This paper describes an algorithm to detect obstacles and landmarks, using the omnidirectional vision system of a RoboCup robot, to build an internal representation of the robot’s environment. The restriction to pixels corresponding to an equally spaced grid on the floor around the robot and a biologically inspired fault-tolerant colour segmentation of this grid result in a fast and robust dete...

This paper presents the design, development and implementation of a Dynamic Fuzzy Neural Networks (D-FNNs) Controller suitable for real-time industrial applications. The unique feature of the D-FNNs controller is that it has dynamic self-organising structure, fast learning speed, good generalisation and flexibility in learning. The approach of rapid prototyping is employed to implement the D-FN...

Towards autonomous 3D modelling of moving targets, we present a system where multiple ground-based robots cooperate to localize, follow and scan from all sides a moving target. Each robot has a single camera as its only sensor, and they perform collaborative visual SLAM (CoSLAM). We present a simple robot controller that maintains the visual constraints of CoSLAM while orbiting a moving target ...

This study proposes an approach, the unit circle (UC), to qualitative modelling of a planar robot. A robot is described as a collection of constraints holding among time-varying, interval-valued variables. The UC representation is presented, the continuous motion of the end-effector is evaluated by the change of directions of qualitative angle and qualitative length. Analytical formulas of qual...

This paper presents a new neuro-fuzzy controller for robot manipulators. First, an inductive learning technique is applied to generate the required modelling rules from input/output measurements recorded in the off-line structure learning phase. Second, a fully differentiable fuzzy neural network is developed to construct the inverse dynamics part of the controller for the on-line parameter lea...