2,682 research outputs found
Realization of reactive control for multi purpose mobile agents
Mobile robots are built for different purposes, have different physical size, shape, mechanics and electronics. They are required to work in real-time, realize more than one goal simultaneously, hence to communicate and cooperate with other agents. The approach proposed in this paper for mobile robot control is reactive and has layered structure that supports multi sensor perception. Potential field method is implemented for both obstacle avoidance and goal tracking. However imaginary forces of the obstacles and of the goal point are separately treated, and then resulting behaviors are fused with the help of the geometry. Proposed control is tested on simulations where
different scenarios are studied. Results have confirmed the high performance of the method
Wavefront Propagation and Fuzzy Based Autonomous Navigation
Path planning and obstacle avoidance are the two major issues in any
navigation system. Wavefront propagation algorithm, as a good path planner, can
be used to determine an optimal path. Obstacle avoidance can be achieved using
possibility theory. Combining these two functions enable a robot to
autonomously navigate to its destination. This paper presents the approach and
results in implementing an autonomous navigation system for an indoor mobile
robot. The system developed is based on a laser sensor used to retrieve data to
update a two dimensional world model of therobot environment. Waypoints in the
path are incorporated into the obstacle avoidance. Features such as ageing of
objects and smooth motion planning are implemented to enhance efficiency and
also to cater for dynamic environments
A reconfigurable hybrid intelligent system for robot navigation
Soft computing has come of age to o er us a wide array of powerful and e cient algorithms
that independently matured and in
uenced our approach to solving problems in robotics,
search and optimisation. The steady progress of technology, however, induced a
ux of new
real-world applications that demand for more robust and adaptive computational paradigms,
tailored speci cally for the problem domain. This gave rise to hybrid intelligent systems, and
to name a few of the successful ones, we have the integration of fuzzy logic, genetic algorithms
and neural networks. As noted in the literature, they are signi cantly more powerful than
individual algorithms, and therefore have been the subject of research activities in the past
decades. There are problems, however, that have not succumbed to traditional hybridisation
approaches, pushing the limits of current intelligent systems design, questioning their solutions
of a guarantee of optimality, real-time execution and self-calibration. This work presents an
improved hybrid solution to the problem of integrated dynamic target pursuit and obstacle
avoidance, comprising of a cascade of fuzzy logic systems, genetic algorithm, the A* search
algorithm and the Voronoi diagram generation algorithm
Neuro-Fuzzy Combination for Reactive Mobile Robot Navigation: A Survey
Autonomous navigation of mobile robots is a fruitful research area because of the diversity of methods adopted by artificial intelligence. Recently, several works have generally surveyed the methods adopted to solve the path-planning problem of mobile robots. But in this paper, we focus on methods that combine neuro-fuzzy techniques to solve the reactive navigation problem of mobile robots in a previously unknown environment. Based on information sensed locally by an onboard system, these methods aim to design controllers capable of leading a robot to a target and avoiding obstacles encountered in a workspace. Thus, this study explores the neuro-fuzzy methods that have shown their effectiveness in reactive mobile robot navigation to analyze their architectures and discuss the algorithms and metaheuristics adopted in the learning phase
Obstacle Avoidance Based on Stereo Vision Navigation System for Omni-directional Robot
This paper addresses the problem of obstacle avoidance in mobile robot navigation systems. The navigation system is considered very important because the robot must be able to be controlled from its initial position to its destination without experiencing a collision. The robot must be able to avoid obstacles and arrive at its destination. Several previous studies have focused more on predetermined stationary obstacles. This has resulted in research results being difficult to apply in real environmental conditions, whereas in real conditions, obstacles can be stationary or moving caused by changes in the walking environment. The objective of this study is to address the robot’s navigation behaviors to avoid obstacles. In dealing with complex problems as previously described, a control system is designed using Neuro-Fuzzy so that the robot can avoid obstacles when the robot moves toward the destination. This paper uses ANFIS for obstacle avoidance control. The learning model used is offline learning. Mapping the input and output data is used in the initial step. Then the data is trained to produce a very small error. To