This paper presents a parallel elite genetic algorithm (PEGA) and its application to global path planning for autonomous mobile robots navigating in structured environments. This PEGA, consisting of two parallel EGAs along with a migration operator, takes advantages of maintaining better population diversity, inhibiting premature convergence, and keeping parallelism in comparison with conventional GAs. This initial feasible path generated from the PEGA planner is then smoothed using the cubic B-spline technique, in order to construct a near-optimal collision-free continuous path. Both global path planner and smoother are implemented in one field-programmable gate array chip utilizing the system-on-a-programmable-chip technology and the pipelined hardware implementation scheme, thus significantly expediting computation speed. Simulations and experimental results are conducted to show the merit of the proposed PEGA path planner and smoother for global path planning of autonomous mobile robots.

Parallel Elite Genetic Algorithm and Its Application to Global Path Planning for Autonomous Robot Navigation

In this study we present our initial idea for using genetic algorithms to help a controllable mobile robot to find an optimal path between a starting and ending point in a grid environment. The mobile robot has to find the optimal path which reduces the number of steps to be taken between the starting point and the target ending point. GAs can overcome many problems encountered by traditional search techniques such as the gradient based methods. The proposed controlling algorithm allows four-neighbor movements, so that path-planning can adapt with complicated search spaces with low complexities. The results are promising.

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A Mobile Robot Path Planning Using Genetic Algorithm in Static Environment

The theme of this paper is to design a real-time fuzzy target tracking control scheme for autonomous mobile robots by using infrared sensors. At first two mobile robots are setup in the target tracking problem, where one is the target mobile robot with infrared transmitters and the other one is the tracker mobile robot with infrared receivers and reflective sensors. The former is designed to drive in a specific trajectory. The latter is designed to track the target mobile robot. Then we address the design of the fuzzy target tracking control unit, which consists of a behavior network and a gate network. The behavior network possesses the fuzzy wall following control (FWFC) mode, fuzzy target tracking control (FTTC) mode, and two fixed control modes to deal with different situations in real applications. Both the FWFC and FTTC are realized by the fuzzy sliding-mode control scheme. A gate network is used to address the fusion of measurements of two infrared sensors and is developed to recognize which situation is belonged to and which action should be executed. Moreover, the target tracking control with obstacle avoidance is also investigated in this paper. Both computer simulations and real-time implementation experiments of autonomous target tracking control demonstrate the effectiveness and feasibility of the proposed control schemes.

Fuzzy target tracking control of autonomous mobile robots by using infrared sensors

Synchronization of uncertain chaotic systems based on adaptive type-2 fuzzy sliding mode control

This paper presents a hybrid metaheuristic GA (genetic algorithm)-PSO (particle swarm optimization) algorithm for autonomous robot navigation to find an optimal path between a starting and ending point in a grid environment. GA has been combined with PSO in evolving new solutions by applying crossover and mutation operators on solutions constructed by particles. This hybrid algorithm avoids the premature convergence and time complexity in conventional GA and PSO algorithms. The initial feasible path generated from the hybrid GA-PSO planner is then smoothed using the cubic B-spline technique, in order to construct a near-optimal collision-free continuous path. Simulation results are conducted to show the merit of the proposed hybrid GA-PSO path planner and smoother for global path planning of autonomous robot navigation.

Global path planning for autonomous robot navigation using hybrid metaheuristic GA-PSO algorithm

In this paper, the authors present an effective method to achieve both obstacle-avoidance and target-tracking for an autonomous mobile robot in an indoor environment. They employ a wall following algorithm using neural network pattern recognition to avoid obstacles. An autonomous mobile robot reaches a given goal target by tracking algorithm. In case obstacles are detected by sonar sensors, an autonomous mobile robot avoids collision with obstacles by wall following algorithm. They propose a simple making method to avoid being trapped in a local minima which was a serious problem in local path planning. Simulation results using mobile robot demonstrate that the proposed algorithms are well suited to the obstacle-avoidance using wall-following and the path planning task for target-tracking.

Path planning of mobile robot using neural network

The direct torque control (DTC) scheme provides very quick torque response without the complex field-orientation block and inner current control loop. DTC is known as an appropriate scheme for high power induction motor drives because it can be used at lower switching frequency. There are two major drawbacks with the application of DTC schemes : flux drooping in low speed range and time varying inverter switching frequency. In this paper, a fuzzy variable switching sector scheme to prevent flux drooping, and a fuzzy switching frequency regulator for lowering the switching frequency are proposed and adopted in the DTC based induction motor drive system. An important contribution of this paper is the proposition of simple realization schemes of the fuzzy switching frequency regulator and the fuzzy variable switching sector.

Direct torque control of induction motors using fuzzy variable switching sector

In this paper, a decoupled fuzzy sliding-mode control design scheme is presented through width adaptation for a class of fourth-order nonlinear systems. Each subsystem, which is decoupled into two second-order systems, is said to have main and sub control purpose. Two sliding surfaces are constructed through the state variables of the decoupled subsystem. We define main and sub target condition for these sliding surfaces, and introduce an intermediate variable from the sub sliding surface condition. The proposed adaptation law, which results from the indirect adaptive approach, is used to appropriately determine the width of the unknown system variables. And the membership functions in the THEN part will vary with the width adaptation. An adaptive law is then used to tune the width in the THEN part to appropriately determine the distribution of each membership function. The main advantage of the proposed method is that the structure of fuzzy controller dose not need to be changed while using a common design procedure and the computing time may be reduced considerably. Finally, a nonlinear system simulation example is shown to verify the effectiveness of the proposed adaptive fuzzy-sliding mode controller.

https://ieeexplore.ieee.org/iel5/8665/27471/01223263.pdf

An indirect adaptive fuzzy sliding-mode control for decoupled nonlinear systems

In this paper, three controllers are considered for a rotational inverted pendulum system (RIPS). The control objectives are to position the pendulum at the upright position and to regulate it at the specified position. The fuzzy control is an effective way to achieve the control objectives in a nonlinear system such as the RIPS. PID controller, fuzzy PID controller and adaptive fuzzy controller are proposed to obtain increased control performance and stability. This paper consists of three parts: (1) modeling of the RIPS, (2) introduction of three control methods for the control of the system, and (3) making a prototype of the system and experimental environment to compare the performances of three controllers.

Soon-Chan Hong

Papers

Path planning of mobile robot using neural network

Direct torque control of induction motors using fuzzy variable switching sector

An indirect adaptive fuzzy sliding-mode control for decoupled nonlinear systems

An implementation of a rotational inverted pendulum using adaptive fuzzy controllers