simple form of receptive eld measurement in [65]. The function of these intermediate neurons was generally highly distributed and dependent on previous states, but a certain interneuron playing a major role in path planning was also identi ed. Others have systematically clamped neuronal activity and studied its e ects on the robot s behavior ....

D. Floreano and F. Mondada. Evolution of homing navigation in a real mobile robot. IEEE Transactions on Systems, Man, and Cybernetics - Part B, 26(3):396-407, 1996.

....have a common temporal behavior, making a transition from one quasi stable state of ring to another together. Dynamical modules give new insights to CPG operation, describing them in terms of a nite state machine, and enabling a rigorous analysis of their robustness to parameter variations. In [10], the activity of internal neurocontroller neurons as a function of a robot s location and orientation was charted by a simple form of receptive eld measurement. Neuronal functioning was generally highly distributed, but a speci c interneuron that had an important role in path planning was also ....

D. Floreano and F. Mondada. Evolution of homing navigation in a real mobile robot. IEEE Transactions on Systems, Man, and Cybernetics - Part B, 26(3):396-407, 1996.

.... and parameterized version of this model in order to evolve optimal synaptic learning rules for RL (with respect to maximizing nectar intake) using a genetic algorithm [26] In contrast to common evolutionary computation applications that involve NNs with evolvable synaptic weights or architectures [1, 10, 32], we set upon the task of evolving the network s neuronal learning rules. Previous attempts at evolving neuronal learning rules have used heavily constrained network dynamics and very limited sets of learning rules [4, 6, 11, 12] or evolved only a subset of the learning rule parameters [45] We ....

.... between evolving traits (such as synaptic weights) versus learning them [1, 19] A relatively small amount of research has been devoted to the evolution of the learning process itself, most of which was constrained to choosing the appropriate learning rule from a limited set of prede ned rules [4, 6, 10]. In this work we show for the rst time that optimal learning rules for RL in a general class of armed bandit situations, can be evolved in a general Hebbian learning framework. The evolved learning rules are by no means trivial, as they are heterosynaptic and employ synaptic plasticity ....

D. Floreano and F. Mondada. Evolution of homing navigation in a real mobile robot. IEEE Transactions on Systems, Man and Cybernetics, 26(3):396-407, 1996.

....systems depend heavily on sensitive agent toagent and agent to environment interactions, performance is often stochastic, and evaluative, rather than gradient based, search methods are appropriate. This type of control optimization has been extensively studied for the case of a single agent [14] [15], 16] as well as for multiple agents [17] 18] 19] II. The Flocking Task A. Task Definition The flocking task examined in this paper is similar in form to the cooperative movement task studied in [20] The agents begin each trial at random positions and orientations within an area A ....

D. Floreano and Mondada F., "Evolution of homing navigation in a real mobile robot," IEEE Transactions on Systems, Man and Cybernetics, vol. 26, pp. 396--407, June 1996.

....conducted. Much of this work was done using computer based simulations only [1] 2] 6] Examples of ER research conducted with real robots include the evolution of walking behaviors in hexapod and octopod robots [7] 8] and the evolution of simple behavioral controllers for small mobile robots[9][10]. The later include the development of phototaxis behaviors [11] 12] and of simple object avoidance [10] and navigation [13] For recent reviews of the field of ER see [14] 15] 16] 13] 1.2 Intelligence performance metrics in ER An ER application may make use of one or more types of fitness or ....

.... research conducted with real robots include the evolution of walking behaviors in hexapod and octopod robots [7] 8] and the evolution of simple behavioral controllers for small mobile robots[9] 10] The later include the development of phototaxis behaviors [11] 12] and of simple object avoidance [10] and navigation [13] For recent reviews of the field of ER see [14] 15] 16] 13] 1.2 Intelligence performance metrics in ER An ER application may make use of one or more types of fitness or intelligence metrics. These include 1) measurement of behavior quality for selection during training, 2) ....

D. Floreano and F. Mondada , Evolution of homing navigation in a real mobile robot. IEEE Transactions on Systems, Man, Cybernetics Part B: Cybernetics, Vol. 26, No. 3, pp. 396-407, 1996.

....Experimental results show that this approach has potential to develop a sophisticated neural controller for complex environments. 1. Introduction There are many studies of constructing mobile robot controller with different approaches such as evolving neural network by genetic algorithm [1], using genetic programming [2] combining fuzzy controller with genetic algorithm [3] and programming [4] In previous work [5] we presented CAM Brain, evolved neural networks based on cellular automata [5,6] and applied it to controlling a mobile robot. However, the controller composed of one ....

D. Floreano and F. Mortdana, "Evolution of homing navigation in a real mobile robot," [EEE Trans. Systems, Man, and Cybernetics, Vol. 26, No 3, pp. 396-407, $une, 1996.

