A pan-tilt camera Fuzzy vision controller on an unmanned aerial vehicle

is paper presents an implementation of two Fuzzy Logic controllers working in parallel for a pan-tilt camera platform on an UAV. This implementation uses a basic Lucas-Kanade tracker algorithm, which sends information about the error between the center of the object to track and the center of the image, to the Fuzzy controller. This information is enough for the controller, to follow the object moving a two axis servo-platform, besides the UAV vibrations and movements. The two Fuzzy controllers of each axis, work with a rules-base of 49 rules, two inputs and one output with a more significant sector defined to improve the behavior of those

Non-symmetric membership function for Fuzzy-based visual servoing onboard a UAV

This paper presents the definition of non-symmetric membership function for Fuzzy controllers applied to a pan & tilt vision platform onboard an Unmanned Aerial Vehicle. This improvement allows the controllers to have a more adaptive behavior to the non-linearities presented in an UAV. This implementation allows the UAV to follow objects in the environment by using Lucas-Kanade visual tracker, in spite of the aircraft vibrations, the movements of the objects and the aircraft. update has been tested in real flights with an unmanned helicopter of the Computer Vision Group at the UPM, with very successful results, attaining a considerable reduction of the error during the tracking tests

Aerial Object Following Using Visual Fuzzy Servoing

This article presents a visual servoing system to follow a 3D moving object by a Micro Unmanned Aerial Vehicle (MUAV). The presented control strategy is based only on the visual information given by an adaptive tracking method based on the colour information. A visual fuzzy system has been developed for servoing the camera situated on a rotary wing MAUV, that also considers its own dynamics. This system is focused on continuously following of an aerial moving target object, maintaining it with a fixed safe distance and centred on the image plane. The algorithm is validated on real flights on outdoors scenarios, showing the robustness of the proposed systems against winds perturbations, illumination and weather changes among others. The obtained results indicate that the proposed algorithms is suitable for complex controls task, such object following and pursuit, flying in formation, as well as their use for indoor navigatio

Visual servoing using fuzzy controllers on an unmanned aerial vehicles

This paper presents an implementa- tion of three Fuzzy Logic controllers working in parallel onboard a UAV, two for a pan-tilt camera platform and the third for control the yaw of the helicopter. This implementation uses a Lucas-Kanade tracker algo- rithm with a pyramidal optical ow implementation, which gives infor- mation to follow statics and moving objects, besides the UAV vibrations and movements. The platform con- troller is helped by the heading con- troller, in order to make smooth the big movements to the platform, re- ducing the risk of lost the warp selec- tion of the object to track. Also, the heading control remove the physic limit of the platform at the yaw axis. Some laboratory and UAV tests are presented in order to show the di er- ent behaviors and the good response of the presented controllers

A Visual AGV-Urban Car using Fuzzy Control

The goal of the work described in this paper is to develop a visual line guided system for being used on-board an Autonomous Guided Vehicle (AGV) commercial car, controlling the steering and using just the visual information of a line painted below the car. In order to implement the control of the vehicle, a Fuzzy Logic controller has been implemented, that has to be robust against curvature changes and velocity changes. The only input information for the controller is the visual distance from the image center captured by a camera pointing downwards to the guiding line on the road, at a commercial frequency of 30Hz. The good performance of the controller has successfully been demonstrated in a real environment at urban velocities. The presented results demonstrate the capability of the Fuzzy controller to follow a circuit in urban environments without previous information about the path or any other information from additional sensor

Mobile robot visual navigation based on fuzzy logic and optical flow approaches

This paper presents the design of mobile robot visual navigation system in indoor environment based on fuzzy logic controllers (FLC) and optical flow (OF) approach. The proposed control system contains two Takagi–Sugeno fuzzy logic controllers for obstacle avoidance and goal seeking based on video acquisition and image processing algorithm. The first steering controller uses OF values calculated by Horn–Schunck algorithm to detect and estimate the positions of the obstacles. To extract information about the environment, the image is divided into two parts. The second FLC is used to guide the robot to the direction of the final destination. The efficiency of the proposed approach is verified in simulation using Visual Reality Toolbox. Simulation results demonstrate that the visual based control system allows autonomous navigation without any collision with obstacles.Peer ReviewedPostprint (author's final draft

Fuzzy Controller for UAV-Landing Task Using 3D-Position Visual Estimation

This paper presents a Fuzzy Control application for a landing task of an Unmanned Aerial Vehicle, using the 3D-position estimation based on visual tracking of piecewise planar objects. This application allows the UAV to land on scenarios in which it is only possible to use visual information to obtain the position of the vehicle. The use of the homography permits a realtime estimation of the UAV's pose with respect to a helipad using a monocular camera. Fuzzy Logic allows the definition of a model-free control system of the UAV. The Fuzzy controller analyzes the visual information to generate altitude commands for the UAV to develop the landing task

Comprehensive review on controller for leader-follower robotic system

985-1007This paper presents a comprehensive review of the leader-follower robotics system. The aim of this paper is to find and elaborate on the current trends in the swarm robotic system, leader-follower, and multi-agent system. Another part of this review will focus on finding the trend of controller utilized by previous researchers in the leader-follower system. The controller that is commonly applied by the researchers is mostly adaptive and non-linear controllers. The paper also explores the subject of study or system used during the research which normally employs multi-robot, multi-agent, space flying, reconfigurable system, multi-legs system or unmanned system. Another aspect of this paper concentrates on the topology employed by the researchers when they conducted simulation or experimental studies

Data Fusion for Vision-Based Robotic Platform Navigation

Data fusion has become an active research topic in recent years. Growing computational performance has allowed the use of redundant sensors to measure a single phenomenon. While Bayesian fusion approaches are common in general applications, the computer vision community has largely relegated this approach. Most object following algorithms have gone towards pure machine learning fusion techniques that tend to lack flexibility. Consequently, a more general data fusion scheme is needed. The motivation for this work is to propose methods that allow for the development of simple and cost effective, yet robust visual following robots capable of tracking a general object with limited restrictions on target characteristics. With that purpose in mind, in this work, a hierarchical adaptive Bayesian fusion approach is proposed, which outperforms individual trackers by using redundant measurements. The adaptive framework is achieved by relying in each measurement\u27s local statistics and a global softened majority voting. Several approaches for robots that can follow targets have been proposed in recent years. However, many require the use of several, expensive sensors and often the majority of the image processing and other calculations are performed independently. In the proposed approach, objects are detected by several state-of-the-art vision-based tracking algorithms, which are then used within a Bayesian framework to filter and fuse the measurements and generate the robot control commands. Target scale variations and, in one of the platforms, a time-of-flight (ToF) depth camera, are used to determine the relative distance between the target and the robotic platforms. The algorithms are executed in real-time (approximately 30fps). The proposed approaches were validated in a simulated application and several robotics platforms: one stationary pan-tilt system, one small unmanned air vehicle, and one ground robot with a Jetson TK1 embedded computer. Experiments were conducted with different target objects in order to validate the system in scenarios including occlusions and various illumination conditions as well as to show how the data fusion improves the overall robustness of the system