Robotic path planning using rapidly-exploring random trees

This study concerns the implementation of Rapidly-Exploring Random Trees (RRTs) algorithm for an autonomous robot path planning. RRTs possesses a number of advantages such as relatively simple, suitable for finding a path for a robot with dynamic and physical constraints, the expansion of RRT is heavily biased toward unexplored areas of search space and the number of edges is minimal. However, the planned path by using basic RRT structure might not always be optimal in terms of path length. Therefore, a path pruning method has been proposed to address this issue and improve the overall performance of the RRTs. Through simulations, the path pruning method has been proven to reduce paths lengths while preserving the aforementioned advantages of RRTs. A Graphical User Interface (GUI) has also been developed to demonstrate the RRTs technique in planning a path for an autonomous robot. The GUI package is designed to be interactive and user�friendly even for the users with minimal or no guidance and practice

An improved data classification framework based on fractional particle swarm optimization

Particle Swarm Optimization (PSO) is a population based stochastic optimization technique which consist of particles that move collectively in iterations to search for the most optimum solutions. However, conventional PSO is prone to lack of convergence and even stagnation in complex high dimensional-search problems with multiple local optima. Therefore, this research proposed an improved Mutually-Optimized Fractional PSO (MOFPSO) algorithm based on fractional derivatives and small step lengths to ensure convergence to global optima by supplying a fine balance between exploration and exploitation. The proposed algorithm is tested and verified for optimization performance comparison on ten benchmark functions against six existing established algorithms in terms of Mean of Error and Standard Deviation values. The proposed MOFPSO algorithm demonstrated lowest Mean of Error values during the optimization on all benchmark functions through all 30 runs (Ackley = 0.2, Rosenbrock = 0.2, Bohachevsky = 9.36E-06, Easom = -0.95, Griewank = 0.01, Rastrigin = 2.5E-03, Schaffer = 1.31E-06, Schwefel 1.2 = 3.2E-05, Sphere = 8.36E-03, Step = 0). Furthermore, the proposed MOFPSO algorithm is hybridized with Back-Propagation (BP), Elman Recurrent Neural Networks (RNN) and Levenberg-Marquardt (LM) Artificial Neural Networks (ANNs) to propose an enhanced data classification framework, especially for data classification applications. The proposed classification framework is then evaluated for classification accuracy, computational time and Mean Squared Error on five benchmark datasets against seven existing techniques. It can be concluded from the simulation results that the proposed MOFPSO-ERNN classification algorithm demonstrated good classification performance in terms of classification accuracy (Breast Cancer = 99.01%, EEG = 99.99%, PIMA Indian Diabetes = 99.37%, Iris = 99.6%, Thyroid = 99.88%) as compared to the existing hybrid classification techniques. Hence, the proposed technique can be employed to improve the overall classification accuracy and reduce the computational time in data classification applications

Rehabilitation system for paraplegic patients using mind machine interface; a conceptual framework

Mind-Machine Interface (MMI) is a newly surfaced term in the field of control engineering and rehabilitation systems. This technique, coupled with the existing functional electrical stimulation (FES) systems, can be very beneficial for effective rehabilitation of disabled patients. This paper presents a conceptual framework for the development of MMI based FES systems for therapeutic aid and function restoration in spinal cord injured (SCI) paraplegic patients. It is intended to acquire thought modulated signals from human brain and then use these signals to command and control FES as desired by the patient. The proposed setup can significantly assist the rehabilitation and recovery of paraplegic patients due to the ease of control for the user