support the movement of the robot so that it is more flexible and smoother in avoiding obstacles and can identify objects in real-time, a three wheels omnidirectional robot is used equipped with a stereo vision sensor. The contribution is to advance state of the art in obstacle avoidance for robot navigation systems by exploiting ANFIS with target-and-obstacles detection based on stereo vision sensors. This study tested the proposed control method by using 15 experiments with different obstacle setup positions. These scenarios were chosen to test the ability to avoid moving obstacles that may come from the front, the right, or the left of the robot. The robot moved to the left or right of the obstacles depending on the given Vy speed. After several tests with different obstacle positions, the robot managed to avoid the obstacle when the obstacle distance ranged from 173 – 150 cm with an average speed of Vy 274 mm/s. In the process of avoiding obstacles, the robot still calculates the direction in which the robot is facing the target until the target angle is 0
Intelligent Robotics Navigation System: Problems, Methods, and Algorithm
This paper set out to supplement new studies with a brief and comprehensible review of the advanced development in the area of the navigation system, starting from a single robot, multi-robot, and swarm robots from a particular perspective by taking insights from these biological systems. The inspiration is taken from nature by observing the human and the social animal that is believed to be very beneficial for this purpose. The intelligent navigation system is developed based on an individual characteristic or a social animal biological structure. The discussion of this paper will focus on how simple agent’s structure utilizes flexible and potential outcomes in order to navigate in a productive and unorganized surrounding. The combination of the navigation system and biologically inspired approach has attracted considerable attention, which makes it an important research area in the intelligent robotic system. Overall, this paper explores the implementation, which is resulted from the simulation performed by the embodiment of robots operating in real environments
Design of a Fuzzy Logic Controller for Skid Steer Mobile Robot
The control problem of four-wheeled skid steering mobile robots is quite challenging
mainly because the skid steering system is an underactuated system and its
mathematical model is highly uncertain. Skid steering configurations employ a
differential-drive technique in which the wheels rotation is limited to around one axis
and the lack of a steering wheel causes the navigation to be determined by the change
of speed in either side of the robot for turning. Equal speed in both sides causes a
straight-line motion. However, the implementation of the dead reckoning technique
on skid-steer mobile robots will limit the precision of current robot’s position
because skid-steer configuration intentionally relies on wheel slippage for normal
operation and this possesses some difficulties when implementing motion control
using the odometric system.
The thesis describes the design of a fuzzy logic controller to compensate the dead
reckoning limitation and implementation on a skid-steer mobile robot. The fuzzy
controller has two inputs (angle error and distance), two outputs (translational and
rotational speed) and 14 rules. These inputs are computed from the dead-reckoning method that is totally reliant on the odometry readings and data are fuzzified to be
the inputs of the fuzzy controller. The outputs are the analogue voltages to the left
and right motors, which drive the mobile robot. For simplicity, membership
functions consisting of triangular and trapezoid shapes have been adopted. The
membership functions of the fuzzy sets are chosen by trial-and-error based on
experimentation. The heuristic rules control the orientation of the robot according to
the information about the distances from the desired positions. The crisp output
values from the fuzzy logic controller are decoded and fed into a decision module
where the ratios of both sides motor voltage are determined for every smooth change
in speed of the motors.
To facilitate the implementation of control system, real-time execution is done in an
indoor environment. Data acquisition is done in a LABVIEW and a MATLAB
control algorithm is called in LABVIEW. A real mobile robot, PUTRABOT2 was
used to conduct the experiment. Performance evaluation is observed from the
accumulated error in orientation and its trajectory obtained after mapping the
information gathered from the real world via odometry sensors. Few features such as
the rise time, settling time and peak time of the output responses are analyzed.
Comparisons are made between fuzzy logic and PD controllers. Comparative results
among these two controllers indicate the superiority of the fuzzy approach with the
ability to minimize the position and orientation errors. Moreover, the trajectory
accuracy is very high and more reliable in the presence of unreliable odometry
readings
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