772 research outputs found
A fuzzy controller with supervised learning assisted reinforcement learning algorithm for obstacle avoidance
Fuzzy logic system promises an efficient way for obstacle avoidance. However, it is difficult to maintain the correctness, consistency, and completeness of a fuzzy rule base constructed and tuned by a human expert. Reinforcement learning method is capable of learning the fuzzy rules automatically. However, it incurs heavy learning phase and may result in an insufficiently learned rule base due to the curse of dimensionality. In this paper, we propose a neural fuzzy system with mixed coarse learning and fine learning phases. In the first phase, supervised learning method is used to determine the membership functions for the input and output variables simultaneously. After sufficient training, fine learning is applied which employs reinforcement learning algorithm to fine-tune the membership functions for the output variables. For sufficient learning, a new learning method using modified Sutton and Barto's model is proposed to strengthen the exploration. Through this two-step tuning approach, the mobile robot is able to perform collision-free navigation. To deal with the difficulty in acquiring large amount of training data with high consistency for the supervised learning, we develop a virtual environment (VE) simulator, which is able to provide desktop virtual environment (DVE) and immersive virtual environment (IVE) visualization. Through operating a mobile robot in the virtual environment (DVE/IVE) by a skilled human operator, the training data are readily obtained and used to train the neural fuzzy system.published_or_final_versio
Discussion on Different Controllers Used for the Navigation of Mobile Robot
Robots that can comprehend and navigate their surroundings independently on their own are considered intelligent mobile robots (MR). Using a sophisticated set of controllers, artificial intelligence (AI), deep learning (DL), machine learning (ML), sensors, and computation for navigation, MR\u27s can understand and navigate around their environments without even being connected to a cabled source of power. Mobility and intelligence are fundamental drivers of autonomous robots that are intended for their planned operations. They are becoming popular in a variety of fields, including business, industry, healthcare, education, government, agriculture, military operations, and even domestic settings, to optimize everyday activities. We describe different controllers, including proportional integral derivative (PID) controllers, model predictive controllers (MPCs), fuzzy logic controllers (FLCs), and reinforcement learning controllers used in robotics science. The main objective of this article is to demonstrate a comprehensive idea and basic working principle of controllers utilized by mobile robots (MR) for navigation. This work thoroughly investigates several available books and literature to provide a better understanding of the navigation strategies taken by MR. Future research trends and possible challenges to optimizing the MR navigation system are also discussed
A Fuzzy Logic Controller for Autonomous Wheeled Vehicles
Autonomous vehicles have potential applications in many fields, such as replacing humans in hazardous environments, conducting military missions, and performing routine tasks for industry. Driving ground vehicles is an area where human performance has proven to be reliable. Drivers typically respond quickly to sudden changes in their environment. While other control techniques may be used to control a vehicle, fuzzy logic has certain advantages in this area; one of them is its ability to incorporate human knowledge and experience, via language, into relationships among the given quantities. Fuzzy logic controllers for autonomous vehicles have been successfully applied to address various (and sometimes simultaneous) navigational issues
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 Survey of Offline and Online Learning-Based Algorithms for Multirotor UAVs
Multirotor UAVs are used for a wide spectrum of civilian and public domain
applications. Navigation controllers endowed with different attributes and
onboard sensor suites enable multirotor autonomous or semi-autonomous, safe
flight, operation, and functionality under nominal and detrimental conditions
and external disturbances, even when flying in uncertain and dynamically
changing environments. During the last decade, given the
faster-than-exponential increase of available computational power, different
learning-based algorithms have been derived, implemented, and tested to
navigate and control, among other systems, multirotor UAVs. Learning algorithms
have been, and are used to derive data-driven based models, to identify
parameters, to track objects, to develop navigation controllers, and to learn
the environment in which multirotors operate. Learning algorithms combined with
model-based control techniques have been proven beneficial when applied to
multirotors. This survey summarizes published research since 2015, dividing
algorithms, techniques, and methodologies into offline and online learning
categories, and then, further classifying them into machine learning, deep
learning, and reinforcement learning sub-categories. An integral part and focus
of this survey are on online learning algorithms as applied to multirotors with
the aim to register the type of learning techniques that are either hard or
almost hard real-time implementable, as well as to understand what information
is learned, why, and how, and how fast. The outcome of the survey offers a
clear understanding of the recent state-of-the-art and of the type and kind of
learning-based algorithms that may be implemented, tested, and executed in
real-time.Comment: 26 pages, 6 figures, 4 tables, Survey Pape
Applications of artificial intelligence in ship berthing: A review
