Architecture For Mobile Robot Navigation Research Articles

Autonomous mobile robot navigation between static and dynamic obstacles using multiple ANFIS architecture

PurposeThis paper aims to design and implement the multiple adaptive neuro-fuzzy inference system (MANFIS) architecture-based sensor-actuator (motor) control technique for mobile robot navigation in different two-dimensional environments with the presence of static and moving obstacles.Design/methodology/approachThe three infrared range sensors have been mounted on the front, left and right side of the robot, which reads the forward, left forward and right forward static and dynamic obstacles in the environment. This sensor data information is fed as inputs into the MANFIS architecture to generate appropriate speed control commands for right and left motors of the robot. In this study, we have taken one assumption for moving obstacle avoidance in different scenarios the speed of the mobile robot is at least greater than or equal to the speed of moving obstacles and goal.FindingsGraphical simulations have designed through MATLAB and virtual robot experimentation platform (V-REP) software and experiments have been done on Arduino MEGA 2560 microcontroller-based mobile robot. Simulation and experimental studies demonstrate the effectiveness and efficiency of the proposed MANFIS architecture.Originality/valueThis paper designs and implements MANFIS architecture for mobile robot navigation between a static and moving obstacle in different simulation and experimental environments. Also, the authors have compared this developed architecture to the other navigational technique and found that our developed architecture provided better results in terms of path length in the same environment.

A biologically inspired intelligent environment architecture for mobile robot navigation

Localisation in conventional autonomous robot navigation using centralised on-board intelligence faces many challenging issues. Steering away from the centralised on-board solution, this paper proposes a distributed approach by deploying distributed wireless nodes that comprise of vision sensors and embedded computers to process images, localise the robot and dynamic obstacles, navigate and control the robot. This schema releases massive on-board information processing required by conventional autonomous robot to its operation environment. The objective is to enable robots with limited intelligence to carry out complex navigation functions. The architectural design and realisation for connecting wireless vision sensors in the environment for global robot navigation path planning and local motion control is described. The paper gives a review of the proposed algorithms. The details of the wireless sensor network connectivity, system functions and modules, organisation and connection of the system components are described. The hardware and software realisation and experiment results are also discussed.

CASE STUDY ON A SELF-ORGANIZING SPIKING NEURAL NETWORK FOR ROBOT NAVIGATION

This paper presents a Spiking Neural Network (SNN) architecture for mobile robot navigation. The SNN contains 4 layers where dynamic synapses route information to the appropriate neurons in each layer and the neurons are modeled using the Leaky Integrate and Fire (LIF) model. The SNN learns by self-organizing its connectivity as new environmental conditions are experienced and consequently knowledge about its environment is stored in the connectivity. Also a novel feature of the proposed SNN architecture is that it uses working memory, where present and previous sensor states are stored. Results are presented for a wall following application.

Interactive Teaching for Vision-Based Mobile Robots: A Sensory-Motor Approach

For the last decade, we have been developing a vision-based architecture for mobile robot navigation. Using our bio-inspired model of navigation, robots can perform sensory-motor tasks in real time in unknown indoor as well as outdoor environments. We address here the problem of autonomous incremental learning of a sensory-motor task, demonstrated by an operator guiding a robot. The proposed system allows for semisupervision of task learning and is able to adapt the environmental partitioning to the complexity of the desired behavior. A real dialogue based on actions emerges from the interactive teaching. The interaction leads the robot to autonomously build a precise sensory-motor dynamics that approximates the behavior of the teacher. The usability of the system is highlighted by experiments on real robots, in both indoor and outdoor environments. Accuracy measures are also proposed in order to evaluate the learned behavior as compared to the expected behavioral attractor. These measures, used first in a real experiment and then in a simulated experiment, demonstrate how a real interaction between the teacher and the robot influences the learning process.

A multi-agent system architecture for mobile robot navigation based on fuzzy and visual behaviour

A multi-agent system based on behaviour for controlling the navigation task of a mobile robot in office-like environments is presented. The set of agents is structured into a three-layer hybrid architecture. A high level of abstraction plan is created using a topological map of the environment in the Deliberative layer. It is composed by the sequence of rooms and corridors to traverse and doors to cross in order to reach a desired room. The Execution and Monitoring layer translates the plan into a sequence of available skills in order to achieve the desired goal and monitors the execution of the plan. In the Control layer there is a set of agents that implements fuzzy and visual behaviours that run concurrently to guide the robot. Fuzzy behavior manages the vagueness and uncertainty of the range sensor information allowing to navigate safely in the environment. Visual behavior locates a required door to cross and fixate it, indicating the appropriate direction to reach it. Artificial landmarks are placed beside the doors to show its position. The system has been implemented in a Nomad 200 mobile robot and has been validated in numerous experiments in a real office-like environment.

A fuzzy logic based extension to Payton and Rosenblatt's command fusion method for mobile robot navigation

Payton and Rosenblatt (1990) have proposed a command fusion method for combining outputs of multiple behaviors in a mobile robot navigation system such that information loss due to command fusion can be reduced. Using linguistic fuzzy rules to explicitly capture heuristics implicit in the Payton-Rosenblatt approach, we have extended their approach to a fuzzy logic architecture for mobile robot navigation in dynamic environments, which is simpler and easier to understand and modify. We have also developed and empirically tested a new defuzzification technique for alleviating difficulties in applying existing defuzzification methods to mobile robot navigation control.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Integrating reaction and planning in a heterogeneous asynchronous architecture for mobile robot navigation

We present an architecture for controlling autonomous mobile robots based on control of continuous activities (processes) rather than discrete actions. We define a hierarchy of activity, and argue that different levels of activities require different sorts of computational mechanisms to control them. Many controversial issues concerning the use of persistent internal state and higher levels of abstraction can be better understood in terms of this hierarchy. Two experiments using the architecture to control mobile robots performing complex navigation tasks are described.