In this paper we develop two time-invariant control laws for a unicycle-type mobile robot. A mobile robot of this type is an example of a system with a nonholonomic constraint. Similarly to the majority of results in the literature thus far, the controllers are based on the robot's kinematic model. They do not directly address realistic factors such as motor dynamics, quantization, sensor noise...

This paper provides a new approach to the multi-robot path planning problem predicting the position of a dynamic obstacle which undergoes linear motion in the given workspace changing its direction at regular intervals of time. The prediction is done in order to avoid collision of the robots with the dynamic obstacle. First the work is done in simulation environment then the entire work has bee...

The Autonomous Robotics and Control Systems Laboratory at the University of Washington has developed a testbed consisting of seven K-Team Khepera II miniature robots, a global vision system and a custom-designed infrared communication structure. The purpose of the testbed is to provide a platform for the real-world testing of distributed and centralized control algorithms for groups of autonomo...

We present the results of a research aimed at improving the Q-learning method through the use of artificial neural networks. Neural implementations are interesting due to their generalisation ability. Two implementations are proposed: one with a competitive multilayer perceptron and the other with a self-organising map. Results obtained on a task of learning an obstacle avoidance behaviour for ...

An evolutionary algorithm for the creation of recurrent network structures is presented. The aim is to develop neural networks controlling the behaviour of miniature robots. Two diierent tasks are solved with this approach. For the rst, the agents are required to move within an environment without colliding with obstacles. In the second task, the agents are required to move towards a light sour...

In this paper we describe a fuzzy logic based approach for providing biologically based motivations to be used in evolutionary mobile robot learning. Takagi-Sugeno-Kang (TSK) fuzzy logic is used to motivate a small mobile robot to acquire complex behaviors and to perform environment recognition. This method is implemented and tested in behavior based navigation and action sequence based environ...

We describe a hippocampal neural model in which spatio-temporal features of the environment are extracted by visually driven neu-rons. The neuronal ring activity implicitly measures properties like agent-landmark distance and egocentric orientation to visual cues. This leads to a neural representation where populations of place cells encode spatial locations within the environment. In addition,...

We have used an automatic programming method called genetic programming (GP) for control of a miniature robot. Our earlier work on real-time learning suffered from the drawback of the learning time being limited by the response dynamics of the robot's environment. In order to overcome this problem we have devised a new technique which allows learning from past experiences that are stored in mem...

We present a biologically motivated computational model that is able to anticipate and evaluate multiple hypothetical sensorimotor sequences. Our Model for Anticipation based on Cortical Representations (MACOR) allows a completely parallel search at the neocortical level using assemblies of rate coded neurons for grouping, separation, and selection of sensorimotor sequences. For a vision-contro...

We propose a bio-inspired approach to autonomous navigation based on some of the components that rats use for navigation. A spatial model of the environment is constructed by unsupervised Hebbian learning. The representation consists of a population of localized overlapping place elds, modeling place cell activity in the rat Hippocampus. Place elds are established by extracting spatio-temporal ...