Distributed neural control for complex autonomous robots

Chapter 2: Evolutionary Robotics


This second chapter is an analysis of the selected framework, making first a description of the techniques from the ER approach to be used for ANN training. Then the limitations of the current ER framework for our particular application are described, and finally, a solution to those limitations is proposed.

The evolutionary robotics paradigm

Evolutionary robotics (ER) is a general framework for the automated design of controllers for autonomous robots. It reproduces in robot controllers the Darwinian principle of selective reproduction or natural evolution; by accumulating small changes in a progressive development. ER uses genetic and evolutionary algorithms to develop control programs for autonomous robots. A robot control system is represented as an artificial chromosome that progressively updates throughout its evolutionary process until it successfully accomplishes the desired robot behaviour, in this way simulating genetics and natural selection laws. Since a robot controller is based on ANNs as processing elements, in this thesis artificial chromosomes will encode ANN weights, so that network
connections of ANNs are evolved.

The evolutionary process evolves the required knowledge for solving the task through experience, and self-adjusts its control system to meet the specific demands of the domain. This means that if avoiding obstacles is a necessary component for good performance, the genetic algorithm will select those networks which are best suited to avoid obstacles.

different applications of the DAIR architecture

The ER problem

The reason for failing to apply ER to complex robots is primarily that the search space that the evolutionary algorithm has to face is huge (due to the high number of parameters to evolve; this dictated by the number of devices to control). Hence, firstly, it is difficult to find a solution through small changes, and secondly, the bootstrap problem prevents the generation of simple solutions with minimum fitness values at the beginning of the evolutionary process that could guide the evolutionary path towards the final solution.

The proposed solution

In order to obtain a solution to this problem, we identify two requirements to be incorporated in the evolutionary process:
  • Use of selective bias: Even though bias is not usually desired in ER, it does seem unavoidable (understanding by bias the use of external information to direct the evolutionary process towards a before-hand known solution). This research suggests that this necessity for bias arises when the artificial evolutionary algorithm tries to reproduce the effects of natural evolution under unfair conditions. While natural evolution gradually evolved at the same time as the animals body plan, sensors and actuators, nervous system, and even environment and needs, artificial evolution tries to evolve the nervous system of a robot for a fixed given body, given group of sensors and actuators, and within a given complex environment and particular task required. If none of the constraints can be relaxed, then using bias seems mandatory.
  • Use of a staged evolution: We propose the use of a mechanism where the controller is evolved using different fitness functions, each one affecting only selected parts of the controller. Each fitness function will contain the information bias that should affect only that part of the controller. The bias is therefore, selective. The controller has to be developed in several stages, and each stage will have separate parts involved in the process. We refer to this process as staged evolution.

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