Experiments in Competence Acquisition for Autonomous Mobile Robots
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This thesis addresses the problem of intelligent control of autonomous mobile robots, particularly under circumstances unforeseen by the designer.As the range of applications for autonomous robots widens and increasingly includes operation in unknown environments (exploration) and tasks which are not clearly specifiable a priori (maintenance work), this question is becoming more and more important. It is argued that in order to achieve such flexibility in unforeseen situations it is necessary to equip a mobile robot with the ability to autonomously acquire the necessary task achieving competences, through interaction with the world. Using mobile robots equipped with self-organising, behaviour-based controllers,experiments in the autonomous acquisition of motor competences and navigational skills were conducted to investigate the viability of this approach. A controller architecture is presented that allows extremely fast acquisition of motor competence such as obstacle avoidance, wall and corridor following and deadend escape: these skills are obtained in less than five learning steps,performed in under one minute of real time. This is considerably faster than previous approaches. Because the effective wiring between sensors and actuators is determined autonomously by the robot, sensors and actuators may initially be wired up arbitrarily,which reduces the risk of human error during the setting up phase of the robot. For the first time it was demonstrated that robots also become able to autonomously recover from unforeseen situations such as changes in the robot's morphology, the environment or the task. Rule-based approaches to error recovery obviously cannot offer recovery from unforeseen errors,as error situations covered by such approaches have to be identified beforehand. A robust and fast map building architecture is presented that enables mobile robots to autonomously construct internal representations of their environment, using self-organising feature maps. After a short training time the robots are able to use these self-organising feature maps successfully for location recognition. For the first time the staged acquisition of multiple competences in mobile robots is presented.First obtaining fundamental motor competences such as wall following and deadend escape (primary skills), the robots use these in a second stage to learn higher levels of competence such as the navigational task of location recognition (secondary skills). Besides laying the foundation of autonomous, staged acquisition of high level competences, this approach has the interesting property of securely grounding secondary skills in the robot's own experience, as these secondary skills are defined in terms of the primary ones.