Neural Network Methods

Koh et al. [91] have presented a neural network based navigation system for a mobile robot in indoor environments. They mainly dealt with the three issues, which are the determination of the current position and heading angle, path control in order to follow the desired path and local path planning for uncertain environments. The fuzzy logic and

reinforcement learning are used for obstacle avoidance and vision system, and the neural network is used for path tracking control. Explanation based neural network learning algorithm for solving the navigational problem of a mobile robot in an indoor environment has been addressed by Thrun [92]. Mobile robot navigation using feed forward neural network has been addressed by Pal and Kar [93]. They have trained the neural network with sonar range inputs to obtain steering angle for the robot. Obstacle avoidance and path planning of mobile robot using an analog neural network dynamics have been proposed by Glasius et al. [94]. The neural network receives inputs, then the neuron in the controller starts to change their activity towards a specific value. The path is framed using neural activity gradient by adjusting the direction of the motor response. An efficient neural network navigational controller for real time motion planning of a mobile robot has been proposed by Yang and Meng [95]. The proposed controller is planned through the biologically inspired neural network without any prior information about the environment and without any learning process. Path planning and obstacle avoidance for a mobile robot using artificial neural network has been studied by Zarate et al. [96]. They proposed a path planner, which is based on the neural memorization of a path previously planned and it allows to move the robot from its start position to target by avoiding obstacles. Yao et al. [97] have presented the RAM based neural controller in mobile robotics to detect and avoid obstacles in real time. Janglova [98] has presented a new path planning strategy for an autonomous mobile robot in partially unstructured environments using neural networks. She used two neural networks; one is used to construct the free space using ultrasonic sensors, and other is used to find the safe direction of the robot while avoiding the nearest obstacle. Navigation strategy for a mobile robot using a probabilistic neural network (PNN) has been addressed by Castro et al. [99]. They implemented the proposed methodology in a real prototype robot, and the result obtained validates its feasibility. Wahab [100] has developed a new navigational system for a mobile robot based on the artificial neural network. He used two neural networks, one neural network for creating free space to avoid obstacles and the other is for navigating the robot towards the target. Behaviour control of a mobile robot using artificial neural network has been proposed by Leon et al. [101]. They designed several behavior modules for a mobile robot based on the neural network paradigms. The developed neuro- controller has been validated through simulation results and tested on a Khepera robot. Mobile robot navigation in dynamic environments using heuristic rule based neural

networks has been addressed by Parhi and Singh [102]. They hybridized the heuristic rules with a neural network to create the required mapping between perception and motion. Singh and Parhi [103] have developed a path optimization algorithm for a mobile robot based on the neural network. They used four layer neural networks with back propagation algorithm to solve the path optimization problem of mobile robots. Motion and path planning of a mobile robot using neural network approach has been proposed by Engedy and Horvath [104]. They used back propagation through time (BPTT) training approach to train the neural network. Mobile robot path planning using neural network has been discussed by Mahmud et al. [105]. They applied the Kohonen type concurrent self-organizing map (CSOM) to determine the correct steering direction of the robot. Mobile robot navigation using the recurrent neural network (RNN) has been addressed by Brahmi et al. [106]. They used two RNN connected in series to control the motion of the mobile robot. Local path planning of a mobile robot using neural network strategy has been discussed by Motlagh et al. [107] shown in Figure 2.9. In this study, they utilized the neural network and reinforcement learning to enable the robot to learn environments on its own.

Cao et al. [108] have presented a spiking neural network based path planner for the autonomous mobile robot. They used CCD cameras, encoders and ultrasonic sensors into spike train, which are embedded in the three layered spiking neural network to give motion to each motor.

Figure 2.9 Neural Network approach for Mobile robot navigation proposed by Motlagh et al. [107]. OL OF OR ON TL TR LWH LWL RWH RWL W11 W12 Wij . . . W63 W64

OL-Obstacle at left, LWL-Left wheel velocity Low

OF-Obstacle at front, LWH-Left Wheel Velocity High

OR-Obstacle at right, RWL-Right Wheel Velocity Low

ON- No Obstacle RWH-Right Wheel Velocity High TL-Target at left