2,676 research outputs found
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
Multi-mobile robot and avoidance obstacle to spatial mapping in indoor environment
The advancement of technology and techniques applied to robotics contributes to increasing the quality of life
and safety of humanity. One of the most widespread applications of mobile robotics is related to monitoring
indoor environments. However, due to factors such as the size of the environment impacting the monitoring
response, battery autonomy, and autonomous navigation in environments with unknown obstacles, they are
still significant challenges in the diffusion of mobile robotics in these areas. Strategy adopting multiple robots
can overcome these challenges. This work presents an approach to use multi-robots in hazardous environments
with gas leakage to perform spatial mapping of the gas concentration. Obstacles arranged in the environment
are unknown to robots, then a fuzzy control approach is used to avoid the collision. As a result of this paper,
spatial mapping of an indoor environment was carried out with multi-robots that reactively react to unknown
obstacles considering a point gas leak with Gaussian dispersion.This work has been supported by FCT - Fundação para a Ciência e Tecnologia within the Project Scope:
UIDB/05757/2020. Additionally, this work was supported in part by the National Counsel of Technological
and Scientific Development of Brazil (CNPq), in part by the Coordination for the Improvement of
Higher Level People (CAPES).info:eu-repo/semantics/publishedVersio
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
Integrating an electronic compass for position tracking on a wheeled tricycle mobile robot
Dead-reckoning via encoders on wheeled-mobile robots is a simple but inaccurate method to estimate position. The major drawback of encoders is wheel slippage errors that accumulate over time. This problem is often addressed by using additional sensors such as compass, gyroscope, or GPS. This paper details the integration and effectiveness of a relatively low-cost solution using an electronic compass to reduce positioning error on a wheeled tricycle mobile robot. A customised Visual Studio program has been developed to adjust the settings of the electronic compass and integrate it with the Visual Studio based robot control system. The electronic compass heading data is fused with the encoder odometry heading data in three different ways: simple fusion, linear weighted fusion, and Kalman filter fusion. Simple fusion and linear weighted fusion rely on parameters determined from angular acceleration and angular velocity, respectively. The Kalman filter uses variance data for the encoders and electronic compass to determine an optimal heading. Experiments have been conducted in an indoor corridor environment to evaluate and compare the various fusion methods. Position error is successfully reduced and is sufficient to locate the robot within the corridor
- …