....simulator. Simulation results show the possibility of easily developing higher behaviors by integrating CAM Brain behavior modules. 1. Introduction There are many studies of constructing mobile robot controller with different approaches such as evolving neural network by genetic algorithm [1], using genetic programming [2] combining fuzzy controller with genetic algorithm [3] and programming [4] In previous work [5] we presented CAM Brain, evolved neural networks based on cellular automata [5,6] and applied it to controlling a mobile robot. However, the controller obtained had a ....

D. Floreano and F. Mondana, "Evolution of homing navigation in a real mobile robot," IEEE Trans. Systems, Man, and Cybernetics, Vol. 26, No 3, pp. 396-407, June, 1996.

.... object of several trial and error experiments until the choice of the one with better performance (which in some cases needs a little help from the human, in establishing good situations) Moreover, a need to shorten evolution time is also identi ed by researchers using only GAs (see for example [2]) Consequently to appreciate the di erence between the two approaches we must rst consider where to place the border of human design and what we gain with its displacement in either way. 3 As to the second question, the use of EA in engineering research is interesting for obtaining parameter ....

Dario Floreano and Francesco Mondada. Evolution of homing navigation in a real mobile robot. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 26(3):396-407, June 1996.

....differing environments and remain operational when non vital components fail or become damaged. Previously, it has been demonstrated that robots can automatically acquire behaviours via a variety of learning methods, see [1] 2] 3] 4] 5] for reinforcement learning (RL) methods and [6] [7], 8] 9] for classifier (or evolutionary) techniques) However, these methods only produce successful results where the input to output state space is small enough to be learnt in real time. Generally, this restricts most robot learning experiments to be performed with toy robots equipped with ....

D. Floreano and F. Mondada, Evolution of Homing Navigation in a Real Mobile Robot, IEEE Trnas. on Sys. Man and Cybernetics, Vol. 23, No. 5, 1996.

....based decision control, using the model presented in [2] Other solutions, such as evolutionary programming or arti cial neural nets, might have been used. But the evolution or learning times involved and the speci c problems they would pose, would delay conclusive results (see, for instance, [3]) 3.1 A Procedural Representation The proposed solution to represent how to get back home uses the constituent behaviour modules themselves. The robot memorizes which behaviours have been active from the moment it left home until it nds the light source. Thereafter the robot will revert those ....

Dario Floreano and Francesco Mondada. Evolution of homing navigation in a real mobile robot. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 26(3):396-407, June 1996.

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D. Floreano and F. Mondada. Evolution of homing navigation in a real mobile robot. IEEE Transactions on Systems, Man, and Cybernetics--Part B: Cybernetics, 26(3), pages 396--407, 1996. 75 76 REFERENCES

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D. Floreano and F. Mondada. Evolution of homing navigation in a real mobile robot. IEEE Transactions on Systems, Man, and Cybernetics, 1996.

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D. Floreano and F. Mondada. Evolution of homing navigation in a real mobile robot. IEEE Transactions on Systems, Man, and Cybernetics---Part B: Cybernetics, 26(3):396--407, 1996.

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D. Floreano and F. Mondada, Evolution of Homing Navigation in a Real Mobile Robot, IEEE Transactions on Systems, Man, and Cybernetics-Part B, 26:396---407, 1996

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D. Floreano and F. Mondada. Evolution of homing navigation in a real mobile robot. IEEE Transactions on Systems, Man, and Cybernetics-- Part B: Cybernetics, 3(26):396--407, 1996.

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Floreano, D. and Mondada, F., Evolution of Homing Navigation in a Real Mobile Robot, in IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics, v. 26, n. 3, pp. 396-407, 1996.

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Dario Floreano and Francesco Mondada. Evolution of homing navigation in a real mobile robot. In [16], pages 402--4101, 1996.

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D. Floreano, F. Mondada, Evolution of homing navigation in a real mobile robot, IEEE Transactions on Systems Man, and Cybernetics. Part B: Cybernetics 26 (3) (1996) 396--407.

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D. Floreano, F. Mondada , Evolution of homing navigation in a real mobile robot. IEEE Transactions on Systems, Man, Cybernetics Part B: Cybernetics Vol. 26 No. 3, pp. 396-407, 1996.

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D. Floreano and F. Mondada. Evolution of homing navigation in a real mobile robot. IEEE Transactions on Systems, Man, and Cybernetics, 1996.

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D. Floreano, F. Mondada , "Evolution of homing navigation in a real mobile robot," IEEE Transactions on Systems, Man, Cybernetics Part B: Cybernetics, vol. 26 no. 3, pp. 396-407, 1996.

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D. Floreano and F. Mondada. Evolution of homing navigation in a real mobile robot. IEEE Transactions on Systems, Man, and Cybernetics, 1996.

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D. Floreano and F. Mondada. Evolution of homing navigation in a real mobile robot. IEEE Transactions of Systems, Man, and Cybernetics-Part B: Cybernetics, 26(3):396-- 407, 1996.

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