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It has been an ultimate long‐term dream in robotics and AI fields to build robotic systems with life‐like appearance, behaviours and intelligence, reflected by many science fiction books and films. This is also an extremely challenging task. This paper introduces our current research efforts to build a multi‐agent system for cooperation and learning of multiple life‐like robots in the RoboCup domain. A behaviour‐based hierarchy is proposed for the Essex Rovers robot football team to achieve intelligent actions in real time, which includes both a neural network‐based color detection algorithm and a fuzzy logic controller. Preliminary results based on legged locomotion experiments of Sony walking robots are presented.

Landmark‐based navigation of autonomous mobile robots or vehicles has been widely adopted in industry. Such a navigation strategy relies on identification and subsequent recognition of distinctive environment features or objects that are either known a priori or extracted dynamically. This process has inherent difficulties in practice due to sensor noise and environment uncertainty. This paper is to propose a navigation algorithm that simultaneously locates the robots and updates landmarks in a manufacturing environment. A key issue being addressed is how to improve the localization accuracy for mobile robots in a continuous operation, in which the Kalman filter algorithm is adopted to integrate odometry data with scanner data to achieve the required robustness and accuracy. The Kohonen neural networks have been used to recognize landmarks using scanner data in order to initialize and recalibrate the robot position by means of triangulation when necessary.

Robots operating in the real world should be able to make decisions and plan ahead their actions. We argue that learning using generalized representations of the robot's experience can assist such a ability.

We present results from our research on methods for enabling mobile robots to plan their actions using generalized representations of their experience. Such generalized representations are acquired through a learning phase during which the robot explores its environment and builds subsymbolic (connectionist) representations of the result that its actions have to its sensory perception. Then these representations are employed by the robot for autonomously determining task‐achieving sequences of actions (plans),for attaining assigned tasks.

Such subsymbolic mechanisms can employ generalization techniques in order to pursue plans through unexplored regions of the robot's environment.

Subsymbolic motion planning can autonomously determine task‐achieving sequences of actions in real environments, without using presupplied symbolic knowledge, but instead generating novel plans using previously acquired subsymbolic representations.

Synthetic aperture radar exploits the receiving signals in the antenna for detecting the moving targets and estimates the motion parameters of the moving objects. The limitation of the existing methods is regarding the poor power density such that those received signals are essentially to be transformed to the background ratio. To overcome this issue, fractional Fourier transform (FrFT) is employed in the moving target detection (MTD) process. The paper aims to discuss this issue.

The proposed MTD method uses the fuzzy decisive approach for detecting the moving target in the search space. The received signal and the FrFT of the received signal are subjected to the calculation of correlation using the ambiguity function. Based on the correlation, the location of the target is identified in the search space and is fed to the fuzzy decisive module, which detects the target location using the fuzzy linguistic rules.

The simulation is performed, and the analysis is carried out based on the metrics, like detection time, missed target rate, and MSE. From the analysis, it can be shown that the proposed Fuzzy-based MTD process detected the object in 5.0237 secs with a minimum missed target rate of 0.1210 and MSE of 23377.48.

The proposed Fuzzy-MTD is the application of the fuzzy rules for locating the moving target in search space based on the peak energy of the original received signal and FrFT of the original received signal.