Obstacle avoidance and finite-time tracking of mobile targets

In this paper we consider the finite time control of a simulated point mass robot with acceleration control to a target in an environment possessing obstacles that the robot must avoid. Both target and obstacles may be non-stationary. The algorithm presented combines classical Liapunov techniques and Terminal Sliding Modes to simlultaneosly achieve obstacle avoidance and finite time convergence - properties previously distinct to both methods

Handling constraint avoidance and finite time switching control for simulated mobile robots

In this paper we first consider the finite time control of a simulated point mass mobile robot with acceleration control to a target point within a closed square defined by constraint boundaries. It is shown how to incorporate the potential functions associated w'ith constraint boundaries directly into the sliding mode parameter terms of Liapunov function which yields a very simple controller for control to the target point and also ensure avoidance of the constraint boundaries. We also discuss the resulting 'sliding mode' surfaces and their role in enabling the point mass to be controlled to a specific target point. The analysis is extended to the simulated control of two point masses to respective targets within the square

Evolutionary learning of a fuzzy edge detection algorithm based on multiple masks

In this paper we demonstrate how an evolutionary algorithm (EA) can be employed to learn a fuzzy knowledge base (FKB) utilised in an edge detection algorithm based on the fuzzy paradigm. The proposed method calculates a fuzzy measure 'edginess' at each pixel of the image using masks of different sizes. Then the edge strengths calculated using these masks are used to form a fuzzy knowledge base which in turn is used to decide whether a given pixel belongs to an edge or not. When calculating the above mentioned fuzzy measures, the algorithm takes into account both step like edges and 'line edges' in the image being processed. The final edge map of a given image is produced by generating output pixel values 'non-linearly' proportional to the above mentioned fuzzy measure 'edginess'. The results are presented for several real and synthetic images to show the effectiveness of the proposed technique

Fuzzy document filter for the internet

Current rnajor search engines on the web retrieve too many documents, of which only a small fraction are relevant to the user query. We propose a new fuzzy document- filtering algorithm to filter out documents irrelevant to the user query from the output of Internet search engines. This algorithrn uses output of 'Google' search engine as the basic input and processes this input to filter documents most relevant to the query. The clustering algorithm used here is based on the fuzzy c-means with simple modifications to the membership function formulation and cluster prototype initialisation. It classifies input documents into 3 predefined clusters. Finally, clustered and context-based ranked URLs are presented to the user. The effectiveness of the algorithm has been tested using data provided by the eighth Text REtrieval Conference (TREC8) [25]and also with on-line data. Experimental results were evaluated by using error matrix method, precision, recall and clustering validity measures

Using evolved paths for control in robot soccer

In this paper we use an evolutionary algorithm to evolve paths using robot velocity profiles from an initial configuration to a configuration behind the ball. Physical conditions for wheel lift are incorporated as well as forward and reverse motion of the robot. a fuzzy clustering technique is used to build the fuzzy control knowledge base from optimised paths found for a large grid of initial configurations

Optimal control using fuzzy logic and evolutionary algorithms

In this paper we show how to use evolutionary algorithms to find a nonlinear, feedback, fuzzy controller to solve optimal control problems. In particular, we study the continuous stirred tank reactor optimal control problem and a problem in which the state equations contain a non-differentiable component of a rectangular pulse over a subinterval of the time range Results are compared with the iterative dynamic programming technique

A new multi-layered fuzzy image filter for removing impulse noise

In this paper we develop a fuzzy image filter which consists of a multi-layered fuzzy structure based on the weighted fuzzy blend filter for the removal of noise from images heavily corrupted by impulse noise, while preserving the intricate details of the image. The introduction of multi-layered fuzzy systems substantially decreases the number of rules to be learnt. We then show how Evolutionary Algorithms (EAs) can be used to effectively learn the fuzzy rules in each knowledge base. Results are presented for impulse noise corruption of the well-known ‘Lena’ image

Parameter optimisation and rule base selection for fuzzy impulse filters using evolutionary algorithms

In this paper we present an effective scheme for impulse noise removal from highly corrupted images using a soft-computing approach, The filter is capable of preserving the intricate details of the image and is based on a combination of fuzzy impulse detection and restoration of corrupted pixels. In the first stage a fuzzy knowledge base required for detection of impulses as well as the optimum parameters for the fuzzy membership functions employed, are effectively 'learnt" using an Evolutionary Algorithm (EA). For the detection of noisy pixels and the subsequent replacement, a novel scheme where a pixel is transferred to a simulated noise free environment is introduced. We present the results for several real images and make comparisons with some of the existing noise removal methods wherever applicable to show the effectiveness of the proposed technique

Multi-dimensional encoding to reduce bias in fuzzy knowledge-bases

This paper presents preliminary study into dimensional bias introduced when a naturaly multi-dimensional structure (eg. a fuzzy logic knowledge-base) is encoded as a single-dimensional string for use in an evolutionary algorithm. The evolutionary algorithm is modified to use a multi-dimensional encoding in favour over a single-dimensional string, preserving the multi-dimensional nature of a fully-specified fuzzy logic knowledge-base. Experiments show a clear benefit in function and data approximation applications, but show no differene in the chosen control application. Side benefits to using multi-dimensional encoding are foud that make it worth considering even if a particular problem does not suffer from significant dimensional bias

A coevolutionary approach for optimization of image enhancement filters

In this paper we demonstrate how Co-Evolutionary Algorithms (CEAs) can be employed for optimization of image enhancement filters. Specifically, we take the example of an impulse noise filter constructed using the fuzzy paradigm, and show how a CEA could be effectively used for its optimization. The fuzzy impulse filter we take results from our research where it was seen that the shape and the corresponding parameters of the membership functions used in the fuzzy inference process, play a major role in the quality of the enhanced image, apart from the proper selection of the fuzzy rule base. This is true, both in terms of objective and subjective evaluations of the processed image. During our experiments, we employed a CEA (having a blend of cooperativeness and competitiveness) to optimize the rule base and to select the best shape of the membership function used in the fuzzy inference process, and we present the results for several real images, to show the effectiveness of the proposed approach