Navigation of autonomous mobile robots in dynamic and unknown environments needs to take into account different kinds of uncertainties. Type-1 fuzzy logic research has been largely used in the control of mobile robots. However, type-1 fuzzy control presents limitations in handling those uncertainties as it uses precise fuzzy sets. Indeed type-1 fuzzy sets cannot deal with linguistic and numerical uncertainties associated with either the mechanical aspect of robots, or with dynamic changing environment or with knowledge used in the phase of conception of a fuzzy system. Recently many researchers have applied type-2 fuzzy logic to improve performance. As control using type-2 fuzzy sets represents a new generation of fuzzy controllers in mobile robotic issue, it is interesting to present the performances that can offer type-2 fuzzy sets by regards to type-1 fuzzy sets. The paper presented deep and new comparisons between the two sides of fuzzy logic and demonstrated the great interest in controlling mobile robot using type-2 fuzzy logic. We deal with the design of new controllers for mobile robots using type-2 fuzzy logic in the navigation process in unknown and dynamic environments. The dynamicity of the environment is depicted by the presence of other dynamic robots. The performances of the proposed controllers are represented by both simulations and experimental results, and discussed over graphical paths and numerical analysis.

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Interval Type-2 Fuzzy Logic Control of Mobile Robots

Motion planning of mobile robots in unknown and dynamic environments is faced with a large amount of uncertainties. Such those uncertainties we find; the measurement noise, the membership function's translations, data uncertainties... In fact, the known type of fuzzy logic (FL), Type-1, gave some solutions. But, in the last few years, new trends and theory in FL have been appeared, proposing by thus the Type-2 Fuzzy Logic Systems (Type-2 FLSs) which can handle and minimize the effects of the cited uncertainties with a better performance. This paper deals with the design of an Interval Type-2 fuzzy logic controller for the navigation of mobile robots in unknown and dynamic environments. The obtained results are presented and are compared with the navigation using the Type-1 Fuzzy Logic system. The Type-2 FLSs provide very good results and outperform the correspondent Type-1 FLS.

Motion Planning in Dynamic and Unknown Environment Using an Interval Type-2 TSK Fuzzy Logic Controller

Based on our earlier work on construction of fuzzy controllers with B-spline models, we propose an automatic learning approach for generating control vertices of such a type of fuzzy controller. For supervised learning, we point out that rapid convergence of this learning procedure can be guaranteed, which is confirmed by diverse examples of approximating nonlinear functions and interpolating training data. For unsupervised learning, we employ a type of state evaluation functions which can be found for a large amount of control problems. Using such an evaluation function, a learning algorithm is devised which modifies the local control action efficiently to guide the system to the desired state. Implementations with the cart-pole balancing and a sensor-based mobile robot validate this learning approach.

Unsupervised learning of control surfaces based on B-spline models

Kgroo, a simulated 3-link folding legged uniped robot is presented and locomotion training of Kgroo with fuzzy-neural control is discussed. It is observed that for the uniped locomotion problem, global training of a fuzzy or neural controller is subject to failure. It is shown that, starting with a single jump example, a multiagent cerebellum model (MAC-J) can enable Kgroo to learn different jumps with a geometrical learning rate based on a learning-tuning-brainstorming theory. Technically, this work introduces effective means for decomposing the high-dimensional locomotion control problem into kernel spaces; theoretically, incremental learning and coordinated cerebellar agent discovery provide a natural explanation to certain explosive learning behaviors in human and animal locomotion control.

Multiagent fuzzy-neural control of a 3-link uniped

We present an online learning approach for developing sensor-based controllers and show how it can make the programming of assembly tasks easier. We suggest combining the qualitative modelling of human expert skills and the self-tuning of control parameters so that a controller for a complex assembly task can be efficiently developed. It is then discussed how to construct a fuzzy controller with B-splines and why rapid convergence of its learning can be achieved. To apply the concept in a screwing operation, we propose several gradual steps of active online learning. Experiments were carried out with two independently controlled robot arms. Although general-purpose jaw-grippers are used and diverse uncertainties exist, the "elevator control" of a toy aircraft can be robustly built.

Online learning of B-spline fuzzy controller to acquire sensor-based assembly skills

This work presents the concept and realisation of the integration of deliberative and reactive strategies for controlling mobile robot systems. Programming at the task-level in a partially-known environment is divided into two consecutive steps: subgoal planning and subgoal-guided plan execution. A modular fuzzy control scheme is proposed, which allows independent development and flexible integration of different rule bases, each for fulfilling a certain subtask. The design process of the fuzzy controller is demonstrated with the examples of three rule bases.

Modular design of fuzzy controller integrating deliberative and reactive strategies

We use fuzzy logic rules to directly map sensor data to robot control outputs by classifying a set of typical subtasks, such as "path tracking", "local collision avoidance", "contour tracking", "situation evaluation", etc. With the help of existing heuristics, the decision-making process for each subtask can be modelled and represented with "IF-THEN" rules. The underlying concepts of mapping with fuzzy logic rules are briefly explained by considering the proximity sensors, the control of speed and steering angle of a mobile robot. The development of these fuzzy rules is explained, typical rules for dealing with various motion situations are listed. The modularly developed fuzzy rule bases can be integrated to realise task-level programming and the exploration task. Experiments with the mobile robot validate this concept.

Fuzzy logic rules for mapping sensor data to robot control

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Modular design of fuzzy controller integrating deliberative and reactive strategies

Fuzzy logic rules for mapping sensor data to robot control