Neural Network for the Behavioral Navigation of a Mobile Robot

2015

This project presents a neural network approach to the motion control of an artificial mobile robot. The robot is required to move towards its goal in a cluttered environment and simultaneously avoiding collision with obstacles of any size and being randomly distributed. We have used feed-forward back propagation algorithm to control the robot motion. Our main focus is on the steering action of the robot. The algorithm takes input as angle to target, front obstacle distance, right obstacle distance and left obstacle distance. Sensors have been used for the purpose of getting the inputs on various obstacle distances. The network provides the angle through which the robot is to be steered to avoid obstacles. The steering is achieved by controlling the speed of motors and hence of wheels to whom they are attached to. The viability of the algorithm has been demonstrated through various simulations. The real-world experimental outcomes prove the efficacy of the algorithm to navigate the ...

A combination of three separate neural network modules is employed to deal with the mobile robot control problem, giving very promising results. At the lower level, a neuro-fuzzy architecture, trained by reinforcement learning, steers the robot such that to avoid obstacles, exploiting ultrasonic sensor readings, and head to a target, given the heading error. ln the second and most important level, a topologically ordered Hopfield neural network performs global path finding in real time, using an environment map arranged on the fly. The third level involves an ordinary Hopfield neural network, used as an associative memory, which tries to match and complete the currently observed environment with one of the stored ones. Computer simulation validates the efficiency of the approach and shows its potential benefits.

Proceeding of the Electrical Engineering Computer Science and Informatics, 2017

Mobile robot are widely applied in various aspect of human life. The main issue of this type of robot is how to navigate safely to reach the goal or finish the assigned task when applied autonomously in dynamic and uncertain environment. The application of artificial intelligence, namely neural network, can provide a "brain" for the robot to navigate safely in completing the assigned task. By applying neural network, the complexity of mobile robot control can be reduced by choosing the right model of the system, either from mathematical modeling or directly taken from the input of sensory data information. In this study, we compare the presented methods of previous researches that applies neural network to mobile robot navigation. The comparison is started by considering the right mathematical model for the robot, getting the Jacobian matrix for online training, and giving the achieved input model to the designed neural network layers in order to get the estimated position of the robot. From this literature study, it is concluded that the consideration of both kinematics and dynamics modeling of the robot will result in better performance since the exact parameters of the system are known. Index Terms-Dynamics modeling; kinematics modeling; mobile robot navigation; neural network controller.

This paper presents a novel reactive navigation algorithm for wheeled mobile robots under non-holonomic constraints and in unknown environments. Two techniques are proposed: a geometrical based technique and a neural network based technique. The mobile robot travels to a pre-defined goal position safely and efficiently without any prior map of the environment by modulating its steering angle and turning radius. The dimensions and shape of the robot are incorporated to determine the set of all possible collision-free steering angles. The algorithm then selects the best steering angle candidate. In the geometrical navigation technique, a safe turning radius is computed based on an equation derived from the geometry of the problem. On the other hand, the neural-based technique aims to generate an optimized trajectory by using a user-defined objective function which minimizes the traveled distance to the goal position while avoiding obstacles. The experimental results demonstrate that the algorithms are capable of driving the robot safely across a variety of indoor environments.

Seventh International Conference on Intelligent Systems Design and Applications (ISDA 2007), 2007

This paper presents novel hybrid architecture of control system of mobile robot oriented on navigation tasks and based on hybrid neural network MLP-ART2. Using of this model allows movement to target avoiding the obstacles almost without assistance of human operator after just small learning.

Neural network based systems have been used in past years for robot navigation applications because of their ability to learn human expertise and to utilize this knowledge to develop autonomous navigation strategies. In this paper, neural based systems are developed for mobile robot reactive navigation. The proposed systems transform sensors' input to yield wheel velocities. Novel algorithm is proposed for optimal training of neural network. With a view to ascertain the efficacy of proposed system; developed neural system's performance is compared to other neural and fuzzy based approaches. Simulation results show effectiveness of proposed system in all kind of obstacle environments.

1995

rlcccntly it. has been introduced a neural controller for a mobile rohot.llm!.learns both forward aml inverse odomctry of a dlffercnt.ial-drivc robot through an unsupervised learning-bydoing cycle. This article introduces an obst.aclc avoidance module that is integrated into the neural controller. This module makes use of sensory informat.ion to determine at each instant. a cksircd cmgle and distance that causes the robot t.o navigate around obstacles on the wa.y Lo a final LaJ•get. Obstacle avoidance is performed in a reactive manner by representing the objects and target. in the robot's environment. as Gaussian funct.ions. However, t.he influence of the GaussiaJJS is modulated dynamically on the basis of the robol's behavior ill a way that avoids problems with local minjma. The proposed module enables the robot to operate successfully with dHferent obstacle configurations, such as corridors, nla%cs, doors and even concave obstacles.

Computational and …, 2007

A neural architecture that makes possible the integration of a kinematic adaptive neuro-controller for trajectory tracking and an obstacle avoidance adaptive neuro-controller is proposed for nonholonomic mobile robots. The kinematic adaptive neuro-controller is a real-time, unsupervised neural network that learns to control a nonholonomic mobile robot in a nonstationary environment, which is termed Self-Organization Direction Mapping Network (SODMN), and combines associative learning and Vector Associative Map (VAM) learning to generate transformations between spatial and velocity coordinates. The transformations are learned in an unsupervised training phase, during which the robot moves as a result of randomly selected wheel velocities. The robot learns the relationship between these velocities and the resulting incremental movements. The obstacle avoidance adaptive neuro-controller is a neural network that learns to control avoidance behaviors in a mobile robot based on a form of animal learning known as operant conditioning. Learning, which requires no supervision, takes place as the robot moves around an cluttered environment with obstacles. The neural network requires no knowledge of the geometry of the robot or of the quality, number, or configuration of the robot's sensors. The efficacy of the proposed neural architecture is tested experimentally by a differentially driven mobile robot.

2014

Over recent years, automated mobile robots play a crucial role in various navigation operations. For any mobile device, the capacity to explore in its surroundings is essential. Evading hazardous circumstances, for example, crashes and risky conditions (temperature, radiation, presentation to climate, and so on.) comes in the first place, yet in the event that the robot has a reason that identifies with particular places in its surroundings, it must discover those spots. There is an increment in examination here due to the requisition of mobile robots in a solving issues like investigating natural landscape and assets, transportation tasks, surveillance, or cleaning. We require great moving competencies and a well exactness for moving in a specified track in these requisitions. Notwithstanding, control of these navigation bots get to be exceptionally troublesome because of the exceedingly unsystematic and dynamic aspects of the surrounding world. The intelligent reply to this issue ...

Proceedings of the International conference on …, 2009

This paper deals with the reactive control of an autonomous robot which move safely in a crowded real world unknown environment and to reach specified target by avoiding static as well as dynamic obstacle. The inputs to the proposed neurocontroller consist of left, right, and front obstacle distance to its locations and target angle between a robot and a specified