653 research outputs found
Navigation Control of an Automated Guided Underwater Robot using Neural Network Technique
In recent years, under water robots play an important role in various under water operations. There is an increase in research in this area because of the application of autonomous underwater robots in several issues like exploring under water environment and resource, doing scientific and military tasks under water. We need good maneuvering capabilities and a well precision for moving in a specified track in these applications. However, control of these under water bots become very difficult due to the highly non-linear and dynamic characteristics of the underwater world. The logical answer to this problem is the application of non-linear controllers. As neural networks (NNs) are characterized by flexibility and an aptitude for dealing with non-linear problems, they are envisaged to be beneficial when used on underwater robots. In this research our artificial intelligence system is based on neural network model for navigation of an Automated Underwater robot in unpredictable and imprecise environment. Thus the back propagation algorithm has been used for the steering analysis of the underwater robot when it is encountered by a left, right and front as well as top obstacle. After training the neural network the neural network pattern was used in the controller of the underwater robot. The simulation of underwater robot under various obstacle conditions are shown using MATLAB
A survey on uninhabited underwater vehicles (UUV)
ASME Early Career Technical Conference, ASME ECTC, October 2-3, 2009, Tuscaloosa, Alabama, USAThis work presents the initiation of our underwater robotics research which will be focused on underwater
vehicle-manipulator systems. Our aim is to build an underwater vehicle with a robotic manipulator which has a robust system and also can compensate itself under the influence of the hydrodynamic effects. In this paper, overview of the existing underwater vehicle systems, thruster designs, their dynamic models and control architectures are given. The purpose and results of the existing methods in underwater robotics are investigated
A Consolidated Review of Path Planning and Optimization Techniques: Technical Perspectives and Future Directions
In this paper, a review on the three most important communication techniques (ground, aerial, and underwater vehicles) has been presented that throws light on trajectory planning, its optimization, and various issues in a summarized way. This kind of extensive research is not often seen in the literature, so an effort has been made for readers interested in path planning to fill the gap. Moreover, optimization techniques suitable for implementing ground, aerial, and underwater vehicles are also a part of this review. This paper covers the numerical, bio-inspired techniques and their hybridization with each other for each of the dimensions mentioned. The paper provides a consolidated platform, where plenty of available research on-ground autonomous vehicle and their trajectory optimization with the extension for aerial and underwater vehicles are documented
Intelligent Control Strategies for an Autonomous Underwater Vehicle
The dynamic characteristics of autonomous underwater vehicles (AUVs) present a control
problem that classical methods cannot often accommodate easily. Fundamentally, AUV dynamics
are highly non-linear, and the relative similarity between the linear and angular velocities about
each degree of freedom means that control schemes employed within other flight vehicles are not
always applicable. In such instances, intelligent control strategies offer a more sophisticated
approach to the design of the control algorithm. Neurofuzzy control is one such technique, which
fuses the beneficial properties of neural networks and fuzzy logic in a hybrid control architecture.
Such an approach is highly suited to development of an autopilot for an AUV.
Specifically, the adaptive network-based fuzzy inference system (ANFIS) is discussed in
Chapter 4 as an effective new approach for neurally tuning course-changing fuzzy autopilots.
However, the limitation of this technique is that it cannot be used for developing multivariable
fuzzy structures. Consequently, the co-active ANFIS (CANFIS) architecture is developed and
employed as a novel multi variable AUV autopilot within Chapter 5, whereby simultaneous control
of the AUV yaw and roll channels is achieved. Moreover, this structure is flexible in that it is
extended in Chapter 6 to perform on-line control of the AUV leading to a novel autopilot design
that can accommodate changing vehicle pay loads and environmental disturbances.
Whilst the typical ANFIS and CANFIS structures prove effective for AUV control system
design, the well known properties of radial basis function networks (RBFN) offer a more flexible
controller architecture. Chapter 7 presents a new approach to fuzzy modelling and employs both
ANFIS and CANFIS structures with non-linear consequent functions of composite Gaussian form.
