Navigation of multiple mobile robots in a highly clutter terrains using adaptive neuro-fuzzy inference system

Motion planning is one of the most important parts when we design any wheeled or walking robots. The meaning of motion planning is to plan a path from start point to goal point between obstacles in any working environment. Many techniques have applied by researchers from last two-three decades for wheeled robot motion planning. This article presents a critical review on motion planning of wheeled robot by using different nature-inspired techniques like evolutionary algorithm and swarm-based optimization algorithm.

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Different Nature-Inspired Techniques Applied for Motion Planning of Wheeled Robot: A Critical Review

This paper concentrates on the recognition of a navigational algorithm, which can resolve path optimization challenges in minimum time. This navigational algorithm helps the mobile robot to avoid collision with obstacles. Implementation of spider monkey optimization (SMO) algorithm is carried out to explain the objectives of this paper. SMO algorithm is established upon the splitting-combination nature of spider monkeys. The proposed algorithm is examined in various terrains to determine the robustness and effectuality of the proposed algorithm. Simulation results confirmed that spider monkey optimization algorithm selects a path, which guides the robot to reach the target in minimum possible time. Subsequently, it decreases the computational cost, as the time for computation is minimized. In extension, we replicated the previously used environment, and the proposed algorithm is compared with the corresponding implemented algorithm. Our results demonstrate that Spider monkey optimization (SMO) provides better results on the basis of time by selecting a path with lesser complexity. To determine the adequacy of the prescribed standard route-outlining control design, the common element of this paper is the numerical simulation.

Spider Monkey Optimization Algorithm Based Collision-Free Navigation and Path Optimization for a Mobile Robot in the Static Environment

This article discusses a Neurofuzzy navigation strategy for sensor-based mobile robotics system. ATransputer computation power is used to carry out complicated needed computation (reading sensors data, deciding actions, outputting wheels data, ... system monitoring). Robot control mythology was run on a parallel computing environment known as Transputers. The Transputer embedded real-time controller was used onboard the robot to meet various intelligence requirements for the free navigation and obstacle avoidance. The control system consists of a hierarchy of robot behaviors. The mobile behavior control system was based on the use of a number of Transputers processors. Behavior methodology was based on the utilization of a structure of a five layers Neuro-fuzzy system that learns, trains, and adapts itself to the environment.

Neuro-fuzzy Controlled Autonomous Mobile Robotics System

The investigation reported in this thesis attempt to develop efficient techniques for the control of multiple mobile robots in an unknown environment. Mobile robots are key components in industrial automation, service provision, and unmanned space exploration. This thesis addresses eight different techniques for the navigation of multiple mobile robots. These are fuzzy logic, neural network, neuro-fuzzy, rule-base, rule-based-neuro-fuzzy, potential field, potential-field-neuro-fuzzy, and simulated-annealing- potential-field- neuro-fuzzy techniques. The main components of this thesis comprises of eight chapters. Following the literature survey in Chapter-2, Chapter-3 describes how to calculate the heading angle for the mobile robots in terms of left wheel velocity and right wheel velocity of the robot. In Chapter-4 a fuzzy logic technique has been analysed. The fuzzy logic technique uses different membership functions for navigation of the multiple mobile robots, which can perform obs...

Navigation Techniques for Control of Multiple Mobile Robots

Controlling mobile robot navigation system that operates in an unknown and uncertain environment is a difficult operation Much of this difficulty is due to environmental inconsistencies and sensor inadequacies We present a new neurofuzzy controller that controls the navigation system of a mobile robot to move safely in an unknown environment in presence of obstacles Training data is accumulated from robot’s sensors to generate a set of fuzzy rules that govern the robot navigation system on-line

Osama Mosaad El Refaie

Papers

An Intelligent Robot Navigation System Based on Neuro-Fuzzy Control