Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
An introduction to fuzzy control
An introduction to fuzzy control
Computational principles of mobile robotics
Computational principles of mobile robotics
Advances in Intelligent Autonomous Systems
Advances in Intelligent Autonomous Systems
Intelligent Control Systems Using Soft Computing Methodologies
Intelligent Control Systems Using Soft Computing Methodologies
Real-time map building and navigation for autonomous robots inunknown environments
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Brief Backstepping for nonsmooth systems
Automatica (Journal of IFAC)
Development of a new minimum avoidance system for a behavior-based mobile robot
Fuzzy Sets and Systems
A three dimensional path planning algorithm
ICS'10 Proceedings of the 14th WSEAS international conference on Systems: part of the 14th WSEAS CSCC multiconference - Volume I
Methods and algorithms for motion control of walking mobile robot with obstacle avoidance
ECC'11 Proceedings of the 5th European conference on European computing conference
Walking robot method control using artificial vision
Proceedings of the 15th WSEAS international conference on Systems
Evolutionary Design of Fuzzy Logic Based Position Controller for Mobile Robot
Journal of Intelligent and Robotic Systems
An expert fuzzy cognitive map for reactive navigation of mobile robots
Fuzzy Sets and Systems
Autonomous Mobile Robot Navigation using Artificial Immune System
Proceedings of Conference on Advances In Robotics
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This paper describes how soft computing methodologies such as fuzzy logic, genetic algorithms and the Dempster-Shafer theory of evidence can be applied in a mobile robot navigation system. The navigation system that is considered has three navigation subsystems. The lower-level subsystem deals with the control of linear and angular volocities using a multivariable PI controller described with a full matrix. The position control of the mobile robot is at a medium level and is nonlinear. The nonlinear control design is implemented by a backstepping algorithm whose parameters are adjusted by a genetic algorithm. We propose a new extension of the controller mentioned, in order to rapidly decrease the control torques needed to achieve the desired position and orientation of the mobile robot. The high-level subsystem uses fuzzy logic and the Dempster-Shafer evidence theory to design a fusion of sensor data, map building, and path planning tasks. The fuzzy/evidence navigation based on the building of a local map, represented as an occupancy grid, with the time update is proven to be suitable for real-time applications. The path planning algorithm is based on a modified potential field method. In this algorithm, the fuzzy rules for selecting the relevant obstacles for robot motion are introduced. Also, suitable steps are taken to pull the robot out of the local minima. Particular attention is paid to detection of the robot's trapped state and its avoidance. One of the main issues in this paper is to reduce the complexity of planning algorithms and minimize the cost of the search. The performance of the proposed system is investigated using a dynamic model of a mobile robot. Simulation results show a good quality of position tracking capabilities and obstacle avoidance behavior of the mobile robot.