Ionic polymer metal composite (IPMC) is a highly innovative material that has recently gained attention in many fields such as medical, biomimetic, and micro/nano underwater applications. The main characteristic of IPMC lies in its ability to achieve a large deflection under a fairly low driving voltage. Moreover, its agile, light weight, noiseless and flexible features render it well suited for certain specific applications. Like other smart materials, such as piezoelectric ceramics, IPMC could be used in actuators or sensors. In this paper, we study the application of IPMC as an actuator for underwater use. The goal is to develop a robust feedback controller for the IPMC actuator to track a desired reference whilst dealing with the uncertainties due to the inherent actuator nonlinearity, external disturbance or the variations of working environment. To this end, we first present a nominal model of the IPMC actuator through experimental identification. Next, a nonsingular terminal sliding mode controller is proposed. Lastly, experimental studies are conducted to verify the tracking accuracy and robustness of the designed controller.

No abstract

In this study, we developed an Ant Colony Optimization (ACO) - Genetic Algorithm (GA) hybrid approach for solving the Multi objectives Optimization global path planning (MOPP) problem of mobile robot. The ACO optimization algorithm is used to find the sub-optimal collision free path which then used as initial population for GA. In the proposed modified genetic algorithms, specific genetic operator such as deletion operator is proposed, which is based on domain heuristic knowledge, to fit the optimum path planning for mobile robots. The objective of this study is improving GA performance for efficient and fast selection in generating the Multi objective optimal path for mobile robot navigation in static environment. First we used the proposed approach to evaluate its ability to solve single objective problem in length term as well as we compared it with traditional ACO and simple GA then we extended to solve Pareto optimality ideas based on three criteria: length, smoothness and security, and making it Multi objective Hybrid approach. The proposed approach is tested to generate the single and multi objective optimal collision free path. The simulation results show that the mobile robot travels successfully from one location to another and reaches its goal after avoiding all obstacles that are located in its way in all tested environment and indicate that the proposed approach is accurate and can find a set Pareto optimal solution efficiently in a single run.

A new hybrid approach algorithm based on modified Genetic Algorithm (GA) and modified the search algorithm (A*) and has been developed to solve the Multi objectives global path planning (MOPP) problem for mobile robot navigation in complex environment with static distributed obstacles. The aim of this combination is to improve GA efficiency and path planning performance. Hence, several genetic operators are proposed based on domain-specific knowledge and characteristics of path planning to avoid falling into a local minimum in complex environment and to improve the optimal path partly such as deletion operator and enhanced mutation with basic A*. In addition, the proposed approach is received an initial population from a classical method or modified A*. The objective function for the proposed approach is to minimizing travelling distance, smoothness and security, without collision with any obstacle in the robot workspace. The simulation results show that the proposed approach is able to achieve multi objective optimization in complex static environment efficiently. Also, it has the ability to find a solution when the environment is complex and the number of obstacles is increasing.  Index Terms-multi objectives optimization, path planning, mobile robot, static complex environment, GA, A*.