The Dynamical Nightwatch's Problem Solved by the Autonomous Micro-Robot Khepera

Löffler A, Klahold J, Rückert U. The Dynamical Nightwatch's Problem Solved by the Autonomous Micro-Robot Khepera. In: Hao J-K, Lutton E, Ronald E, Schoenauer M, Snyers D, eds. Selected Papers of the 3rd European Conference on Artificial Evolution (AE97). Vol 1363. Nimes, France: Springer-Verlag; 1998: 303-313.In this paper, we present the implementation, both in a simulator and in a real-robot version, of an efficient solution to the so-called dynamical nightwatch’s problem on the micro-robot Khepera. The problem consists mainly in exploring a previously unknown environment while detecting, registering and recognizing light sources which may dynamically be turned on and off. At the end of each round a report is requested from the robot. Therein we made use of an agent-based approach and applied a self-organizing feature map in order to refine some of the behaviour generating control-modules

Decision tree learning for intelligent mobile robot navigation

The replication of human intelligence, learning and reasoning by means of computer algorithms is termed Artificial Intelligence (Al) and the interaction of such algorithms with the physical world can be achieved using robotics. The work described in this thesis investigates the applications of concept learning (an approach which takes its inspiration from biological motivations and from survival instincts in particular) to robot control and path planning. The methodology of concept learning has been applied using learning decision trees (DTs) which induce domain knowledge from a finite set of training vectors which in turn describe systematically a physical entity and are used to train a robot to learn new concepts and to adapt its behaviour. To achieve behaviour learning, this work introduces the novel approach of hierarchical learning and knowledge decomposition to the frame of the reactive robot architecture. Following the analogy with survival instincts, the robot is first taught how to survive in very simple and homogeneous environments, namely a world without any disturbances or any kind of "hostility". Once this simple behaviour, named a primitive, has been established, the robot is trained to adapt new knowledge to cope with increasingly complex environments by adding further worlds to its existing knowledge. The repertoire of the robot behaviours in the form of symbolic knowledge is retained in a hierarchy of clustered decision trees (DTs) accommodating a number of primitives. To classify robot perceptions, control rules are synthesised using symbolic knowledge derived from searching the hierarchy of DTs. A second novel concept is introduced, namely that of multi-dimensional fuzzy associative memories (MDFAMs). These are clustered fuzzy decision trees (FDTs) which are trained locally and accommodate specific perceptual knowledge. Fuzzy logic is incorporated to deal with inherent noise in sensory data and to merge conflicting behaviours of the DTs. In this thesis, the feasibility of the developed techniques is illustrated in the robot applications, their benefits and drawbacks are discussed