Heuristic approaches in robot path planning

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Mobile robot is an autonomous agent capable of navigating intelligently anywhere using sensor actuator control techniques The applications of the autonomous mobile robot in many fields such as industry space defence and transportation and other social sectors are growing day by day The mobile robot performs many tasks such as rescue operation patrolling disaster relief planetary exploration and material handling etc Therefore an intelligent mobile robot is required that could travel autonomously in various static and dynamic environments Several techniques have been applied by the various researchers for mobile robot navigation and obstacle avoidance The present article focuses on the study of the intelligent navigation techniques which are capable of navigating a mobile robot autonomously in static as well as dynamic environments

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Mobile Robot Navigation and Obstacle Avoidance Techniques: A Review

Path planning in uncertain environment by using firefly algorithm

Autonomous mobile robots are used in several application areas including manufacturing, mining, military, and transportation, search and protect missions, etc. For the navigation system it is necessary to locate the position of the mobile robot in surrounding environment. For avoiding obstacles efficiently and to reach the target under many different shapes of obstacle in environment, a fuzzy logic controller has been designed to improve the movement of mobile robot according to obstacles positions by defining or establishing input variables, output variables, fuzzy logic membership functions, fuzzy logic rule base ‘If-Then’ fuzzy inference system rules and defuzzification method. Then it has to plan a path towards desired goal. The navigation system of a mobile robot has to identify all potential obstacles in order to search for a collision free path. Obstacles avoidance and destination point can be achieved by changing the direction angle of the mobile robot. To make the mobile robot move in its environment, the basic path planning strategies have been used. While the mobile robot is navigating in its workspace environment, it avoids obstacles and look for the target. In this paper the simulation of path planning technique for an autonomous mobile robot is presented. The figure shows simulation of the mobile robot with four obstacles.

Path planning navigation of mobile robot with obstacles avoidance using fuzzy logic controller

Neuro-fuzzy 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, neuro-fuzzy based systems are developed for behavior based control of a mobile robot for reactive navigation. The proposed systems transform sensors' input to yield wheel velocities. Novel algorithms are proposed for a) to find the range of the mobile robots from nearby obstacles and b) to generate training pairs for neural network, optimally. With a view to ascertain the efficacy of proposed system; developed neuro-fuzzy system's performance is compared to neural and fuzzy based approaches. Simulation results show effectiveness of proposed system in all kind of obstacle environments.

Neuro-fuzzy based autonomous mobile robot navigation system

The autonomous vehicle parking problem has drawn increased attention in recent times. It can resolve the parking-related issues that involve minimizing human driving errors and saving fuel and time. In practice, parallel parking needs extra care when dynamicity is present in the environment. Many human drivers have the expertise to take quick action when obstacles appear on their driving path. An autonomous system should possess such intelligence that can mimic human-like behavior in the presence of obstacles. This work focuses on providing an intelligent autonomous parking design using a car-like mobile robot (CLMR) that efficiently parks the vehicle in dynamic environmental conditions. A novel fuzzy-based obstacle avoidance controller is proposed that integrates sensor information into the parking problem, obtained from the surrounding of a CLMR. Ultrasonic sensors’ arrangement and their grouping provide inputs for the fuzzy system. A fuzzy-based obstacle avoidance controller can execute intelligent parking like a human by avoiding static as well as moving obstacles. The proposed work is tested in different challenging environmental conditions. Simulation results demonstrate that the proposed algorithm accomplishes autonomous parallel parking reasonably well in the presence of static and moving obstacles and can be used to park the vehicle in real-time. The proposed work helps to solve the autonomous parking problem with safety, especially in dynamic environmental conditions.

A human-like decision intelligence for obstacle avoidance in autonomous vehicle parking

An Intelligent Fuzzy based Hybrid Approach for Parallel Parking in Dynamic Environment

Neuro-fuzzy systems have been used for robot navigation applications because of their ability to exert human like expertise and to utilize acquired knowledge to develop autonomous navigation strategies. In this paper, neuro-fuzzy based system is proposed for reactive navigation of a mobile robot using behavior based control. The proposed algorithm uses discrete sampling based optimal training of neural network. With a view to ascertain the efficacy of proposed system; the proposed neuro-fuzzy system’s performance is compared to that of neural and fuzzy based approaches. Simulation results along with detailed behavior analysis show effectiveness of our algorithm in all kind of obstacle environments.

Reactive Navigation of Autonomous Mobile Robot Using Neuro- Fuzzy System

Fuzzy systems have been used in past years, for robot navigation applications because of their ability to capture human mind expertise and to utilize knowledge to develop autonomous navigation strategies. In this paper, the Fuzzy based systems are developed for behavior based control of a mobile robot for reactive navigation. The Fuzzy system transforms sensors’ input to yield wheel velocities. The rule bases for Fuzzy systems are generated by “handcrafting” that generates training sequences optimally. Fuzzy membership functions are fine tuned manually based on simulation experiments. With a view to ascertain the efficacy of the Fuzzy based systems; our developed Fuzzy system’s basic architecture is compared with architecture based on heuristic approach. Simulation results show effectiveness of proposed system in all kind of obstacle environments. Keywords-Mobile robot; Reactive navigation; Fuzzy system; Behavior-based control; Fuzzy membership functions

Maulin Joshi

Papers

Neuro-fuzzy based autonomous mobile robot navigation system

A human-like decision intelligence for obstacle avoidance in autonomous vehicle parking

An Intelligent Fuzzy based Hybrid Approach for Parallel Parking in Dynamic Environment

Reactive Navigation of Autonomous Mobile Robot Using Neuro- Fuzzy System

Fuzzy Based Autonomous Robot Navigation System