Ship berthing operations in restricted waters such as ports requires the accurate use of onboard-vessel equipment such as rudder, thrusters, and main propulsions. For big ships, the assistance of exterior supports such as tugboats are necessary, however with the advancement of technology, we may hypothesize that the use of artificial intelligence to support ship berthing safely at ports without the dependency on the tugboats may be a reality. In this paper we comprehensively assessed and analyzed several literatures regarding this topic. Through this review, we seek out to present a better understanding of the use of artificial intelligence in ship berthing especially neural networks and collision avoidance algorithms. We discovered that the use of global and local path planning combined with Artificial Neural Network (ANN) may help to achieve collision avoidance while completing ship berthing operations
Applications of artificial intelligence in ship berthing: A review
855-863Ship berthing operations in restricted waters such as ports requires the accurate use of onboard-vessel equipment such as
rudder, thrusters, and main propulsions. For big ships, the assistance of exterior supports such as tugboats are necessary,
however with the advancement of technology, we may hypothesize that the use of artificial intelligence to support ship
berthing safely at ports without the dependency on the tugboats may be a reality. In this paper we comprehensively assessed
and analyzed several literatures regarding this topic. Through this review, we seek out to present a better understanding of
the use of artificial intelligence in ship berthing especially neural networks and collision avoidance algorithms. We
discovered that the use of global and local path planning combined with Artificial Neural Network (ANN) may help to
achieve collision avoidance while completing ship berthing operations
Mobile robot controller using novel hybrid system
Hybrid neuro-fuzzy controller is one of the techniques that is used as a tool to control a mobile robot in unstructured environment. In this paper a novel neuro-fuzzy technique is proposed in order to tackle the problem of mobile robot autonomous navigation in unstructured environment. Obstacle avoidance is an important task in the field of robotics, since the goal of autonomous robot is to reach the destination without collision. The objective is to make the robot move along a collision free trajectory until it reaches its target. The proposed approach uses the artificial neural network instead of the fuzzified engine then the output from it is processed using adaptive inference engine and defuzzification engine. In this approach, the real processing time is reduce that is increase the mobile robot response. The proposed neuro-fuzzy controller is evaluated subjectively and objectively with other approaches and also the processing time is taken in consideration
Control of autonomous multibody vehicles using artificial intelligence
The field of autonomous driving has been evolving rapidly within the last few years and
a lot of research has been dedicated towards the control of autonomous vehicles, especially
car-like ones. Due to the recent successes of artificial intelligence techniques, even
more complex problems can be solved, such as the control of autonomous multibody vehicles.
Multibody vehicles can accomplish transportation tasks in a faster and cheaper way
compared to multiple individual mobile vehicles or robots.
But even for a human, driving a truck-trailer is a challenging task. This is because of the
complex structure of the vehicle and the maneuvers that it has to perform, such as reverse
parking to a loading dock. In addition, the detailed technical solution for an autonomous
truck is challenging and even though many single-domain solutions are available, e.g. for
pathplanning, no holistic framework exists. Also, from the control point of view, designing
such a controller is a high complexity problem, which makes it a widely used benchmark.
In this thesis, a concept for a plurality of tasks is presented. In contrast to most of the existing
literature, a holistic approach is developed which combines many stand-alone systems
to one entire framework. The framework consists of a plurality of modules, such as modeling,
pathplanning, training for neural networks, controlling, jack-knife avoidance, direction
switching, simulation, visualization and testing. There are model-based and model-free
control approaches and the system comprises various pathplanning methods and target
types. It also accounts for noisy sensors and the simulation of whole environments.
To achieve superior performance, several modules had to be developed, redesigned and
interlinked with each other. A pathplanning module with multiple available methods optimizes
the desired position by also providing an efficient implementation for trajectory following.
Classical approaches, such as optimal control (LQR) and model predictive control
(MPC) can safely control a truck with a given model. Machine learning based approaches,
such as deep reinforcement learning, are designed, implemented, trained and tested successfully.
Furthermore, the switching of the driving direction is enabled by continuous
analysis of a cost function to avoid collisions and improve driving behavior.
This thesis introduces a working system of all integrated modules. The system proposed
can complete complex scenarios, including situations with buildings and partial trajectories.
In thousands of simulations, the system using the LQR controller or the reinforcement
learning agent had a success rate of >95 % in steering a truck with one trailer, even with
added noise. For the development of autonomous vehicles, the implementation of AI at
scale is important. This is why a digital twin of the truck-trailer is used to simulate the full
system at a much higher speed than one can collect data in real life.Tesi
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