This merger of CANFIS and a RBFN lends itself naturally to tuning with an extended form of the
hybrid learning rule, and provides a very effective approach to intelligent controller development.The Sea Systems and Platform Integration Sector,
Defence Evaluation and Research Agency, Winfrit
A Survey and Analysis of Cooperative Multi-Agent Robot Systems: Challenges and Directions
Research in the area of cooperative multi-agent robot systems has received wide attention among researchers in recent years. The main concern is to find the effective coordination among autonomous agents to perform the task in order to achieve a high quality of overall performance. Therefore, this paper reviewed various selected literatures primarily from recent conference proceedings and journals related to cooperation and coordination of multi-agent robot systems (MARS). The problems, issues, and directions of MARS research have been investigated in the literature reviews. Three main elements of MARS which are the type of agents, control architectures, and communications were discussed thoroughly in the beginning of this paper. A series of problems together with the issues were analyzed and reviewed, which included centralized and decentralized control, consensus, containment, formation, task allocation, intelligences, optimization and communications of multi-agent robots. Since the research in the field of multi-agent robot research is expanding, some issues and future challenges in MARS are recalled, discussed and clarified with future directions. Finally, the paper is concluded with some recommendations with respect to multi-agent systems
Task-space dynamic control of underwater robots
This thesis is concerned with the control aspects for underwater tasks performed by
marine robots. The mathematical models of an underwater vehicle and an underwater
vehicle with an onboard manipulator are discussed together with their associated
properties.
The task-space regulation problem for an underwater vehicle is addressed where the
desired target is commonly specified as a point. A new control technique is proposed
where the multiple targets are defined as sub-regions. A fuzzy technique is used to
handle these multiple sub-region criteria effectively. Due to the unknown gravitational
and buoyancy forces, an adaptive term is adopted in the proposed controller.
An extension to a region boundary-based control law is then proposed for an underwater
vehicle to illustrate the flexibility of the region reaching concept. In this novel
controller, a desired target is defined as a boundary instead of a point or region. For a
mapping of the uncertain restoring forces, a least-squares estimation algorithm and the
inverse Jacobian matrix are utilised in the adaptive control law.
To realise a new tracking control concept for a kinematically redundant robot, subregion
tracking control schemes with a sub-tasks objective are developed for a UVMS.
In this concept, the desired objective is specified as a moving sub-region instead of a
trajectory. In addition, due to the system being kinematically redundant, the controller
also enables the use of self-motion of the system to perform sub-tasks (drag
minimisation, obstacle avoidance, manipulability and avoidance of mechanical joint
limits)
INTELLIGENT FAULT TOLERANT CONTROL SCHEMES FOR AUTONOMOUS UNDERWATER VEHICLES
The area of autonomous underwater vehicles (AUVs) is an increasingly important area of
research, with AUVs being capable of handling a far wider range of missions than either
an inhabited underwater vehicle or a remotely operated vehicle (ROV). One of the major
drawbacks of such vehicles is the inability of their control systems to handle faults
occurring within the vehicle during a mission. This study aims to develop enhancements
to an existing control system in order to increase its fault tolerance to both sensor and
actuator faults.
Faults occurring within the sensors for both the yaw and roll channels of the AUV are
considered. Novel fuzzy inference systems (FISs) are developed and tuned using both the
adaptive neuro-fuzzy inference system (ANFIS) and simulated annealing tuning methods.
These FISs allow the AUV to continue operating after a fault has occurred within the
sensors.
Faults occurring within the actuators which control the canards of the AUV and hence the
yaw channel are also examined. Actuator recovery FISs capable of handling faults
occurring within the actuators are developed using both the simulated annealing and tabu
search methods of tuning FISs. The fault tolerance of the AUV is then further enhanced
by the development of an error estimation FIS that is used to replace an error sensor.
It concludes that the novel FISs designed and developed within the thesis provide an
improved performance to both sensor and actuator faults in comparison to benchmark
control systems. Therefore having these FISs embedded within the overall control
scheme ensure the AUV is fault tolerant to a range of selected failures
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